CA2019099A1 - Neural controllers - Google Patents

Abstract

Abstract of the Disclosure A neural controller, to be called a neuroplanner, is described herein for finding a sequence of system configurations from an initial system configuration to any one of a set of goal system configurations specified by a targeting system the presence of prohibited system configurations.
Means are provided for the goal system configurations to be specified by selecting a targeting configuration that is in turn correlated with one or more system configurations. In an embodiment as a robot arm controller, the system configurations are arm configurations, the targeting system is a pointing device that specifies a point in three dimensional space that is to be touched by the end-effector, and the prohibited system configurations are illegal arm configurations; e.g., those that place the arm beyond the physical limits of a joint of the robot arm or in a region that must be temporarily avoided because it is currently occupied by an obstacle. The sequence of configurations provided by the controller constitutes a trajectory that will move the arm from its current configuration to any configuration in which the end-effector is at the specified target point while simultaneously avoiding obstacles. The neuroplanner includes means for creating an environment network, a system configuration network, and a targeting configuration network that respectively encode quantized representations of the environmental information, the system configurations, and the targeting configurations. Furthermore, means are provided for correlating the environmental network with the system network and also correlating the targeting network with the system network so as to be able to specify prohibited system configurations and goal system configurations respectively. Finally, means are provided for finding a path from one system configuration to a goal system configuration that excludes prohibited system configurations.

Description

2 0 1 ~
- ! , ' ," ', :, '- ' ' ~:: , : -: : , ' This invention relates generally to controllers ~and~ more paIticularly to controllers for robot manipulators. ~

Many robotic systems frequently include a manipulator arm that consists of a series of rigid links connected by joints, and a~plaeform~ upon whlch the manipulator is mounted herein called the body of the robot. The first joint of the mar~ipulator is~connected to the body of the robot, and the 1 ast joint~ is~connected to~a grasping dèvice, ~normally called ~the end-effector. T e tip of the end-effector is normally referred to as~the point-of-touch of the manipulator. The number of joints in a manipulator is referred~ to as its degrees-of-freedom (DOF). Most industrial robots have 6 degrees-of-freedom.~ There are two logical types of joints, namely revolute joints, and prismatic joints Revolute joints produce a turning tlon that rotate a link around an axis of rotadon through the c entre of the joint The amount that one link is turned~relative to the other is called the ioint angle.
Prismatic ~oints produce a sliding motion~ that~ translate one link along the length ~of another. The amount~that such link is tr~anslated relative to The other ls called the joint disp}acement. When used herein, the~term joint, refers to either a revolute joint or a prismatic joint and the term joint settings refers to either joint angles or joint displacements. The mechanism that actually moves a joint is called an actuator. The most commonly used actuators are stepper motors, DC motors, hydraulic plStonS and pnoumatic pistons ~
Each combination of joint settings produces a unique physical configuration of the manipulator. A sequence of physical configurations that smoothly dIive a robot arm fromlsome initial physical configuration to a final physical configuration is terrned a trajectory. If the end-effector is a rigid gripper that does not change shape, it is often treated as another link on the manipulator. In order to place the end-effector at an arbitrary position and at an arbitrary orientation in three-dimensional space, a manipulator must have at least 6 degrees-of-freedom; e.g., three to determine the (x,y,z) position and three to determine the (roll,pitch,yaw) orientation. The region in . : - , ' "' , i, ~ "
~ ~ 2 ~ 3 ~ 0 ~
- . ".", :, :,,.,,:
.. ... ... ...
~. " .. ",, the immediate vicimty of a robot s called the workspace of the robot. In the case of a mobile robot, the workspace can be thought of as a region centred on the body of the robot, that moves with the robot.
Robotic systems generally make use of two types of sensors: environment sensors that provide inforrnation about the ~environment; e.g.; camera images, tactile feedback, voice recognition, etc., and feedback sensors that tell the robot about its physical state; e.g., joint setting sensors, strain gauges indicating resistance to movement, etc. Most industrial robots do not have sophisticated environment sensors. Instead, they are programmed to move manipulators to predetermined points in space where objects are carefully pre-positioned. Feedback sensors are used to determine the exact physical configuration of the arm during this process. Ingenious schemes are necessary~ to ensure that obJects to be grasped by the robot always have the same position~ and orientation. Integrating~ and using sensory data obtained from several sensory modalities is an active area of research.
",, Various systems have been disclosed in the patent literature that provide solutions to specific - - . ~ -problems associated with robot conffol. For exampIe, the problem of determining the extent of the -~
robot's workspace, and the location of~obstacles it contains is addressed by U.S. Patents ;~ ~ 4,639,878 to Day et al, and 4,685,054~to Manninen et al. The Day Patent employs three cameras - - `~
and the Manninen patent employs a laser pointer to determine this information.
. .
The problem of coping with unprogrammed changes in the robot's kinemadcs is the object of U.S. Patent No. 4,831,549 to Red~ et al. The robot controller must be supplied with pre- -programmed forward and inverse kinemadc equations, and the Red system can only apply :- -correcdons to these equadons to attempt to improve the positioning and orientadon accuracy of the robot. ~ ~ -Patent No. 4,764,873 to Libby, provides a partial soludon to the find-path problem. A device ~ -is described which can detect intersections of straight line paths with specific, rectilinear obstacle ~ :
sets. The device is inadequate for dealing with the general problem of generating collision free : -movements of multijointed robot manipulators. -FeW robot contol Isystems have been described that successfully integrate solutions to~ more ; ~ -than one aspect of the collision free control of robot manipulators. The present invention provides a novel computadonal architecture that can integrate simple descriptions of the workspace contents (e.g., as supplied by the sensors in U.S: Patents 4,639,878, and 4,685,054 above) with automatdcally derived information about the kinemadcs of the robot to produce movements of the arm that posidon the end-effector at a goal point without colliding the arm with obstacles in the ~ -:
workspace.

:: :

2D~90~1~

- :
Systems to control robot manipulators are typically designed by deriving a set of kinemadc equadons which express the physical constraints of the robot manipula~or, and then deve}oping a computer program that employs these equations to generate sequences of motor control commands that move the robot's end-effector from its initial position to a target position specified in some system of coordinates. Each cornrnand consists~ of ~,.n,i joint settings for an "n" DOF arm.
While~ these control systems work well for simple, carefully engineered environments, they suffer from senous limitations when applied to more~realistic environments. First, wear and tear on mechanical parts changes the kinema~tics of manipulators, and sensor characteIistics change with time.~ Whea this happens, the control; program must be updated or the robot must undergo maintenance. ;Second, realistic workspaces~often contain sets of unpredic~table obstacles. Avoiding collisions with these obstacles adds~to the complexity of control systems and can significantly slow response time If the obstacles~have~complioated~shapes or~change positlon often. Physical arm configurations that intersect obstacles within the robot's workspace are termed illegal configurations; all other physical arm configurations are legaL Nature has designed systems which deàl with these kinds of problems~ remarkably welL The key to this success is a high degree of ausonomous adaphve learn~ng.

It is the general object of this invention to provide~ a con~ller~that can determme a trajectory to~ move a controlled system from ~àn initial ~system configuration ~to any one of a number of desirable~ goal configur~ons~without encountering prohibited system configurations.
Is it a furthèr general object of this invention to provide a controller that can be applied to systems that are characterized by the following properties: (1) the state of the environment that the system must~be negotiated through can~be~detected by sensors and represented by a continuous metric space, (2) the system:configurahons can~ be described as a continuous metrlc space, (3) the goals of the~ system~ can be satisfied~ by a subset of the space of system configurations and can be described by points in a targedng metnc space that can be mapped to points in the system configuration~ metric space, and ~4) ~obshcles to the operation ~of the system can be mapped to points in the environment metric space ~and these pomts can additionally be mapped to points in the system configuration metric space represendng prohibited system configurations.
It is another object of this invention to provide a controller for use in a robotic manipulator system which aums to overcome the disadvantages of the prior art.
. . - , .: .
.: . ..: ~-. .
~:~ :. . ~: :, - 3 ~
.-. -- :~:

2 0 ~

It is also an object of this invention to provide a device that can be used as part of a controller for a robot manipulator arm that is able to avoid collisions with obstacles in its workspace in a ~; novel rnanner that i5 significantly faster than the prior art. ~ ~
It is yet another object of this invention to provide a device that can be used as part of a , - . -:~ controller for a robot manipulator arrn that is able to avoid collisions with arbitrarily shaped - .
obstacles.
- , , -:
It is stilI another object of this invention to provide a devlce which allows arbitraTy positioning -accuracy where accuracy is a funcnon of the capacity of the system and capacity is a function of cost.
It is yet~another object of this invention to prov~de a device which enables selecting a trajectory from multiple trajectones, such trajectories beginning at any initial system configuration and extending to one of a number of ~goal system configurations.
, It is another object of this invention to provide a procedure that can dispense with the need for preprogrammed knowledge of the standard robotic kinematic equations7 by autornatically acquiIing this knowledge during a preprocessing step, thus enabling the sarne controller to work with arms of arbitrary design.
-. . ..
It is a further object of this invention to provide a controller that integrates each of the components of this invention to ~provide a cohesive system for the purpose of controlling a robot manipulator arrn t'nat is able to avoid collisions with obstacles in its workspace.
~ , It is still another object of dlis~invention to provide a procedure to efficiendy map any set of arbitrarily shaped obstacles into legal and ~illegal configurations for the purposes of determining feasible trajectories.
It is still another ob~ect of this invention to provlde a procedure having a separation between a preprocessing (initialization) stage that initializes the system for all possible future obstacles and a controlling stage tha~ computes trajectories that avoids specific obstacles, such separation allowing the controlling stage to be very efficient at the expense of a less efficient preprocessing stage.
It is another objectiof this invention to provide a procedure that allows targets points to be selected in a novel and efficient manner using a targeting system such as a pair of video cameras. -~;
It is another object of this invention to provide a procedure that can adapt to changes in the ~ -robot arrn kinematics and joint sensor characteristics without human intervention by reapplication of the preprocessing stage.

:
2 0 ~ 9: 0:~9 ~

It is a first object of this disclosure to descnbe a neural controller, to be called a neuroplanner, for finding a se~uence of system configùrations~from an initial system configuration to any one of a set: of ~goal system configurations ~ specified by a :targ'eting :system in the ~presence of prohibiEed system configùrations and compnsmg. (a) mèans for specifymg goal system configurations by selectlng;a targetmg configtttation~that~ls~ In~turn~ correla~ed wlth one or more of the system configurations; (b)~ means for creating an~ environment network, a system configuration network, and~a targeting configuration;~net~k that`~respectively encode quantized representations of the environment~infonttation,~the~confi~s,: ~ t~e ng ~fi~ ions; (c) mea s for correlating~thesenvironmental 'network~with' the system ~co;nfiguradon network and also ela~ é t=, gèhng~c~ et ~with`the~system~config t on net ork so as to e abl~e to'speci~syst ~ ~n~ions~à ~'g ~systët '~conf1g s;respectively; (d) èans~'~for'finding~ a~trajecto~;~ con~ on;~e.g.,~the curent system configuration,~to a~goa ~ system ~on that éxcludes ~ t s~t~ c aE ons. ~ ' e~esent invention has~'po~tentiàl application~in~general domains.~For example in the ~n~ ~d~oma n, the~ tént ~ ould~enco e~opa onal sensor data, the s~m ~u a:t'on netw~ld~' de ~`o f1g r tions of t e~ system, and t e t get ng ` s ~ ~network would~per' 't~ t e ~ner ~ goal of ~e system~ n àbstract t~s; e.g., a specified ràtio of che~cal A~ to ~cal B~ or;~a~pre~ product viscosity~. ~ va , da gerous or ~ iona~ld~be~cot 13te ~wlth~pro ibited~ m ~guato s a~ tàtes).'Abs~t goals`would~bè~ c~with~desi~rable configurat ns ~ the~syste ~ on~would énable the~ ~n`t ol ~move~f om~an exlsting c ig r t on t a go31~tton (if such a s~uence o c n t s ~ XIS S . ~
It~is a~se c d~obJect of t is" s 1 s ~'p~i e~an~emb ~m nt~of the~i~en ion in ther~o!botics domain~In~this~ o~me`~n~t~às~a~r ot~ ~ n~ the~;en nment netwo~ is a~sentadon of t ~ ~(ca~ed~ t e' workspace ne~ork) encoding th se reg ons at~a ~ ccu led,~the syst ~co~on network~is~a quant zed~ r resentati n of the rm conf~gu atlons~ (called t e~ a con igurat on ~net , a d' the~ t et ng net ork~ is a qua dzed ~res~tation~ofthe~configurations~of~a~pomt~g~icet atspecilesa~ oint'n~t eed ensiona space;that is to be touched by the~énd-effector. l~é targeting network in this embodiment is called the eye configuration network since the ~intènt is to have the~ arm reach for the point "obsehed" by the targeting~ system. Correspon~gly, the~ targeting configurations are called eye configura~ons.
By this~invention, a system Is~provided for`fin~ng a~sequence of arm configurations from an initia~arm configuration to any one of a set of goal ~arm configurations in~the presence of prohibited 2 ~ ~, q ~

arm configurations, comprismg: ~a) means for creating a workspace network, an arrn conf guration network, and an eye configuration network that consists of artificial neurons which quantize their respective metric spaces: the workspace, the arrn configuration space, and the eye configuration space; (b) means for correlating the workspace network neurons (workspace neurons for short) with the arm configuration network neurons (arm neurons for short) and also for correlating the eye~configuration network neurons~ (eye neurons for short) with the arm neurons; (c) means for deactivating arm neurons correlated with activated workspace neurons where these workspace neurons are activated because ~they represent occupied regions of the workspace; (d) means for activa~ing goa: arrn nourons correlated with activated eye neurons where these eye neurons are achvated because they represent the~ configuradon of the pomting device~that serves as the eyes of the robot; and~ (e) means for finding a collision-free path from one arm neuron to another alm neuron,;that path excludiIlg any deactivated neurons.
Additionally, it is a goal of ~thls:disclosure~to provide:~(a3 means~for sensing the occupied regions; of the workspace (e.g., ~a~ stereo ~camera~ system or a~scanning laser range finder) to determine which workspace neuron is activated, such environment sensors being fixed relative to the~f rst joint; (b) means for~sensing joint~ sémngs ~of the~arm (hereafter called joint sensors; e.g., joint displacement or joint~ angle offset~ sensors) ~to deterrnine~ which arm neuron represents the current~arm configuration; (c); means for indicating the position in the workspace that is to be touched by the end-effector--any~pointing device in which thé device configuration can be sensed and which uniquely defines a target~point~ in~ the workspace; e.g., a pa* of laser pointers where the lines-of-sight~ intersect at the~ target point or ~a pair of cameras that both center the target point in ;their fields of view; (d)~means for;having the~targeting~system track the~end-effector during a preprocessing phase; e.g., by centering a~pair of ~cameras on a special light attached tto the end~
effector or by using~ positioning; data from an ~æcelerometer attached to the end-effector; (e) ~means to move the arm through a representative set of conf:gurations by random increments of the joint settings;~ e.g., using a random number generator; and (f) means for moving the arm through the seque~nce~ of arm configurations in the trajectory provided by the neuroplanner.
T his invenuon provides~ a~neural controller, ~to be called a neuroplanner, for a robot having a body and at least one arm having any number or degrees-of-freedom, an "n"-degree-of-freedom manlp~lator, with arm links and arm joints that may be revolute andlor prismatic.
:
An important feature of the invention is the means by which the networks quantize their respective spaces. Each network consists of a plurarity of neurons, each either activated or :
deactivated, and each containing a vector having ~the same dimensionality as the network's space;
: ~ :
- 6- :

, '-'- ~
.. .. . . . . . . . .

:

2 ~ ~ S O ~

e.g., an arm configuration network quantlzing a 6 DOF ann configuration space would consist of neurons containing 6-dimensional vectors; an eye configuration network quantizing the pararneter space for a pointing device consisting of t~vo targeting cameras mounted on a swivelling pallet (one degree of freedome for each camera and one for the pallet) would consists of neurons containing 3-dimensional vectors. The arrangement of the vectors of tho neurons determines a nearest neighbor quantization of the associated space; i.e.~, a neuron represents all points that are nearest neighbors to the vector in the neuron. For oxample, the current arm configuration is represented by the arm neuron with~the vector that is closest~to the arm neuron where distance is defined using an appropnato motric~ such as the~ Euclidean metric.~
In addition, the notwork~ mus~ be~ arranged topographically; i.e., the ~neurons are arranged on a multi-dirnensional grid of tho same~ dimensionality ~as the space such that each neuron has two neighbors in the grid along~eac~h~dimension ~except for those along the border? and such that ne~ghbonng neurons have vectors that~are nelghbors in the associated space. This is equivalent to saying that a continuous curve through the space can be represented as a continous chain of neighboring~neurons in tho network (thoso neurons with vectors closest to the cwe).
One embodirnent of a network Is~a regùlar~grid-like arrangement of the neuron vectors in the space; i.e., each neuron represents equal-sized hypercubes in the space. Another more practical embodttnent adaptively arranges the vectors such that the density of the vectors is greater in regions of the space that are more important to the system being controlIed. For exarnple, since the body of the~robot prevents the arm from achieving some configurations, there is no need to finely quantize these regions of the space and few vectors need be devoted to these regions. On the other hand, the regions corresponding to configuratlons~ often attained by the robot (e.g., those that touch the workpiece) should be finely quantized and many vectors should be devoted to these regions. A
well-known algorithm due to T. Kohonen, the self-organizing map, is an appropriate technique for quantizing ;the space in this way.
By increasing the number~of neurons in a network, the spaee is more finely quantized and consequendy more accurately representated by the network. Thus the accuracy of the controller can be made arbitrarily precise by increasing the number of neurons in the network. However there is a limit tolthe number of neurons that can~be practically made available to a network; i.e1, the accuracy is limited by the availability of neurons; e.g., if neurons are implemented in software, memory size is the limitation; if they are irnplemented as silicon hardware devices, the availaMe chip area is the limitation.
, ,-~ ~:

:

2 ~
, , , I .

,. .
The present invention is a neural architecture providing an alternative approach to the design of control systems. Like biological systems, neural architectures support a high degree of adaptability and are capable of learning sensory-motor constraints from training examples. The neural architecture, in its robotic embodiment, is one which, in addition to learning kinematic constraints for eye-hand coordination, also learns obstacle avoidance constraints that can be used to rapidly find collision-free movements of the arm. This architecture is able to generate many alternative solution configurations which touch ~a ~target point, such learned collision constraints bemg used to find an arm movement which IS guaranteed collision free. ~ :
Neuroplanners integrate three~ continuous vector spaces: a targeting configuration space, a system con guration space, and an environment space. The targeting configuration space is used to guide the system being controlled; the system configuration space represents the state of the system being guided; and the environment space is a constraint space that dictates illegal system - - -configurations~ Each vector represents a point in a space. The neuroplanner does not store all points in the space but uses the self-organizing Kohonen algorithm (in one embodiment) to create a qu~zed representation of the space.
More specifically, the environment, system configuration, and targeting configuration spaces are represented by corresponding ~networks of artificial neurons that quantize the corresponding ~ -spaces. A neuron is a computing element that stores four data values: a unique identifier (id); a state indicator, one of neutral, activated, or deactiVated; a k-vector rjl,j2,...,jkl which is the centroid of a k-tupIè of intervals ~jI+el,j2+e2,...,jn+ek] representing a~region of k-dimensional space where k is the neuron's dimension ttypically 3 for environment neurons, 6 for system neurons, and 3 for targeting neurons) and 2ei is the interval width for dimension i; and a list containing the ids of neighboring neurons. When the list associated with neuron "p" contains the id of a neuron "q", "p"
is said to be connected to "q". Similarly, "p" may be connected to another neuron "r" by adding "r"
to the list associated with "p". ~ i : -, Connections are generally made from one network to another. More specifically, environment neurons are connected to system neurons, system neurons are connected to environment neurons, and targeting neurons are connected;to system neurons. Additionally, system nellrons are also connected to other system neurons to keep track of neighbours. Hence, the system configuration network is topographically organized. Thus, given a system neuron, it is possible to determine which other neurons are neighbours. This is needed for determining a path of neurons in the system configuration network that represents the corresponding path in the associated space. In this embodiment, it is not necessary for the other networks to be topographically organized.

~` 2~1309~
. . .

Each of the neurons could be individual processors or software data structures contained in a single conventional computer. Each of the three networks encodes its own specific domain in its neurons. More specifically, they encode the following: The domain of the environment network is the 3-dimensional volume of the workspace of the robot. Each environment neuron contains a 3-t uple that represents one of the equai-sized subvolumes of the quantized domain (in one embodiment). The domain of ~the system configuration network is the "n"-dimensional configuration space of the robot's arm, ~typically~n is 6 or more. Each system neuron contains an n-tuple that represents one of the equal-sized subdivisions of the quantized arm configuration space.
The~ domain of the targeting confiOuration network is the; "m"-dimensional configuration space of t he robot's~targeting device--typieally~m is 3.~Each targenng neuron contains an m-tuple that represents ~one of the e~ual-sized subvolumes of the quantized domain (in one embodiment). RecaIl that~another embodiment based~on the Kohonen ~vector quantizatio:n) self-organization map may have unequal sized volume. ~
Neuroplanners are intended to~be used in~domains where: there is a natura,l correlation between targeting configuration~and~system configuration spaces; the environment space can be u sed to constrain the system~configuration space; and these spaces and correlations can be learned In the neuroplanner of the present~ invention,~ there are ~two phases: a preprocessing phase, where initialization and correlahon (learning) occurs ;and~ a pIanning phase, where a simple plan is generated. As~ used he~rein, a plan ;in~ the~ system ~space is an ordered sequence o~ legal system configurations that satisfiès ~a goal by moving ~the controlied system from an initial system configuration to a desired~system~ conf guration without violating the environment space constraints. A corresponding; plan in the targeting s~pace is a similar sequence of targeting rather than system configurations.
This architecture~relies on~sensors~to d~tect motor lirnits, collision detectors to prevent self~
intersection during adaption~and calibration ~(but not during normal use), and a simple vision facility that can observe the environment, e.g., the arm and obstacles.
The environmeni network Is~ a workspace oceupaney map, an environment neuron represents a volume element that is eit er:~ occupied by an obstaele or the arm (the self-image); or unoccupied.
If any~part of the vol~ume element is oecupied,~the neuron represent an oecupiedlvolume. The representation is conservative, wlth oespect to path planning, the arm will avoid a partially-occupied element. For the planner to be more aceurate, it is neeessary to use smaller volume elements. The obstacles in an instanee of the f;nd-path problem can be deseribed simply by marking the occupied volume elements aetive.
-- .,, ~9~

'' , , .~ , , ~ , ~
2 ~

The power of the neuroplanner architecture is derived primarily from the interconnection structure of the three quantized networks.
The environment network is~a sensory network that dynamically tracks the observable state ~ .
of the system. In the robotic embodiment, ;the environment neuron represents a volume element ~ -occupied;eithèr by an obstacle~or the arm (the self-image)--the background is subtracted away.
s ~ In simple applications, a two-dimensional quantizauon may suffice. More generally, a three-dimensional~quantization is required.
For planning purposes~ (to~be discussed later), we`need to correlate the environment and system spaces.~ More~ specifically,~we~ need~to ~be able to constrain the neuroplanner so that it avoids o bstacles. This corresponds to ~elimina:ting all arm;configurations whose self-image regions mtersect with~obssacle~regions m~the~environment space.~Intuiuvely, we want an environment neuron~ corresponding to an obstacle; to~inhibie ali ~system neurons where some part of the ~h~ corresponding self-image would intersect the environment neuron. These are system neurons that ~; the arm will not be allowed to assume.~ On the other hand, the arm at any one time is in a current configuration. To prevent the self-image from being treated as its own obstaCIe, the system neuron c orresponding to thè current configuration ~ternporarily disables the self-image env*onment neurons; l.e., subtracts the current~ self~image neurons from the environment network. .
More~ precisely, an environ;ment~neuron~is a neuron in the environment network. It is active (tumed on) if it represents an area occupied by an object and 2. ~no connection from the~ systèm~network~éxphcitly inhibits it (turns it off)--the .
current self-image ~;conn~ections.
It ~ls connected to all and only those system~neurons that satisfy the following~
1. The environment neulon Pe connects to~system neuron Ps if Pe is a neuron on the arrn when the arm is in ~the configurauon~represented by Ps. -The environment network consists of neurons that quantize the 3-d workspace into a gnd of ~ ~
volume elements. The ~the active neurons form an image that "looks like the obstacle". In a typical ~ -scenario, connections would numb~m the~thousands, not just one or two.
Environment neurons can inhibit system neurons if they are on (obstacles) and not inhibited by the culTent system neuron. Note: the active system neuron can inhibit many environment neurons; an active environment neuron can inhibit many system neurons. ~ ~
: :

- 10- . ~

,",, ~,", ,,; ",,,j,,,,,~ " ~"" , ,, ",, ",,~ " ,, "~",;,,,,, ,"" "; ,~ ~"j,";; ;, ,,,,, ,,; " ,, ; :~ ,""i-,:"'~j~, ` : ~ i, 'i` ' ' 2 ~ l s a ~

The targeting network is a representation of the conf~gurations that can be adopted by the targeting device such a a stereo camera when used~to "look at a target"--the goal or intended destination of the robot arm's end-effector. A targeting neuron represents a volume element occupied by a quantized representation of the targeting configurations. We think of these as eye configurations to differentiate them from the more traditional arm configurations. For this application, 3 or more degrees of freedom would be expected of the eye configurations although 2 might~suffice in very simple 2-dimensional environments.
In practice, it is not only mconvenient but also dif~lcult to specify target goals for the end~
;effector by llsting ~goal arm configurations. Often, there are many--possibly an infinite number that satis~fy a given end-effector~ target. It~ is much simpler to represent a goal as an eye configuration in a~ targeting ~space--~any one that targets the 3-dimensional workspace at the the nght place. ~
;; To correlate the targeting~and system spacès, each targeting point corresponding to an eye configuration that focuses on some point in the workspace that can be reached by the end-effector must~be connected to all system neurons for arm configurations that touch the point.
More formally, a targeting neuron is a neuron in the targeting network. It is active (turned on) if 1. it~represents the eye configura~on that focuses the targetting device on a target point.
It is connected to all and only those system neurons ~that~satisfy the following~
The targeting neuron Ps connects to system neuron Pc if the corresponding arm and eye configurations make the~targeting target~ and the arm end-effector intersect.
A targeting neuron can activate an ;arbitrary number of goal system neurons, some of which may be ~nhibited by tho environment network.
The system network is a;representation of the arm configurations. Note that it has nothing to do with cartesian coordinates of the end-effector. A system neuron represents a volume element occupied by a quantized representation of the arrn configurations. For this application, a n-degree of freedom arm with n>S~ might be typical. The larger the n, the more the redundancy but also the more the cost in terms of neural processors for hardware implementations or main memory for software irnplementations A system neuron is a neuron ln the system network. It can be active (turned on) if 1. it represents the current arrn configuration (the current system neuron).
~ ~ . : .:
. .,,.,-~, ~,"

~:
. . . .
: . -: - .:
''''.``',,''' ' ','',;' 2~a~

2. it represents one of a large number~of goal conf1gurations activated by the targeting ; ' ~'' ' network but not inhibitd~by~the environment network (a~ goal system neuron). " ', The system network is connected~ only to the~envwnment~ network (for deactivating the self- ' ' image). More important, the system~nétwork behaves as~a rudimentary planner by finding any shortest path~from the current system neuron to any~one of the~active goal system neurons.
Because~neurons~ store their associated~vectors;~in~ the~vector space they quantize, the path can be used~to construct a sequence;of ~arm~configurations~that will take the arm from the current configuration to any goal configuration whîle avoiding obstacles. Since configuration space is " ' '-"
béing used rather~than cartesian~'space and~t~his; spacé,is topographica}, the arm may make arbitrary shaped~ convolutions to avoid ~the- obstacles.~ Yet the ,path is the shortest possible in configuration ' , ,' "

~e t ree ~net orks a e ~us ~in~ tho planning (or problem-solving)~ pha e as follows:,; ~ ',' "
nitialization The current arm configur tion~activates~exactly one neuron in the system network (the , ~' '.. ',' current system neuron).
2. I he target is selected by~ the targetting cameras causing exacdy one neuron in the ;~
targeting network~(the~goai targeting~neuronj to~be activated.
3. ~ ~;The obstacles and the~image~of the a~m activate~many neurons in the environment ''~
network.
Propagation~
4.~ Thecurrentsystemneuron~inhibits~many~environmentneurons(theself-image). ~ ' 5.~ The~ active environment~neurons inhibit~many system neurons (the system neurons '' ' ~-that conflict with obstacles). ~
" ~ 6. ~ The ~goal targeting neuron actlvates many~ system neurons (goal system neurons). , : ' Solution Determination ~, jl; ~ , 7 . A shortest path from the current system neuron to an uninhibited goal system neuron ' ' ~"~ that passes only through~uninhibited system neurons is a so}ution path.
-:~: : ::: ~ .
Execution 8. The arm configurations~corresponding to~the-so}ution path provides a smooth , ,~, sequence of a~n configurations that can be interpolated (if necessary) to bring the arrn's end-effector to~the~target neuron without touching obstacles.

:

- 12~-:, , - ;~
2 ~

Only two time steps are needed to accomplish the above since most of the activity occurs in parallel. A standard path planning algorithm can then be used to find a minimal path. If each neuron is a processor in a systolic array, a spreading acdvation algorithm similar to that described by [Miller and Stout, 1985] could be used to find a minimal path in O(~rn) time where n is the number of neurons in the network.
The preprocessing phase must be executed whenever a fundamental change to the the robot is made; e.g., inihal assembly, addition or removal of arm links, bending of the links, extreme wear, changes in size due to extreme temperatures, etc. In general, not all neurons allocated to the system and targeting networks ~are~ usable--some correspond to impossible arm or eye configurations. To~ensure that impossible configurauons are not part of neuroplanner solutions, it is convenient to permanently inhibit those ~that cannot be reached. The remainder are found during network~correlation which is applied once~ as part of the~ preprocessing step in a workspace entirely devold of obstacles as follows~
1. The arm is successively movéd through all possible quantized configurations using a probabilistic algori~m. Movement is permitted only if there is no response from the collision sensors.
2. For each valid arm configuration, the targeting device is focused on the end-effector and the arm's self-image noted.
3. The corresponding system neuron (for the arm configuration), targeting neuron (for the eye configuration),~ and obstacle neurons (for the self-image) are correlated by mahng connections as described above ; The present invention provides a soludon to one version of the find-path problem, namely the following: given a robot arm with~"d" degrees of freedom and a 3-dimensional workspace that is ; po~entially cluttered with obstacles,~find a path (if one exists) from some initial configuration of the arrn to any configuration in which the end-effector targets a specified 3-dimensional location. In the general case (where obstacles may be moving), the ~best algorithm requires exponential time. By requiring that the path be a shortest path (independent of the metric), the problem remains exponendal even when the objects~ are restricted to polyhedral obstacles. When the obstacles are fixed, the problem is polynomial in the number of degrees of freedom. In particular, an approximate algorithn~ is O(pqrd~l), where "d" is the number of degrees of freedom in an arm that has had its joint ranges divided into "r" values, and where the number of faces and edges in the polyhedrons representing the arm and the obstacles respectively are "p" and "q".
To obtain fast algorithms, approximate techniques with special preprocessing steps are necessary. In the present invention the problem domain is quantized by dividing each joint range into "ri' values, and by encoding the problem independent information requiring O(rd) space in a ,,,'',;',':"'-', . . . ...
' ' :-~- ' ";
-, :,~ -~ 2~0 ~:~ 0 9 3 ~;, , . , . ~ ~

preprocessing step that is done once for~each robot arm. Subsequently, the problem solution stage uses~ the same pre-processed informatlon on any number of mstances of 'the problem. Each instance i s provided by specifying a set of obstacles, a current configuration, and a target Although the preprocessing~stage can be computationally intensive, it is only performed once per~robot arm. The problem~solution~s~age,;however, can be perforrned any number of times on differént instances of the find-pa~h problem uslng~the~same~preprocessed information. The probIem solution stage executes in time~that~it~is independent~of the~number of obstacles on suitable parallel neura} hardware. On the ~er hand, to store this compiled mformation in neural hardware requires O(rd) neural elements.
In the~previous~ embodim~ent of ~the~invention, th~e; neuroplanner provides a solution to the r blomof;movingana toa~ tsp ~byat ge'ng~lce~mt e~se c of obstacles. n ~o~her~ embodiment of this mvention, the~ bi~rectionàl~ om~ent, the :neuroplanner architecture provides- a~ solution t o the~ invers'e~problem, namely,'; of moving the targeting device to look at the end-effector. Tn this case,~ the~targetting ~device is likely to be~ a more complicated neck~eadleye system that can peer at arbitrary points around and~ between obstacles. The two systems may be coupled to~permit àn~integrate~mechan~i~sm ~as ~oppo`sed to two separate mechanisms) either to move the end-effector to ~o target speoified by the~targerang device or~to have~the targetting device "look"~at the end-effector, both~of these~ being~ in the presence of obstacles.
The- bidirectional neuroplanner~ is~ extend; ed ~to permit~ path~planning in the~ eye space. This allows the~system~to r~nove the~articulated;camera platform into a goal position where it can see the end-effector of the~robot. To;~do~'this,~it~must find~an~eye~trajectory that avoids colliding with obstacles, M~ oreover, the goal position mùst~have a clear line-of-sigl~t to the end-effector--no object~must~intersect~with~the two virtua~l rays~extending from the carnera pa~r playLng the Tole of targeting device to~the~end-effector. ~
T wo~ notions are added to deal with this extension: `the eye self-image analogous to the arm self-image~and a new notion, the lines-of-sight self-image, which is the set of neurons in the environment network that;are~on the-path~ *om'the oamera lenses~to the foveating point.
The environment network is extended~so that;obstacle connections can~be made to both the arm an'dI~eye spaces. Additionally, line.of-sight~connections are added 'to correlate environment neurons on ~the line-of-sigh~for `a~particular eye configuration with the corresponding eye configuration neuron. Recall~the existence of connections in the other direction, from the arm network to the environment network to correlate~an arm configuration neuron with a~m self-image neurons. Similar connections~are~added from the èye~network to the environment network to correlate eye configuration neurons to eye:self-imago neurons.

: ~
..

':

0 ~ ~$ 0 9 ~

~ ~ , Path planning in the eye space is ~exactly analogous to ~path planning in the arm space.
Conceptually, it is sufficient to interchange the roles of the arm and eye spaces. The current arm configuration is used to activate ~eye ~configurations. The current eye configuration is used to deactivate~the eye self-image neurons~in~the environment;network. The environment network eliminates i~egal eye configurahons~--where an ~obstacles intsects the camera platform or blocks either of the lines-of-sight.
In more detail, the~eye network:path piannor's ~ask ls~to~find a path from the current eye configuration~ neuron~ to an goal;~neuron through~uninhibited~neurons. A goal neuron is an eye neuron~ that corresponds to an ~eye~ configuration in~ which~ the~ cameras (targeting device) are focused on the end-effector. Clé~, ~the platform cannot intersect an obstacle nor have its lines-of-sight bl~ked~by~ one. ~
It ~first must determine~pote~dal ~goal éye neurons. ~The prevwus embodiment ~which is included in the bi~rectional~embodt~ment) already provides connections between :the eye and arm networks which correlate eye~and arm~configuration~;neurons when the eye foveadon point~is the same~ as~the~arm end-effector position.~In~the~bi~ction~ embo~ent, connections are made both : ~ r ~ ways~ during~learning.~ By ~following these connections from the current arm configuration neuron to the eye network, we obtain a sèt of eye neurons~that~represent configurations of the camera and ~, platform (targeting device) that focus; on~ the end-effector.~It ls not nown y whether or not they intersect with~ obstacles ~or violate the hnes-of-sight constraint.
lt must also ensure that the~currènt eye configuration and those whose self-image intersects with the~c~rent~one are not ~hibited.~ O~erwlse, movement away~from the cur ent position would be pr ite~. Since~every eye~configuration~neuron~is correlated~with its self image neurons in the environment space, it ls sufficient io deactlvate these. The :current configuration is no longer viewed as~ an obstacle~ by the environment network.
Additionally, neur ~ons that collide~with ~obstacles~are not permitted in the solution path. Nor are~goal neurons whose the lines-of-sight are~blocked by obstacles. Note that intermediate neurons in~the~solution can havè their lmes-of-slght~blocked--the cameras can foveate anywhere while being moved. Eliminating neurons that ~collide with obstacles is achieved by deactivating any neuron that receives obstacle connection signals~from active neurons in the environment network.
The line-of-sight constraint can be satisfied by deacdvating po~ential goal eye neurons that receive lines-of-sight connecdon signals~from~active neurons in the environment network.
A Short ~Igorithmic Description of the Bidirectional Neuroplanner The following summarizes the abov: in an algonthmic~fashion.

15~
: , ,.,. : .
~ .

- - ~ ,, 2~ 0 ~ ~
:,. ~ , ~ , . . .

The system is trained without obstacles in two stages~
1. Generate a representadve set of arm and~ eye configurauons by randomly moving the arm and articulated camera platform. In each case, orient the carneras to focus on the end-effector and ; use the associated configurations as training data for the Kohonen algorithm to self-organize the arm and eye networks.~
2. ~After self-organization, generate ~a~new~representative set of arm and eye configurations as above. In each case, using the Kohonen algorithm, determine the corresponding arm and eye neurons~a~and e respectively and~perfi~rm~the;followl:ng:~ ~
~a) For each environment neuron o ln the arm self-lmage,~ -i ) grow a connection from o to a~and~from a to o (b) For each environment neu~on o ~ e eye~ self-image, i?~ grow an obstacl~e~connecion from~o to e grow~a connection~fr:om~e to o ~
(c) FOr each environment n~on o in the lines-of-sight self-image, grow~a lines-of-sight connection~ from o to;e i ~ , -; (d)~ o or~e~is~at~the limit~of~movement,~connect~the;permanently illegal configurations neuron~ to the corresponding~ a~rm or eye neuron. ~
The~system plans in~; the arm~ space as~follows~arbitrary obstacles allowed):, All neurons are placed in`the inactivé state,:the-articulated cameras are oriented to target, and the ~environment network~is~`~ini:tialized~ (areas~occupied by objects are active; otherwise inactive).
2.~ The~arm and eye neurons corresponding;~to the~current arm and eye configurations are determined by Kohonen competion.
3 . The cùrrent arm neuron inhibits `the~environment~neurons~it is connected to ~its self-irnage).
4 . ~ ~ Simultanously,~ the following occurs~
a)~ t he active eye neuron activa~tes a~set of alm nèurons (potential goal neurons). , b) ~ the~active~environment néurons inhibit aIl connected arm neurons ~arm configurations that intèrsoct obstacbs).; ~
5. The~path planner finds a rninimal path from~;the current arm~neuron to any one of the active goàl ~neurons that includes only `inactive (not inhibited)~ neurons.

6. The ~weights of these ;neurons represent tho soluhon~ arrn trajectory.
The~system plans in the eye space as follows (arbit;rary obstacles allowed):
Note analogous to above except; for part 4c. ~
1. All neurons are placed in the inac~ive state, the arrn is placed in some arbitrary configuration and the environment network is initialized ~areàs occupied by objects are active; otherwise inactive).
2. The arrn and eye neurons crresponding to the` current arm and eye configurations are determined by Kohonen competion.

~ ~ ~ 16--:

. , 2~0~$a~

3. The current eye neuron inhibits the environment~neurons it is connected to ffts self-image).
4. Simultanously, the following occurs~
(a) the active arm neuron~ activates a set of èye neurons (potential goal neurons).
(b) the active environment neurons inhibit alI obstacle-connected eye neurons (eye ;
configurations that intersect obstacles).
(c) the active environment neurons inhibit all.line-of-sight-connected eye neurons that are ; ~ ' potential goals (goal eye ~configurations that cannot see the end-effector). ' ' "" ' -5.~; The~path planner finds a~ m nimai~ path~from the current eye~neuron to any one of the active ;goal neurons~that includes~only~inactive~(not inhibited) neurons. ~ ' 6. The waghts of these neurons~represesit the~;solution eye trajectory. " ', ,~

~ the accompanying~in~js, ~
Figure~1is~a~schèm 'cview`of~onee io ment~of~t ea apt-vérobotarchitecture;
Figure~2~1s~à~schematic composite~view~in~Fig es 2(a), 2(b), 2(c)~and 2(d) of one émo mentofthemetho for~lear ' garm-eyeco~iguration;
Figure 3 is a~schematic view'oone~em~ nent of the àdaptive arm controller architecture;
Figure 4 is a schematla~composite~view~ Figures 4(a)~and~4(b) of one~em~ent of the ''~
inter-network connections,~re Figure 4~a) ~shows the connections from Me to Ma, and Figure ' ' ' ' ' 4(b) shows the conne tions from~Mo~to Ma;~
' Figure~S~is a composite~representation in~Figure~5(a) and Figure 5(b) of the obstacle network;
Figure; 6 is~a schematic of ~the~m`ethod~for~letun~ng w~uch involves weight modification; ' Figure 7 of a block diagra~m: showing one embodiment of the interface to the neuroplanner Figure ~8 is~ a schematic~drawing representing~one embodiment of a: intuitive 2-dimensiona}
"representation~ of a~system configuration space~with legal~and illegal regions;' - ' '' ;
Figure 9~ is a schematic~draw1ng represent1ng~ one embod1ment of a path through a system conf1guration space;
Figure 10 is' al' schematic~ illustration~ o f ~one embodiment of the internal lstructure of a ; ' ' ^ ' ' neuroplanner, ~
F1gure 1 l is a schematic~ composite view~ m Fiugre l l (a) a~d Figure l 1 (b) of one embodiment - ~ ' o ftherobotenv~ronmentnetwork;

, ~ ~
,-: ' ', :- ' .~ , -.

-2 0 1 9 ~:~ 9: ~

Figure ~12 is a schematic' composite~view m ~Figure~ 12(a), Flgure ~12(b) and Figure 12(c) of :
one embodiment of a typical learning ~arm run showing: in Figure 12(a) 0 itera~ons; in Figure 12tb) ~, , 500~iterations; and in Figure 12(c) 60,000 iterations; ~
Figure 13 is a schematic~composite view~in~Figure 13(a), Figure 13(b) and Figure 13(c) of a ' ., typical learnlng arm run showing. in Figure 1~3(a) direct~eyes ~o~target; in Figure l3tb) find legaltarget:configurations; and in Figure~:13(c) selectpath~and~move; ~:
Figure~ 4 IS ~a;schematic'v~ew`of;one,~ombodiment of a feasible internal s~ucture for a ' ,,~
netlroplanner, Figure:~15:~is a schematic representàtion~of one em~lment~of an oyeslhea~neck system coupledwith~an:arm;~
Figure~:16~is~a schematic~representation~of:one:~embodirnent of an:eye/head/neck system ' :~

Figure:17 is a~schematic~composite representation~in~Figure 17~a) and Figure 17(b) of one ,~
ernbodiment of the environmént nétwork:for~an:arbitrary;workspace;:
; ` Figure~ 18 ,is ~a :schematic co~site~view ~in Figure~ 1 8(a) and~: Figure l 8(b) of one emoDdimeDt of a method wher~by thè~system'fin'ds a collision-fre,e~tra~ectoy;;:~
Figure~19 is:a~schematic co,mpc site~viéw~in~Figure l9~a~ :and Figure: l9(b~ of one embodiment of ~a me~hod~whereby~a system find~a~colIision-free ar~culated~càmèra/platfo,rm trajectory; ~
Figure 20 is a schematic v~ew o~yet~another~embodiment:of the neuroplanner design; and Egure~21~ is~a~ t ch~ematic view ~of yet~another ~Dmoodiment~of ~feasible mternal structure for a ' neuroplanner.

Figure 1 is a schematic:representation'of one~embodiment:of the architecture of the adaptive :,';
robot. In order to simplify the~ presen~ation, this ~Fi~gure relates to a 2-dimensional robot with a 2~
:: degree-of-freedom,~rovolute-~ointed:a~n,:It~should be'emphasized how~ever that this embodiment generalizes to 3-dimensional~robots and arms of:any number:of degrees-of-freedom. As seen in '~
Figure 1, the adaptive robot::lO~inc}ùdes a:pair of arm:links 11j 12,~which may be of arbitrary `~' : -shape, and:inciudes a "J" degree-of-frèedom mànipulator:13. A sensory pallet 14 is provided with '~
ènvironment sensors, namely stereo carneras,~:or range~finders: 15, 16, which is mounted on the ' robot body 10 at a point that provides:a clean line of sight to the end effector 17. The environment ' ' :

:
' 2 0 ~ :9 ~

sensors 15, 16, which may be~considered as being the eyes of the robot 10, can be turned independendy. Joints 18 and 19 each have~joint setting sensors 18a, l9a wh ich output the amount, and ~2, that the associated arm ~1inks ~11 and l2 are offset from their rest positions. The vector is referred to as a joint setting vector or arm~configuration. When the line of sight of each "eye" 15, 16 is~ centred on a workspace point 20, called a point of gaze, the eye motor displacement angles, Yl and ~r2, uniquely define the~direction~and distance of the point of gaze. A "D"-dimensional workspace will require a minimum of "D'i displacement angles to uniquely specify each point of gaze. The an~le ~Ir is referred to;as the eye~ang,le vector or eye config~urahon.
As seen~ in Figure l, ;the robot l O includes~ the control subsystems of the robot~ The task level controller ( I LC) 21 is a high-Ievel task planner thlat~ selects a target point and orients the "eyes" 15, 16~tow ;rd~it. ~Glancing at the target~point directs the arm ~11, 12 to ~ouch; it with the end-effector l7.
Targets~ are selected relative ~to the cuIrent~ sensory view rather than~by referring to an objective, umversal~coordinate systern. ;~e~(E~ æ~is a pr~tive controller~that onents the 'ieyes" 15, 16 towards the tip of the end-effector 17, whenever the (TLC) 21 is not otherwise determining the point of gaze. The (EET) 22 is~lgnorant of the hnematics of the arm 11, 12. It mérely tracks a moving light source or pattern ~that~is~ attJched~to the end-effector l7. During learning, the random movement generator ~RMG)~23~increments~each joint~setting ~ by srnall random amounts. If the arms~ 11, 12 are brought into contacs with ~ an obstacle,~ force sensors (not shown) prevent the alms 11, 12 from:being~ damJged ùntiI~ Its random movements freè ~it. The (EET) 22 will track the end-effector l7 as the arms l l, 12 move randomly through the workspace. The adaptive aTm controller ~AC) 24, receives joint setting~ feedbàck~from the~;arms 11,~12, eye angle feedback yf from the sensory pallet~14, and information régarding~the positions of objects in the workspace. Eye angle feedback;yr can;also be taken~from the (TLC)~ 21, rather than direcdy from the eye angle sensors 15, 16, allowing the (TLC)~ 21~to~propose~target points that the eyes 15, 16 are not currendy viewing.
Thus, as shown in Figure 1, the; (AAC); 24 receives~ eye angle vectors ~ r2 from the "eyes"
15, 16. The ~;(AAC) ~24 also receives joint setting vectors ~ 2 from the joint setdng sensors associated with the joints 1;8,~19,~and provides joint~setting feedback to the~ joints 18, 19. The (RMG) 23 provides jomt setting~feedback to the ~oints setting sensors associated with the joints 18, l9.,Th¢:(EET)22provideseye~anglefeedback~ 2to.the"eyes"15,16.Finally,,the(TLC)21 provides eye angle feedback~ 2 to the eyes ~15, 16.
Initially, the (AAC) 24 has~no~knowledge~of the~hnematics of the eye/armJ~ody combination it is associated with. During~ a preliminary~ training phase, it must learn to associate eye angle ~; vectors ~ 2 with one or mo~re target alm ~11, 12~ configurations that place the end-effector 17 within an alIowable distance from tho target point.
: :: ~ ~ ' ~: :: ' 19 ~
-:

:
, . . .
:: :': ' ~` 2 0 ~
, :
, :.-, This is shown in Figure 2(a), and Figure 2(b). Once training is complete and the (TI,C) 21 has selected a target, the (AAC) 24 must generate a sequence of joint system configuration vectors that move the arm 11, 12 from its current configuration to one of the target configurations. A joint system configuration vector~consists of "J" joirit settings that joint motors (not shown) are to assume. ~ Learned obstacle avoldance constraints must ~prevent the (AAC) 24 from generating undesired arm configurations~to avoid obstacle 25, e.g., the configuration shown in Figure 2(c). In shon, the controller must learn a~ "sense;of space" from its own sensory-motor experiences.

The (AAC)~conslsts of three layers of neural processors as shown in Figure 3: an obstacle network 31 ;~ an~ arm configuration network 32;~ and an eye configuration network 33. Network 31 represents regions of the~workspace~ where the~ arms 11, 12 cannot go. Network 32 represents ~ -where the arms 11, 12 currently~are ~and~where they~ can be; (the configuration of the arms 11, 12 determining the position of the end-effector ~17). Network 33 represents the place where the arms :~ -l l, 12 are intended to go (the targe~ yoint of ~gaze;that~hé end effector 17~ must touch).
The steps that move the end-èffector~ 17 to the point being looked at may~ be explained as follows.~ Each workspace region occupied`by an obstacle aetivates a processor in networ~ 31, the current arm configuratlon activates~one~processor in~network~32, and the~target point of gaze activates one processor in 33. Inter-network connec~ions associate regions of space in network 31 with arm~coDfigurations in~network~32, and eye~configurations in network 33 with arm ; ~ -configurations in network 32.~
Thus, m~ networl; 31, the obstacle~network, the~obstàcle data position is shown as inætive neurons 34,~ and~as ~active neurons 35. This~network 31 ~specifies where the arm cannot go. In network 32, the arm configuration network, the inactive neurons are shown as 36 and the winner neuron 37;is~representative of ~aIl the~settings ~, i.e., ~ . This shows where the arms are.
In network~33, tho eye~configuradon;network, the mætive~neurons are shown as 38, and the winner neuron 39 is representative of ~all the ~Ir, namely ~1.. ~. This shows where the arms are to go.~

Figure 4(a), and Figure 4~b) illustrate these ~connections in two parts to make the explanation clear. In practice, however, these two stages of processing occur in parallel. As shown in Figure 4(a), each eye configuration corresponds to one or more target arm configurations. Consequently, as shown in Figure 4(a), connections from the active processor 39 in network 32 cause one or ~, , -, - . ~ , ",:
,::

. !
" ' : ~, more~target configuration processors 35 in network 31 to be activated. Figure 4~b) shows that connections from active processors 39 in network 31 inhibit those processors 40 in network 32 that correspond to arm configurations intersecting obstacles (e.g., configuration l in Figure 2c). At this point, the levels of activation in network 32 represent the current arm configuration, the possible target arm configurations and those arm configurations which result in collisions with obstacles. ;~
If two important conditions hold, the final ~step of finding an arm movement sequence (not shown here), is a matter of finding a path through network 32, that does not include an inhibited processor 40, extending from~the current~ arm configurahon processor to any uninhibited target configuration processor. The~sequence~of~arm configurations assoc~ated with processors on this path~ will safely lead the arm l~l, l2 to the targeted point of gaze 20. Path finding is a well-understood computational problem and~many efficient sèrial and para11el algorithms already exist to find paths with desired characteristics~;and ;such algorithms are incorporated herein by reference.
For example, if the arm configuration ~network 32 is~ also a systolic array, a modified version of the alg:rithm may be used to find~a minimal path in O(~ln) time (where n is the numberiof processors m~the arm configuration network~32).
The two conditions necessary for path; finding are that: (1) processors 36, 37 in network 32 only correspond to arm configurations ~which are possible given the kinematics of the arm, and (2) processors~ 36,~ 37 which are neighbours~ in 32 correspond to arm configurations that are neighbours in the~ confi~uration ~spæe of the~arm 1 l, 12. The first condition is necessary in cases where the~arrn has restricted ranges of movement. The (AAC)~24 must not try to move the arm ll, 12 into~ impossible configura~ions. The~second condihon is necessary to ensure that a connected path of processors 36, 37 in network 32 corresponds~ to a smooth and continuous movement of the arm. The following disclosure now provides a cIoser iook at oach layer, (networks 31, 32, 33) and the laws~ that allow the (AAC) 24 to find the necessary inter-layer connections and to satisfy conditions forpathfinding. ; ~

The obstacle network (shown in Figure 5(a) and Figure Sb) is an egocentric image of the worksp,ace 50 of the robot lO centered on an arbitrary fixed point e.g., the joint 2 of the arms l l, 12. If a D-dimensional grid 53~1S superimposed onto the workspace 50 (as shown in Figure S(b)), each grid neuron 54 is the receptive field of a corresponding processor 34 in network 31. This processor 34 is active only if an object, e.g., the arm ll, 12 of the robot lO, the body of the robot 10, or an obstacle 25, falls into the recephve field 54. In a 2-dimensional workspace, the activation pattern of the obstacle network 3 l most naturally corresponds to a high-contrast, or back-lit image ::
- 21 ~
, :
, ' ~ , -- 2 ~

produced by a camera suspended above the robot 10 (as seen diagramrnatically in Figure 5(b)).
Equation (1) below describes the activation rule for these processors. Equations (2) and (3) describe the post training activation rules for processors 36, 38 in network 32 and network 33 respectively.
, ~ ' ' a(oi)t = 1, if object in receptive field (1 ) 0, otherwise tX(aj)t = 1, if ll~t- ~L(aj)tll = rl~in~q ll~t - ~L(aq)~ll "Winner" OR (2) cL(e~ t )(ek,ai) > 0 ,(and not Inhibited) "Target"
O, if tX(Oi)t ~(oi,aj) ~ O OR (x(b)t-~(b,nj) > O "Lr~hibited~ OR
otherwise "Inactive" ( 3 c~(eL~)t = l, if 119t- Il(ek)~ll.= rninvq 11~ (eqjtll O, otherwise: : :

The processors 36, 38 in the arm configuration network 32 and the eye configuration network 33, respectively of the (AAC) 24 receive configuration vectors as input from the robot 10. Each processor 36, 38 has a weight vector ~, ~, associated with its input lines. When an input is presented to the network, procéssors 36? 38 compete to "represent" it. The processor 36, 38 with the weight vector closest to the input vector in the input space wins the competition by becoming active 37,39. This behaviour quantizes the input space into Vornoi regions which represent sets of input vectors that activate each processor. The precision of the system is directly related to the resolution of this Vernoi tessellation, and can therefore be made as fine as desired by adding processors to the system.
These networks 31, 32,33 also have self-organizing properties that satisfy the two conditions described. Both conditions are necessary in network 32 to ensure error-free path finding. Any self-organizing laws that (1) cause the distribution of weight vectors in the input space to converge asymptotically on an approxirnation of the training data distribution, and (2) preserve the topology of the network in the input space may be used. The first condition ensures that weight ~, ~, are distributed over the same area of the configuration space as the training configurations. Since arm configurations that occur during training are by definition possible, processors on a path through network 32 (after training) will have weights that correspond to legal arm configurations. The second condition ensures that adjacent processors on a path will have weight vectors that are neighbours in configuration space and therefore represent only small changes in arm configuration.
Such a path represents smooth and continuous movements of the aIm. The path finding mechanism m network 32 must be capable of disnnguishing between the winning (current) configuration , .,~' - -, ~' :' 2 ~

processor (start of path), active target processor (end of path), inhibited configuration processor (not on path) and all other processors (possibly on path).
, .

The ilh processors in networks 31, 32, and 33 as~ processors i, a; and ei respectively, are shown in Figure 6. The activation values for these processors ~are denoted a(oi), tx(ai) and a(ei).
The input weight vectors from the joint~ setting sensors to processor ai are denoted ll(ai); the input weight vectors from the eye motor ang~le sensors to processor ei are denoted by ~ei). The inter-network connection weight~from processor ito aj is denoted (ek, aj). The special boundary processor b which occurs in equ~tion 2 has been described hereinabove.

Figure 7 dep~cts the neuroplanner interface 70 m schemahc form. It consists of a planning mode switch 71, four inputs 72-75,~ and;one output 76. The planning-mode switch 71 determines the mode to be used during the;planning stage; it indicates either a hypothetical initial condition or an actual initial condition. In the hypothetic21 mode, the initial targeting configuration signal 74 is used: otherwise, the initial system~configuration signal 73 ls~used. The environment data 72 is a quantized representation of thè environment~space.~Thè~maximum bandwidth of this channel is determined by the number of neurons in the representation. An "n"-dimensional space with "r" grid divisions per dimension would have rn neurons, for exarnple. The initial system configuration g ~ signal 73 consists of a system~configuration and an indication ~as to whether or not it is legal.
During learning, it is used as training data;~during~the planning stage, it is used as the initial system configuration when not in hypothètical mode (in that case, the legal indication is ignored). The initial targeting configuration ~slgnal is ~a targeting~ configuratioD. During learning, it is used as training~data; during the planDmg~stàgç, it is used as the initial targeting configuration when in the hypothetical mode. The final targeting configuration signal 75 is a targeting configuration that represents a goal. It is used only~ in the planning ~stage. Finally, the output plan 76 is the planned sequence of system configurations.
To be effective, the neuroplanner 70 must learn something about its domain of application. In particular, it must learn which configuratloDs are included in the system configuration space. For example, an automated vehicle would never have both the power and braking systems fully activated. To limit its choice of configurauons, the neuroplanner must learn where the boundaries ;; are for legal and illegal system configuratlons and what~the typical system configurations are.
Illegal states can occur for~two reasons: (1) they represent some impossible situation, e.g., a state beyond the physical lirn ts of a joint or a situation that is to be permanently avoided, e.g., a -, .

2 0 ~

meltdown region in a nuclear plant, or (2) they represent some situation that is illegal only in special circumstances, e.g., the front legs in a legged vehicle being prevented from being fully extended because they are resting on a hillock. Permanently iilegal configurations are associated with the system configuration space. Temporarily illegal configurations are associated with the environment space; they will be considered later.~

.
Figure 8 presents an intuitive 2-dimensional representation of a system configuration space 80 with~legal system configurations 81 ;and illegal system configurations 82. The system configuration space 80 in this case consists of 2-ve`ctors with x cornponents 83 and y components 84. In the automated vehicle example, they could~be braking pressure and~ fuel mJection rate respectively.

As seen in ~Figure 9, a plan ~is ~a path 84 passing ~through the legal system configurations 81 in the system configuration space~80.~Any~plan is acceptable~that successfully moves the system from the initial configuration 85 to~any one of the legal finaI configurations 86. ~ormally, a path 84 is selected that is~minimal~or~near-minimal. ~For the approach~to work, some mechanism is needed :temporarily to make illegal all of those:system configurations that violate the obstacle constraints.
:: It~ may not be convenient~to represent goals~by~listing :individual system configurations. Often;
there are many, possibly an infimte ~number, o final system configurations that satisfy a given goaL It is much simpler to represent a ~goal as~ a configuration in a targeting configuration space.
The targeting~ :configuration spac~e is~ defined by a~ set; of targeting configurations whose values are provided thr`ough targedng configurahon sensors. In fact, it is quite natural to thmk of the entire targeting configuration space as a goal space.
To~learn the`correlations between the tàrgeting:configuration space and system configuration space, the user of the system must ~ensure that the targeting configuration sens:ors can be aligned with an; observable that results from each system configuration. This alignment provides the correlations between the targehng configuration~and~system configuration spaces during learning.
During the planning phase, tho goal is specified~ with~ a targeting configuration; the neuroplanner can then find final system configurations that are correlated with the targeting configuration for use in the planning process.
Distinct system configurations can of ~ course be associated with the same targeting configuration; i~.e., there is a one-to-many mapping from the targeting configuration space to the system configuration space. Since illegal goals are not needed, it is not necessary to distinguish between legal and illegal targeting`configurations.

~. .: .

~" 2 ~
.; ~ . , ~ , j , . .. ..
. .
.
Additional constraints are often imposed on the planning process. Sensory information regarding obstacles that interfere with the planning process further constrains the shapes of valid plans. One must plan to navigate around obstacles, not through them. Observables that are illegal or undesirable are referred to as obstacles. Obs~acles may represent either physical objects or abstract situations to be avoided. For example, an autopilot is constrained to avoid situations where the state of a vehicle leads to an~ observable collision with another vehicle. Similarly, a nuclear power plant might be constrained to ~avoid observable excesses of heat, radiation, and power o utput that can be controlled by its~darnpmg rods. ~
The environment space;~is a sensory space that dynamically tracks the observable state of the domain. ObstacIes and the~observablé~result of the system configuration appear in the environment space as~ regions of points. These are referred to~ as the obstacle regions and the observable result regions~respectively. For~planning purposes, the~controlled system needs to be constrained to avoid obstacles. In the environmont~space, this corresponds to preventing obstacle regions from intersecting~with the observable~résult~regions. In~order to do this efficiently, it is sufficient to correlate the points in the observable result regions with their associated system cor~lguration.
Later, when~obstacles are observed, it~ls~not necessary to;mterséct these with the observable result regions. Instead, it is~sufficient;to~insure~that~the~plan solution~does not include system c onfigurations associated with o~stacle region points. This is achieved by correlating the systern configulahoDs with their~own ~observable~result region points during a learning phase.

Figure io illustratos ~schematically~the ~nternal structure of a controller 100 including a neuroplanner 70 used for non-hypothetical reasoning. The controller includes the (RMG) 23 and the (EET) 22.
Thus, Figure lO Aepicts~the~robot system lO and associated controller lO0. The neuroplanner system configuration space consists of joint setting vectors ~also called arm configurations) while the ~targeting configuration space consists of eye angle vectors (or eye configurations).
As seen in Figure 10 a neuroplanner ;70~1S used to design a controller for a 2-degree of freedom robot manipulator that finds collision-free arm movements in a workspace cluttered by an arbitrary set of obstacles. The controller l00 makes use of vision sensors l5j 16 to adapt to the specific eye/hand and armlbody kinematics of any~ arbitrarily shaped robot 10 during an initial, unsupervised training phase. After tra'ning, the~robot selects a target point by "glancing" at it and the controller 100 moves the~end-effector into position.
:
. . .
- .:

:
- 25 - ; ~ ~

~; -2 ~

~ , The end-effector tracker (EET) 22 and the random movement generator (RMG) 23 provide facilities for unsupervised learning of correlations between targeting conf~guration and system configuration spaces. The (EET) 22 orients the eyes toward the tip of the end-effector 17 as the (R~lG) 23 randomly moves the~arms ll, 12 during correlation training. This arrangement guarantees that the targeting configuration sensors are aligned with an observable, namely the position of the end-effector tip 17. This observable is neither a system configuration nor a targeting configuration. To ensure that the;~oint~limits are not exceeded, complementary system configurations are provided~for each~legal system configuration that is at a joint limit. A
co:mplementary configuration is an~ illegal system~ configuration that is an epsilon amount beyond the legal range of the joint which is at its ~limit.

The configuration of the~robot~10 u~ its obstacles 82~is shown in Figure l l(a).
As seen in ~igure 11(b) the e!lvironment space 110 is a high-contrast image 111 produced by a camera suspended above the robot workspace 28. The receptive field of each neuron in the environment network corresponds~to a neuron in the~digitized image. The neuron is active if any oùject (obsttcl- or ~bot) is tn its recepdvc~field. ~

~ The results of a typical training~run are shown;in Figure 12(a), Figure 12(b) and Figure 12(c).
The simulation is written in ParcPlace~ Smalltalk-80 and runs on a Sun workstation. (ParcPlace SmalltaLc is a trademark of ParcPlace Systems Inc.) Figure 12(a) depicts the four simulation window~ views~ (numbered 1 through 4). View 1 is a representat~on of the robot 10 and~its~workspace 28. The eyes I5~ 16 are represented as two x's on t he firont of the body (rectangular in this case).~The dotted lines extending from the eyes 15, 16 to the tip 17 of the arm represent the lmes-of-sight of each eye. View 2 represents the pattern of ~P~ ~ ; environment network activations. View 3~ depicts ~the targeting configuration space and network.
View 4 represents ~the system configuration network. The horizontal and~ vertical axes represent the arm configuration joint settings in the range O~to 2~1. System configuration network weights are shownImapped into *e space~by connecting neighbouring weights by line segments.iThe sequence of windows in Figure 12(a), in~Figur~e 12(b) and in Figure 12(c) show the weight vectors at 0 iterations, 500 iterations and 60,000 iterations :rPspecdvely. The network weights gradually assume the same distribution as the training data. Each~ dot in view~ 1 represents the end-effector position of a training sample. Inter-network connections are not shown.

, - ~, `' `'~,'~"','"`" ..
:

Figure 13(a), Figure 13(b) and Figure 13(c) show the movement sequence generated for a target point of gaze. Obstacles 25 have now been introduced into the workspace 28. The bottom (views 3 and 4) have been replaced with representations of the network activations. Each neuron is represented by the distance of its weight vector to the input vector. Large dots represent proximal welghts, small dots represent distant weights. ~The neurons which represent the current system configuration and targeting configurations~ :re marked with X's, target neurons are diamonds, inhibited neurons are shaded grey~and neurons;on the path are black rectangles.
In Figure 13(a), the robot l0~has directed the eyes ~15,~16 to target point of gaze 17. Neurons represenhng the current eye and~arm~configurations are acdvated. Figure 13(b) shows the system c onfiguration network at the next instant in~ time. The~winning targeting neuron has activated a set of~ 16~target arm configurations~in the~ system;~configuration network (view *. These arm configurations have also been drawn in~the workspace for clarity (view` 1). Shaded neurons in the system configuration network have~been inhibited by active neurons in the environment network Ten target configurations have~been~ inhibited ~in~the~ system configuration network. These configurations~intersect the two~obstacles near the~point of gaze. Figure 13(c) shows one of the many fflovement paths possibIe.~ The path generated here illustrates that paths may wrap around joint~ setting space if joints have~unrestricted mo~ement. The path begins at the current arm configuration (nght side of view 4), exténds up to 27~; and then continues from 0 (bottom of view) to a target configuration. The sequence ~of arm moves generated by this path are shown in view 1.
Notice that the end-effector link swings up and then down to avoid collisions with obstacles as it moves to touch the target point ~
- In the learning phase, two stages aré used:~ (1) the topology leaTning stage where Kohonen's self-organizing algorithm is independently applied~to the system configuration and targeting configuration networks, and (2); the correlation stage which correlates both the environment and targeting configuratlon networks with the~system configuration network as follows. As the system configuration space is sampled, a set of environment configurations and a targeting configuration is associated with the corresponding~system configuration by growing connections from the environment and targeting neurons to the system configuration neuron. An environment neuron, is intended to mean an active neuron in the environment network. Connections need not be grown if they already exist. Because all three networks~are quantizations of the spaces they represent, distinct configurations might be quantized to the same value. The environment and targeting infonnation associated with a specific system configuration is arbitrary. The same environment and targeting data might later become associated with a different system configuration. Once the s ~, ~
2 0 ~ ~ 0 9 3 , ", correlation stage is over, a specific environment configuration is correlated with an arbitrary number of system configurations. Similarly, a targeting configuration is correlated with some arbitrary number of system configurations. In other words, there is a one-to-many mapping from the environment and targeting configuration networks to the system configuration network.
The operation of the neuroplanner m non-hypothetical mode is now summarized, i.e., in the mode where the current system configuration is a meaningfuI starting point for solutions to the targedng configuration goaL The hypothetical mode is~used~when the iniial system configuration is unknown but an initial targeting~configuration is available instead. Given an initial and goal targedng configuranon, the;neuroplanner finds a~sequence of system configurations leading from any~one of the systern configurations associated~with~the initial ~targeting configuration to any one of the system configurations associated with the go~ targetmg eonfiguration.
Once the se f-organization phase is complete, a second phase proceeds to learn the boundaries of tho legal system configurahon regions and the correlations between the environment space and the system configuration spaco ~ The boundaries are delineated by the i~egal system configurations provided when the joints reach~théir~limits of ~rnovement as described previous!y. The illegal system configurations prevent the;path planner frorn~planning paths that cross illegal configuration règions. During learning,~ the observable result region as~ described previously is the image of the arm. Consequently; each active neuron in~ this~ region is coIrelated with its associated arm configuration~ (system oonfiguradon)~ nèuron. Later, when obstacles are introduced during the planning~phase, the neurons representing regions~of the workspace occupied by obstacles will ~hibit system neurons that represent arm- configuradons that intersect any one of these obstacles.

As ~seen in~Figure 14, in the ~planning phase the cuIrent system configuration 1434 activates one neuron 1436 in the system configurahon; network l432 using the standard Kohonen algorithm.
Simil~arly,~the targeting configuration~neuron~l436~activates a neuron 1437 in the targeting configuration network 1433. Correlated with~ each neuron 1437 in the targedng configuration netwo~rk 1433 is a set of neurons 1436 in the~systém configuration network 1432. Correlated with each~ neuron 1437 in the ~targeting configuration~ netwo* 1433 is a set of neurons 1436 in the systeml configuràtion network 1432. The activated neuron 1437 in the targeting configuration network 1433 causes these correlated system configuration neurons 1436 to be activated. At the same time, the environment network 1431 has.a number (usually fairly large) of neurons 1438 that are activated, these are environment neurons. These neurons 1438 deactivate the correlated system configuration neurons 1439 in the central network 1432 preventing them from being activated.
Only`two time steps are needed to accomplish the above since most of the activity occurs in , .,'~ ., .:
: .
... ....

2 ~ 1 9 ~ ~ ~

paralld. A standard path planning~algorithm~can then~bo used to find a minimal path from the current system configuration~neuron 1436~to~anyone of the active goal system configuration ~neurons 1439. If each neuron is a processor in~ a systolic array, a parallel algo~ithm similar to the algonthm~described could be used to find~a ~minimal path in O(~ln) time where n is the number of neurons in the network. The system configurations a~ssociated with the neurons in the path can then be used~to construct the c=orresponding~plan. Actually,~the~system~configurations are not stored with the neurons but they can~bé~reconstructed from the weights stored in the Kohonen map.
During the planning stage,~the~eyes 15, 16~ rgeting configuration sensors) are forced to gaze at the goal point.~Subsequently,~each ~workspace region;occupiéd by~ an obstacle 82 activates a neùron in-the~environment~net~k 1421, the~current~arm;configuration activates a neuron in the systemconfig tionne ~3~à dt t~t ' ~nonsensor 1435activateaneuon in~e~targRing~conf;guration~net~ 143~3.~Each'e~configuration~(targeting~configuration 1435) corr~esponds to one~or more targèt'arm con~fi~s ~system~configurations~1434~. herefore t e active targeting configuration~:DetDon~ 1437~will ;àctivate~ a ,set: of final ;system configuration neu~rons.
Similarly, each active environrnent neuron;~`l438~deactivates any active system configuration neuroDsl436.~1remainngact ~a duni ibited~n onsae dates~t collisi n-ree ar' ~movement plan.~The~pia -is~` ound~by~a;stàndard pat ~finding~ algor m as described ~ lanDers~can be irnplemented~using~a~simple,~non-adaptive, voxel re sent ion of the w~ s~ ee ~Dte ts (ie a p~elated~e~d~eriv~from a`di~tal camera, or compute ~m a stereo depth~map)~for the environmènt'spacè~and~two Kohonen maps for the system configuration and targeting~co~figuration~ spaces~ as~shown in;~Figure~ 14.~Each~network is implemented with a netw'ork of ~netlroDs that eftectivdy~quantize the ~space. Obstacle regions and self-image regions in the~environment~network arè~repres~énted~by neurons in the active state; all others are~inactive Figure~14 actually~ illustrates~the~internal structure of a neuroplanner used for non-hypothehcal ~ re~ complex bulldmg blocks~might be used for; archltectural-level design of neural mechan sms l~e present invention~ also~pr:~ es a preliminary design for such a building block by (1) postuiadng the require,mentS that lt would ~have ~to satlsfy, (2) ~suggès~ng an :implementation that would satisfy those requirements,~ and~(3)~providing~ an~example of its use. The present invention provides a class of buildmg blocks ~that~can be used for subcognitlve planning and control.
A preliminaly design has~ also been~ provided~ for :a combined neural/systolic building block for usé in construcdng subcognitive controllers, These~ controllers have two important properties: (1) the ability to learn the topology of threé~continuous`spaces; a targedng configuradon space, a ::: ` ~ :
- ~:

:: :

2 ~
- ";, ,",,, system configuration space, and an environment space, and (2) the ability to integrate the three spaces so that initial and goal targeting configuration conditions can suggest a sequence of system configurations that lead the controlled system to the goal in the presence of obstacles. The result is a planner or guidance system that can be used for such subcognitive tasks as robot manipulator control, head/eye coordination, and task sequencing.

The operation of the neuroplanner m non-hypothetical mode; i.e., in the mode where the current system configuration is a meaningful starhng point for solutions to the targeting conf1guration goal is now summarized with roference to Figures 15 and 16. The hypothetical mode is used when the initial system configuration is unknown but an initial targeting configuration is available instead. Given an initial system configuration and goal targeting configuration, the neuroplanner finds a soquence~of system~ configurations leading from any one ~of the system configurations associated with the initial targeting configuration to any one of the system configurations associated with the goal targeting configuration. The problem rnight colloquially be described~ as the "look at your finger" problem; assuming that the arm has been moved to some arbitrary position in a workspace, the goal is to have the eyes/head/neck (EHN~ system 1500 look at~the tip of the arm. The robot is assumed to be able to cont~ol its arm. This embodirnent of this invention is~to provide the robot with the additional capability of controlling the EHN system. As depicted, the system has more degr~ees of freedom than necessary (if no obstacles are allowed). In particular, the eyes 1515, 1516 can rotate, the head 1510 can rotate, and the neck 1519 can rotate and stretchlshrink. There is a limit to~ the movement permiKed by each component; 45 for the eyes, 30 for the head, 20 for the neek, and a neck length of ~between one and two head widths.
The EHN system 1500~is separate from the arm system and has access only to a vector which represents an arrn configuration. The meaning of this~vector is unknown to the EHN system 1500, i.e., whether or not it consists of joint settings, polar coordinates, or Cartesian coordinates of the arrn tip is not germane. However, the system 1500 will have to learn the associations between arm configurations and the actual position of the tip of the arm.
, ~ . . :, To complicate the problem, obstacles 1525 are disposed in the way so that the EHN system 1500i may have to "look around" the obstacles that are blocking the view as seen in hgure 16. If it ~ -is not possible for the E~ system~ 1500 to look at the tip 17 of the arms 11, 12, no movement is ,, initiated. Otherwise, the EHN system 1500 looks around whatever obstacles 1525 are blocking the view. The EHN system 1500 includes the dotted lines from the eyes to the point 17 of focus. To have a clear line of sight, obstacles must not intersect either of these lines. ` ~ ~-,.''~,;' '''' '.,.' ' . ,:-. :, '- ,,:
~' ' . , ~

; ,' :.., 2 0 ~ 9 ~

,, ~
,.
' The EHN system 1500 goes through a~ learning phase without obstacles where it learns two things: associations between (l) EHN~configurations znd arrn configurations, and (2) obstacle avoidance constraints. After learning, obstacles l525~can~be added~arbitrarily and the E~ system 1500 will endeavour to look at the hp~of the~arm where possible. Changing the obstacles does not require ~any additional learning. In fact,~ if the system is~ fast enough, it should be possible to track the tip of the a~n as the obstacles~change dynamically. ~
The neuroplanner system configura~ion space;~consists ~of EHN configurations while the targeting configuration ~space consists ~of~ ~arm;configurations. The arm~is used to steer the EHN
system~1 500.~The arm~ configurat,ion can~ be~'used to~ find~ an EHN configuration that focuses on the t 'p of the~a -m~t e~presence of obstacies. In~general~ ~h env-ronment space is a 2-d pixelated network, e.g., as might~be produ~b~y`'~a'~vision~system. The self-image of the EHl~ system lSOO
includes ~the 'workspace ;area taken up~by~ the~neck,~ head,~eyes, ~and lmes of sight up to the point of focus. ~The environment network~ l5 11~ can ~bè~ prodùced~by~ a high-contrast image ~lS 12 (see Figure 17(a) and Figure 17(b)) obtainable~e.g., from a camera suspended above the robot workspace.
Once~the~self-organiza~ion~phase~is complete,~he~second phase proceeds to loarn the boundaries:of~t~he legal~system~configùr~ation~region~s,~the correlation between the environment space~and~the~system configu~ion space,~and thè correlation~between the targeting cor~lguration space~and~the systém~configurati~on space.~No obstacles~are presént during the learning phase. The system~must~learn ~hat obstacles'~oannot-Intèrsec~;wlth the~self-image. This is achieved by associating;each neuron~in a workspa~ce (environment network3 self-image (environment neurons) wlth the~corresponding~system configuration neuron. Aftér learning is complete, actual obstacles ; ~ may ~be ~added to the workspace.~Each 'neuron; which is~ac~ivated by~such an obstacle inhibits all EHM~configurations associated~with self-~rnages that~contained ~that neuron duIing leaming.
The boundaries~ ware delinea~ted by the illegal~system configurations provided when the components of the E~configuratio}~s~reach their limits of ~movement as described previously The illegal~ system configurations ~prevent t ~he;~path planner from planmng paths that cross illegal regions.
When learning the correlation~be~ween the environment space and the system configuration space, '`each active neuron in the self-image~ is correlated with its associated EHN configuration (system configuration) neuron.~I~ater, when obstacles are~introduced during the planning phase, the~neurons in the obstacle regions will lnhlbit~EHN configurations that intersect any one of these obstacles. This~ prevents the EEIN system from~colliding with obstacles or the arm as it moves to k eep the tip of the end-effector in sight. ' ~

: ~ : . , .

' :~ : ;: ', :
~; ~ : , -,, 2 ~
",~
. .
,... .. .
When learning the correlation between the targeting confguration space and the system configuration space, the EHN system is randomly moved around. The the arm is then adjusted so that the tip can be seen by the focal centre of the two eyes (where possible). In that case, the ~; association is made from the targeting configuration space to the system configuration space (the arm to the eyes).
;~ During ~the planning stage, the~ arm is~ moved to some arbitrary point. Subsequently, each ~; workspace region occupied by an obstacle activates a neuron in the environment network, the cuTrent EHN configuration activates a neuron~ in the system configuration network, and the current arm configuration activates a ~neuron in the targeting configuration network. Each arm configuration ~(targeting configuration) corresponds to~one or more target EHN configurations (system configurations) that can see it.~ Therefore, the active targeting configuration neuron will activate a set of final~system~configuration neurons. Similarly, each active environment neuron deactivates~ any active system configuration neurons that are in collision situations.
;; All remaining active and umnhibited ~neurons ~are ~candidates for the collision-free'EHN system movement plan. The plan is found by a standard path finding algorithm as described previously. In this way, knowledge about all~possible solution movements is represented simultaneously by ur~inhibited neurons.
Neuroplanners according to this~embodiment of the present invention possess four important characterisocs. First, they are applicable to~ a wide variety ~of domains. Second, they are capable of self-organization and learning. Third, they~ lend themselves to implementations in massively parallel hardware and are therefore~potentially very fast. Fourth, goals are specifiable at a higher 1 evel than the system configurations~of the system; this includes the ability to specify large sets of :: ~ final system con~lgurations from a single~target nû configurahon.
The EE~ system of this embodiment of this invention is unique in several respects. It will adjust ttself with minimal movement to~keep the tlp of the arrn in view (where possible) regardless of the obstacle arrangement. In particular, it will avoid colliding with obstacles or the arm.
Changing the obstacles will simply~cause the EHN system to re-ad]ust its position to maintain its view of the arm tip. This occurs without~ any additional learning. Although the behaviour is seerningly intelligent (as seen'from an outside observer), it nevertheless arises from a simple interaction arnong neurons in an appropriately organized network architecture.
The present invention in yet another ernbodiment provides a control system for finding target-directed collision-*ee robot alm trajeclories in an obstacle cluttered workspace. By target directed, is meant that the arm could position its end-effector at the point of gaze specified by a pair of stereo targeting cameras. Hence the system is able to "touch the point targeted by its eyes".
, - 32- ~

~ ~: 2~9~
.

' '',"
. . ...
The design described below according to this embodiment of this invention extends the neuroplanner of a first embodimene of this invention to control a more complex targeting system, namely an articulated platform containing a pair of stereo cameras as seen in Figure 18(a) and Figure18~b)andFigure19(a)andFigure19(b)~(whichusethesamereferencenumeralsinFigure 15).~ The extended neuroplanner c~ontinues~ to handle the simpler arm control problem and is additionally~ designed to solve the inverse ~problem,; narnely given the current configuration of the arm, the system can re-orient the platform to focus the cameras on the end^effector. Because of obstacles, the camera platfor~m~will generally~have to peer around obstacles that block its view.
Hence the new system is able tci "move~the eye/head/neck system to see the hand".

T he~ neuroplanner is based on~ the design shown in Figure 20. The environment network is a 3-d representation of the robot's~workspace, it~indicates which regions are occupied by the objects in the~workspace. This information can ~be extracted from data collected by a vision system, e.g., from a stereo depth map. ~For~the simpier 2^d situation,~it is the direct output of a high contrast digital camera where the back~d~is white and obstacles~are~black.
;The space of arm configurations is ~referred to as the arm~ space. The arm network is a representation~ of this space. Sirnilarly~ the space of camera platform configurations, including the joint settings for the articulated platform as ~well as the joint angles for ;the cameras themselves, is referred to as the eye space. The eye~netwo* is a representation of this eye configuration space.
For~ brevity camera platform configurations will~ be referred to as eye configurations~
As previously described, each network 2031, ~2032 and~2033 is implemented with a network of neurons ~2036, 2037, 2038 and 2039 that quantize thè corresponding space. The environment network lS a quantized representation~of the workspace wlth a fixed topology while the arm and eye networks are both self^organiz;ing Kohonen maps. The neurons in each netw~ork can be in one of three states: inactive, active or inhibited.
In the planning phase of the original neuroplanner of the first embodiment of this invention, the current arm configuration activates one neuron~in the arm network using the standard Kohonen algorithm~ As previously described the current eye configuration activates one neuron in the arm network using the standard Kohonen algorithm. Similarly, the current eye configuration activates a neuron in the eye network. Correlated with each neuron in the eye network is a set of neurons in the arm network. These represent possible goal arm configurations that place the end^effector at the point selected by the cameras. The ætivated neuron in the eye network causes these correlated arm :
- 33 ^

i "~` ` 2 0 ~ ~ 0 ~ D

configuration neurons to be activated. At the same time, a subset of the environment neurons are activated; these represent regions of the workspace occupied by objects. These environment neurons deactivate correlated arrn neurons - thereby elirninating arm configurations that intersect workspace objects from solution arm trajectories. ~
Only two hme steps are needed to accomplish the above~ since most of the activity occurs in parallel. A standard path planning algonthm can; then be used to find a minimal path from the current arm configuration neuron to any one of the goal~arm configuration neurons. If each neuron is a~processor in a systolic array, a parallel algorithm could be used to find a minimal path in O(~ln) time where n is the number of neurons in the network. The configurations associated with ehe neurons ~m the~ path can then be~used~ to~ construct~;~the corresponding plan. Actually, the configurations; are not stored with ~the~neurons~ but they can be reconstructed from the weights stored in ehe~Kohonen map.
In ehe original neuroplanner of the~firse embodimene of this invention, connections from the environment network to the arm network~;were used in ehe reverse direction to inhibit the environment neurons that intersect ~with the current posihon of the arrn in the workspace. These neurons are referred eo as the arm's self-image. This step is required to keep the environment network from ~regarding ehe arm~ as~ an obseacle; i.e., eo'ensure ehat the current configuration or any c onfiguraeion~that ineersects~ with thé current one are not deactivated by the environment network. If this were ehe casej ehe arm would~be unable~eo move away from ies current position.

In ehe system of a further embodiment of'this invention shown in Figure 21, this notion is made explicit by separating the Wdirectional conneceions into self-image connections from the arm network 2131 to ehe environment ~ne~work 2132 and obstacle conneceions from the environment network to ehe arm network. It also~shows~the eye network 2133.
In the learning phase, no obstacies~ are pérmitted in the workspace, and two stages are used~
(I) the topology learning stage where Kohonen's self-organizing algorithm is independently applied to the arm network 2132 and the eye network 2133 and (2) the correlation stage which correlates both the environment network 213 1~ and the eye network 21 33 with the arrn network 2132 as follows.
As arm configurations are sampled, two actions are performed in parallel. First, a set of active environment neurons are associated with the corresponding active arm neuron 2139 by growing connections in both directions between environment and arm neurons. These connections are the self-image and obstacle connections described above. Second, the active eye neuron 2139 is ''' ' '"; ' ' ~ 34~

, , associated with the corresponding~active arm neuron 2139 by growing a connection from the eye neuron to the ~arrn neuron. Connectlons~ need not be grown if they already exist.
Because all three networks are~quanhzations~of~the syaces they represent, different configurations might be represented ~by the same neuron.~ Once the correlation stage is over, a specific environment neuron ~will~have been correlated~ with an arbitrary number of arm neurons.
Similarly, an eye~ neuron will~ have been~ correlated~with an arbitrary number of arm neuron.s. In other words,~there is a one-to-many~mapping from~the environment and eye networks to the arrn ; network.
e neùroplanner according to this~further embodiment of this invention can now be extended to p~i~path~planning~in tho eye space. This; allowsithe system to move the articulated camera platform~into a; goal position~where It can see the end-effector~of the robot. To do thisj it must find an;eye~t~ajecto~;th~at~avoids~collid~ith~obstacles.~;Moreover,thegoalpositionmusthaveaclear ~e-of-sight to the ~end-effector,``no;objéct~must intèrsect~with the two virtual rays extending from the;cameras to the end-effector.~
Two notions are needed to deal with this extension:ithe eye seLf-image analogous to the aIm ; self-image and~a~new notion,`the~lines-of-sight'~self-image, which is the set of neurons in the enYironmènt no~vork that are~on the~path~from~the camera~lenses to the foveating~point.
The environment network is~extended~ so that obst ~cle~connections can be made to both the arm and~eye~spaces. Additionally~,~line-of-sight~connections are added to correlate envIronment neurons ~on~the line-of-sight~ for ~a 'particular~eye configuratlon with tho corresponding eye ;configuration neuron. Connectlons ~exist in the~ other direction,~from the~arm network to the environment~network~to corrdate~an'arm~conf~iguration neuron~with arm s~elf-image~neurons Similar `connections ~are ~added~from the eye' network to the` énvironment network to correlate eye co'nfiguration neurons to:eye self-imag'e:~neurons.~
Path~planning in the eye~sp~ace~is~;exac:tly~analoj,ous~ to~path planning in the arm space.
Conceptually,~it is sufficient to~interchango the;roles~of the~arm and eye spaces. The current arm configuration~is used to activate~éye configurations. The~current eye configuration is used to deactive the eye self-image neurons in the environment network. The environment network `eliminates illegal eye'configurations -~where an obstacles intersects the camera platform or blocks either of the ~lines-of-sight. ~
In more detail, the eye network~path' planner's~task is to find a path from the current eye configuration~neuron to a goal ~neuron~through~uninhibited neurons. A goal neuron is an eye neuron that corresponds to an eye configuration in~which the cameras are focused on the end-, : : ~ :,: , :: ~
:

2 0 1 ~ ~ 9 ~
.. . - . ,, ~,:

effector. Clearly, the platform cannot intersect an obstacle nor have ItS lines-of-sight blocked by one.
It first must deterrnine potential goal eye neurons. The earlier system of previous embodiments of this invention already~provides connections between the eye and arm networks which correlate eye and arm configuration neurons whèn the eye foveation point is the same as the arm end-effector position. In;~the~extended system, ~connections are made both ways duIing learning. By following these connections from the current arm configuration neuron to the eye network, a set ~of eye ;neurons;is obtained~that represent configurations of the camera and platform that focus on the end-effector.; ~
It must~also ensure~that the~ current eye~configuratio~ and those whose self-image intersects `with the~current one are not inhibited.~Otherwise, movement away from the current position would be prohibited. Since ~every eye~configuration neuron is correlated with its self image neurons in the environment~space, it is sufficient~to deactivate ~these. The current configuration is no longer viewed as an obstacle by the environment;~network.
Addi~tionally, neurons that~collide with obstacles are~not permitted in the solution path. Nor are go ~1 neurons whose the lines-of-sight~are~blocked by~obstacles~ Note that intermediate neurons in ~the solution can have the~ lines-of~sight blocked, the cameras can foveate anywhere while being moved.~ Eliminating neurons that~collide wlth obstacles is achieved by deactivating any neuron that receives obstacle connection signals from active neurons in the environment network. The line-of-sight constraint ¢an be satisfied by:deacdvating potennal goal eye neurons that receive lines-of-sight connection signals from~ active neurons in the environment network.
The following; summarizes the~ above embodiment of the present invention in an algorithmic fashion.
The system is trained without obstacles in;two stages ~ ~
1. Generate a representative set of arm and eye~ configurations by randomly rnoving the arm ; and articulated camera platform. In~each~case,~orient the cameras to focus on the end-effector and use the associated configurations as training data for the Kohonen algorithm to self-organize the arm and!eye~networks.
2.Afterself-organization,~generaEea~newrepresentatlvesetofarmandeyeconfigurationsas above. In each case, using the Kohonen algorithm, determine the corresponding arm and eye neuwns a and e respectively and perform the following~
(a) For each environment neuron o in the arm self-image, and i) grow a connection from o to a and from a to o.

~ ' ,-' '' :
~; (b) For each environment neuron o in the eye self-image, i) grow an obstacle connecdon from ` o to e, and ii) grow a connecdon from e to~ o.;
(c) For each envwnment ;neuron~o In ~he lines-of-sight self-image, and i) grow a lines-of-sight connection from o to e.
(d) If o or e is at the limit of movement,~connect the permanently illegal configuradons neuron to the~ corresponding arrn or eye; neuron.
The system plans~ in the arm space as follows ~(arb-t~a y obstacles allowed)~
1, All neurons are placed in the inactive state, the ~articulated cameras are oriented to target, and the environment network is initialized ~(areas occupied by objects are active; otherwise 2.~;The~arm~and~eye neurons ~corresponding to the~current arm and eye configurations are determined by Kohonen competition.
3. The cuIrent arm ne ron inhibits the~envir:onment neurons it is connected to (its seLf-image).
4 . ~SImultaneously,~ the~following~occurs (a)~ the active~ eye neuron activates a set of arm neurons (potential goal neurons);~(b) theiactive~environment neurons inhibit all connected arm neurons ;(arm corifiguradons that intersect obstacles).
S.~ The~path ~planner finds a min~al p~a~ from~ the cmrent arm neuron to any one of the acdve goa neurons~that includes only inactive (not inhibited) neurons.
6. T~he weights of ~these neurons~represent~the~solution arm trajectory.
The~ system plans in the eye spacé as~follows (arbitra~y obstacles allowed):
1. All neurons are placed~in~ the~inactive state, the ~arm is placed in some arbitrary configuration~ and the environment network is imtialized (areas occupied by objects are active;
otherwise~inacti~ve).
2. The~arm and eye neurons~corresponding;to;the current arm and eye configurations are deterrnined by Kohonen competi,tion.
3. The current eye neuron inhibits the environment neurons it is connected to (its self`-image).
"
4. Simultaneously, the following occursr ~(a) the active arm neuron activates a set of eye neurons (potential goal neurons); ~(b) the active environment neurons inhibit all obstacle-connected ~- ; eye neurons (eye configurations~thàt intersect obstacles); (cj the active environment neurons inhibit all line-of-sight connected~eye néurons that are potential goals (goal eye configurations that cannot see the end-effector).

~ ~ 37 ~:: : : : :. -. :.: .,:
: .
, , i"''' '! ' '' " ~ ;' ' ~ ; ' 2 ~ ~ ~ 0 ~

, ;,...........
5. ~e path planner finds a mimmal path from the~currènt eye neuron to any one of the active goal neurons that includes only inactive (not inhibited~ neurons. , 6. ~e weights of these neurons represent the solution ~eye trajectory.
The present mvent~on in yet ano~er embo~ment provides another neural architecture which has the following desirable properties. First, ~the approach~is in principle capable of autonomously learning~the kinematics and workspace constraints~for any~general ~robot manipulator dispensing with~the~nee ~for speclalized~ inematic prog am ng.~Second, t o system autonomously learns hand-eye- co-ordin:ation, pro~ding~a natural~form ~of task~level control in which a target point is seIected by~"glancing" at it with visual sensors. Third, the system learns to produce collision-free - ~- ;
movements~of~thé~arm. Four~, the~obstacle~sets can~be~changed without re-learning. Fifth, the robot~can be~ moved~without ~u~n~ ~eit~ reorganization or relea n~g. Sixth, more than one a~n co~l~hon ls proposed for a given ~ ~
The new system per~ts~ a~more complex ~artic~at~camera ~orient~g~system ~d also perrnits operation in two~distinct modes~ touch mode - where~an~arm trajectory ~at wi~ Ming the end- , ;',"`~ i,, ,', ''", effector to~ the~focus point of ~the~carneia~ system~is ~dete~ned; i.e., "touch what you see" or (2) ;
look~rnode - wh~e~a camera o endng~systom rajecto~t at~will~p~it the systemto have a clear line of sigh to the end-effector is détèr~ned;~i.e., "look at your hand't.~
The present invention thus~has~potential~application ~more general domains~ For example, in ~e pro`cess-control; domain, the~ environrnent ~space might~ be~generalized to encode operational sensor data. Obstacles represent mvalid, dangerous or undesi àble operahona pa a et . The a network~repreisents~the state~of~the system;;~e.g.,; valve~setdngs. The eye network is a targeting con~lguration space that can be uséd to~specify~the~goal of the system in more abstract terms; e.g., -a specified ratio of chernical A to chemical ~B~ ~or ~a~ préferrèd product viscosity.
By this embodiment~ of this~invention~the previously described architecture (a;neuroplanner) is - ~ ~
now~ ger err~lized to an extension~of~the~problern~it was initially ~designed to solve, namely the target- ~ ~ J
direct~ed control of a robot arm in an~obstacle cluttered~workspace. By target directed, is meant that the~arm can position its end-effector at the point of ~gaze specifled by a pair of stereo targeting camera,sl. Hence the system is~able to~"touch the:pomt targeted by its eyeS". Thè new design of this ~1., ~ ,, embodlment of this invention extends ~the targeting system to; an articulated camera platform, the `equivalent~of the human eye-head-neck~system.~This~permits the robot to solve the inverse problem:~given the current configuranon of ~the arm, the~system is able to re-orient the camera platfo~n to focus on the end effector. Because of obstæles, the camera platform will generally have to peer around obstacles ~that block its view.~ Hence, the new system is able to "move the eye- ~ -head-rleck system to see the hand".~

38 ~ ;
:: ~ , .~: : :

This embodiment of this invention now provides a robotic arm controller that is able to reach for a target point of gaze in the presence~of obstacles. The~target point of gaze is specified by a pair of targeting cameras. The systèm is novel because~ it is initialized once so that it can henceforth operate with any dynamic obstacle; set.~ During preprocesslngt the workspace is free of obstacles Once preprocessing is complete, obstacles can be arbitrarily introduced into the workspace The system~may then be used to find arm tra~ectories~from the current configuration to a target c onfiguration specified indirectly~via~the targ~ehng cameras. Two distinct advantages over the prior art emerge~ it is possibIe to~change~the~obstacle set~without~reinitialization being required and 2) 1t is possible~ to~reorient~the robot (which changes the apparent orienhtlon of the obstacles if any exist) also withou~t re-initializat~on be;ing needéd. ~
The obstàcle~sensing~sùbsystem Gontains~ ne ns~t a pr i~ a 3-d representation of the r~s~workspace,~ It mdicates.'which re~gions~ aro~occupled~ the~objects m the workspæe and by the robot arm ltself.~This information~can~be~extracted from data collected by a vision system; e.g., from a~stereo depth network. For the simpler~2-d~sltuation,~;it ls~the direct output of ~a high contrast digital camera~where the backg$c~nd is~white and obsta- cles~are~black. The image of the robot am thé obstàcle neurons is efe~ed to:as~lts self-image. The~a ~control subsystem contains ar neurons representing the quantized ~àrm configuration ~space. For a 2 degree of freedom arm, each arm ~ne~uron has~a~centrold~ that ls ~a 2-vector~ ~each~ ~component ~representing one joint setting).
Sirmilarly, the targeting~subsystém~contains -targeting~ ne~ons~representing the quanhzed targeting confifuradon~space. Each targeting~néuron~has a centroid that~is:a 3-vector (assuming each camera can~swivel~horizontally - ~1 angle~per~càmera~for a total~of two,~and both are mounted on the same ~pla~form~iat can ~swivd vertiCally- ~ l more~angle). Thé ~neurons in each network~can be in one of thre~e statès: neutral~aGhva~ed~or:deacdvated. ~
A~simplified~2-DOFrobotic-arrn~ a;2-dimènsionalworkspaceincludes~13~a~targetingneuron céntro~id~is ~a 2-vector~of angles~for a pa~ of targetin~g~cameras~ ~in~ a:3-dimensional workspace, it would bé~genera ized to a~3-vec~, (2~ a ~ a ~neu on cént oid~is~a 2-vector of joint set gs ~it can be gener~ized to~contain- any~numbe of pnsmatic and; revolute joDts)~, and ~3) an obstacle neuron centroidi~not `shown) is the centre coordinate of a pixel from a 2-d image of the workspace as seen from àbove by a third ~dependent camera. ~
There are two operational phases: a preprocessing phase where the neurons are initialized and correlated and a running phase where ~an arm~trajectory; ls generated and executed. In this context, an arm trajectory is an ordered~sequence of centroids for consecutive non-deactivated neighbouring arm~neurons~ that can be used to~ move the~arm~from the centroid of the current arm neuron to the centrold of ;an active goal neuron l.é., an arm neuron that is activated by the culrent targeting 39~

~i 20~ 9~3 neuron. Because the centroids for deachvated arrn neurons, which represent arm eonfiguration that ; .
intersect obstacles, are not included in the trajectory, the arrn movement avoids collision with obstacles. Linear joint interpolated motion can be used to move the arm through the joint settings represented by the successive centroids ~in the traJectory. This produces smooth coordinated motion.
The three stages of the preprocessing may be described as follows: In this phase, no obstacles - ~ i are permined in the workspace.
(1) The Space Quantization Stagè: ~Creates the neurons for each subsystem (eaeh having unique ids and eentroids) that cover the~ space. The space quantization stage creates the neurons that : . - ;J."
cover the spaces; i.e., for a k-dimensional~space, it creatés a set of neurons with centroids of the , ~ .
form [Jl,J2,.. ,ik] where ea~ch~ is one of the values~from~the~sequence ll+ei,li+3i,1i+5ei,.. ,u~h- :~
ei where 2ei is the interval width and li and ui are the lowerbound and upperbound respectively for , ;
dimension i. The smaller the;intérval width, the ~more accurate the resuldng controller will be.
However,;more aceuraey requires~more neurons.
(2)~ ~ The Subsystems Correlation~ Stage: For eaeh arm neuron e, move the arm to the -neuron~eent~roid of c and;perform the~following: (a) for~eaeh obstaele neuron o that interseets with the volume of the arm; (i)~Correlate Obstaeles With Conflieting Arm Configurations: conneet o to e (a confliet eonneetion); i.e., store the id of e in o's list; and (ii) Correlate Arm Configurations With The Self-Image: connect c to o (a~self-image conneetion);;l.e., store the id of o in c's list; (b) correlate Targehng Configurations With~Goal~Arm Configurations: The targeting configuration sensors are oriented to point at the arm tip~and the eorresponding targeting neuron t is determined.
Connect t to o (a goal eonneetion); i.e., store the id of o in t's goal aTm neuron list. ;~
, : : . , The concern here is to provide (I) mappings from a targeting neuron to a set of arm neurons in which the arm tip is at the same point of focus, later~used to find potential goal arm neurons, (2) a mapping from an arm neuron to the obstacle ~neurons that represents the arm (the self-image), later usod to~ deactivate the self-image, and~ (3) a mapping from an obstacle neuron to any arm neuron whose self-image ineludes the obstacle neuron, later used to deactivate arm neurons for arm ~ ~;
configurations that intersect with obstacles. All three mappings can be set up at the same time by `~
considering each arrn lneuron in turn. If the arm can adopt the configuration represented by the centroid (i.e., the links do not collide with each olher or the base), the targeting cameras are focused on the tip of the arm. This can be done automatically by tracking a light mounted on the tip of the arm. The resulting targeting camera configuration can then be used to detem~ine the current targeting neuron. Connections as~described abovc can then be made. ~: -:
' ' ~ .
- 40 - ~
~ . .

2 ~
-. ~ .
~ ~ .
(3) The Neighbour Connection Stage: For each arm neuron c, connect c to d if d differs from c by one interval width in one dimension. A solution path must be an ordered sequence of discrete arm configurations which are neighbours in~the arm configuration space. By neighbours, is meant that the corresponding centroids differ in at most one dimension by one interval width. To permit such paths to be easily found, neighbouring arm neurons are connected by links. C~iven a starting neuron, it is easy to ~md a path~to any~potential goal neuron (an activated one) by using a path finding algorit m that traverses only~neurons that are not deactivated.
The running phase is now~described further as follows. In this phase, obstacles are perrnitted anywhere in the workspace.
(1) The Preparation Stage: Perform (a)~and then perform (b), (c), and (d) in parallel. (a) Place all neurons in a neutral~ state;~ (b) Activate~the arm neuron corresponding to the current arm c on~lguration;~ (c) Activate the tar~geting~néuron corresponding to the targeting configuration; and (d) Activate the obstacle neurons ~coIresponding to occupied volumes of the workspace. Sensory info mation is used to update the state of ;the neurons. In the case~ of the arm control and targeting subsystems,~a search is performed to find the neuron~whose centroid is closest to the respective arm and targeting motor configurations. In the case of the obstacle sensing subsystem, information about the position of all objects~m the workspace Is ~used;to activate the obstacle neurons for the occupied regions.
(2) ~The~Self-Tmage ~Deactivation Stage: Deactive~all obstacle neurons in the list associated wlth the current arm neuron. I~ese are the self-image neurons. The self-image neurons must be deactivated to~ ensure that the arm is~ not considered an obstacle to itself. Otherwise, slight movements of the arm would inevitably mtOEsect with its initial position and be considered illegal;
i,e.,~the path planner would avoid movmg ~the arm through regions that it initially: occupied.
(3)~ ~ The Planning Stage:~Perfor ~ (a) and (b) ~in parallel and then perform (c); (a) Activate Potenti:al Goal Arm Positions:~Activate~all arm neurons in the;list associated with the active target ng~ neuron. These are goal neuro~is; (b) ~Deactivate Arm Positions Conflicting With Obstacles~
Deactivate all arm neurons in the list associated ~with active obstacle neurons. These are conflict neurons, (c) Find a Solution Trajectory: Determine a shortest path of consecutive neighbours from the current arm neurdn to any other active arm neuron that includes only intermediate neutral neurons. First, potential goal arm configurations are found by activating the arm neuron connected to the currently active targeting neuron. These are the ones whose centroids represent arm configurations which position the tip~of th;e~arm near the point focused on by the cameras. By deactivating all arm neuron connected to activè~ obstacle neurons, arm configurations that would intersect with obstacles (the confllct neurons) are eliminated from consideration by the path ,,, ~, ... .
. .

2~3 9~
~ , ;" -planner. Any path from the currently active arm neuron to any other still active neuron is a valid solution if all interrnediate neurons are neutral; i.e., they do not collide with obstacles and they do not represent a final configuration. ~A shortest path (there may be more than one) is the most desirable.
(4) The Arm Movement Stage: The arrn is moved through the successiYe positions in the path's neuron centroids via linearly mterpolated jomt movements. The linear interpolation ensures a smooth movement.
The preprocessmg phase is; aclivated once when the system is inidalized. Initialization from that point on can be permanentIy~deæthated ~although~it rnight be useful periodically to go through the preprocessing stage to handle ~changes in; the ~arm~ kinematics through wear and misuse).
Planning, however, is initiated~ ~1); whenever the arrn~;is to~be~targeted to a new point of focus, (2) whenever the robotic system as a~whole~ls moved, or (3) whenever the obstacle set is changed.
One embodiment of the ~invention is ~now ~described which is configured to control a 2-dimensional robot with a 2-degree of freedom manipulator and a 2-degree of freedom targeting eamOEa subsystem need only provido a view frorn a camera suspended above the workspace. Each pixel ls either occuyied by an~object, e.g., eorrespond to active neurons; or deactivated or neutral neurons.
The main task of tho preprocessing stage~ls to make eonnections between the neurons of the subsystems. The arm is placed in an arbitrary~configuration and the targeting carneras are focused on it. Connections are made ~(l) from~the targeting neurons to the arm neurons, (2) from the o bstaclè neurons to the arrn neurons~and (3) from the arm~neurons to the obstacle neurons for the self-image connections.
Connections may be made from the targeting neurons to the arm neurons. For example, when ; the arm~is in the configuration represènted by arm neuron, the ~targeting system configuration (once it is focused on~the end-effector) is re~presented by a specific targeting neuron. This neuron is then connected ~to the~ arrn system configuration~ neuron.~When~the arm is in another configuration represented by another arm neuron which~happens to place the end-effector at the same point, a second connection *om the first specific targeting neuron eo the second arms system configuration neuron is made. In general, there are an~ infinite number of arrn configurations that result in ehe end-effector being at the same~point.~Consequently, the specific targeting neuron will be colrelated with a large number of arm neurons.~ Because of the quantization, however, this number will be finite. This is important for planning because an arbitrary number of arm configurations can be selected with one targeting system configuration. Additionally, the arm neurons correlated with a speclfic targeting neuron are not generally neighbours in the arm control subsystem. If the planner : .

:~ 2~ 9 ~ J~

,, uses minimal or near minimal paths, only a small subset of these arm neurons will be choices that result in minimal path motion.
Connections may be made from the obstacle neurons to the arm neurons (connections in the other direction for the self-image connections are not shown). The actual connections shown are only suggestive since there are too many to show explicitly. Recall that each arm configuration causes a specific neuron in the arm control subsystem to be activated and a set of neurons in the obstacle sensing subsystem to be activated (corresponding to the self-image since no actual obstacles are used during preprocessing). For exarnple, when the arm is in the configuration corresponding to the first arm neuron, all active obstacle neurons are connected to that arm neuron.
Consequently, many obstacle neurons will be ~correlated with one arm neuron. When the arm is in another configuration corresponding to the second arm neuron, a different set of obstacle neurons are active (although the two sets may have much in common with each other). Each of these active obstacIe neurons are connected to the second arm neuron. An obstacle neuron that lies at the intersection of two self-images will be correlated with two arm neurons. In general, each obstacle neuron wi~ correlate with a~ aTm neurons representing configurations where some part of the robot arm intersects the environment region represented by the obstacle neuron. During the planr~ng phase (after all connections have been made), self-image neurons in the obstacle sensing subsystem are deactivated. The remaining activated obstacle neurons consequently denote areas that are obstacles to the arm. The system wi~ consequently deactivate a~ arm neurons that conflict with these making illegal any arm configuration that would intersect with the corresponding obstacles. Note that changing the obstacle set wi~ merely cause different arrn neurons (in general) to be deactivated. No changes to the connections are made. As an optimization, it is not necessary to correlate immovable parts of the self-image since obstacles can never co-exist with them. Thus it is possible (but not necessary) to subtract the arm control subsystem platform from the neurons in the obstacle sensing subsystem.
In general, there is no need to insist that any of the subsystem quantize their respective domains using equal sized re~ons. For example, to provide more accuracy in the eypical working areas, the neurons in the heavily used areas could be further partitioned. Alternatively, the neurons could be a~ocated using a scheme that topologica~y organizes them for maximal accyacy.
Thus the present invention provides a preliminary design for a new class of neural planners.
Neuroplanners possess four imponant characteristics. First they are applicable to a wide variety of domains. Second, they are capable of self-organization and learning. Third, they lend themselves to implementations in massively parallel hardware and are therefore potentially very fast. Fourth, goa s are spe if iable at a higher level than the system configurations of the system; this includes the abi ity to specify large sets of ina system configurations as a single targeting con lg ration.

2 ~ ~ 9 0 ~ ~ !
',~,' .:' ' The lnvention described hereln has several advantages over the prior art. More specifically, ~-the following advantages can be obtained~for different embodiments of this invention:
1. The invention is capable of ~autonomously learning kinematics and workspace constraints ~ :
for any arbitrary robot anatomy,~dispenslng with~the need for specialized kinematic programming.
2. The invennon integrates~ a colllsion avoidance~process~that permits collision-free trajectory planning for arbitrarily cluttered workspaces contailing arbitrarily shaped obstacles.
3. l~e~adaptlve~nature of theinvent~on makes It ~posslble to automadcally adapt the system to changes in targeting and arm~subsystèm relationships and~feedback sensor calibrations, reducing the~ need for maintenance.
4 . Th~e invention~does not~require a system of ~universal, object based, or robot based coordinate frames. Targets~are ~selected in~terms~;of~egocentric~sensory coordinates by simply orienting~the~ targeting subsystem at tne target point. ~ ~ :
5. The~invention can be made; sensitive to the domain specific distribudon of aIm movements.
It will ~automaticaUy~increase~the accuracy of the arm in those regions of the workspace where precision is most needed.: ~Iigh: prècision ~in important regions of the workspace is: increased at the - ' expense of lower precision in~regions that are rarely used. ~
6 The mYentlon m toletttnt; of ~random noise in the~training~data. A small number of incorrect training samples will not~qualitatively aIter the~system's prècision immediately after preprocessing.

7.~ The invention is capable~ of planning arbitrarily complicated arm movements very quickly. ~ ;
Those~ who are ~skilled~ in the art ~of robotics will understand that the inverse kinematics are ;
calculated in~constant time. Furthermore, the speed of the invéntion is not Telated to the number or shape of obstacles cluttering the workspace. ~

8. Only one preprocessing stage is required to integrate constraints for all possible numbers, sizes and~configurations of obstacles..Consequently, if the obstacle sét changes in any way, the robot does not~have to~re-execute ~the~preprocessing stage. ~ The invention achieves this by integrating knowledge about the obstacles in an indirect manner by determining where they cannot be rather than wherel they are; i.e., by working with the robot's self-image and its egocentric workspace rather than speetfie obstacle~ sets. ~

9. More than one target arm configuration is proposed f a given target point, increasing the likelihood of finding a sueeessful trajeetory in very eluttered workspaces. ;~

10. "Virtual" obstacles and other ~heunsties as used in the prior art are not needed to avoid eollisions.

~: -" ~

Claims (31)

THE EMBODIMENTS OF THE INVENTION IN WHICH AN EXCLUSIVE
PROPERTY OR PRIVILEGE IS CLAIMED ARE DEFINED AS FOLLOWS:

1. A system for finding a sequence of configurations from an initial configuration to any one of a set of goal configurations in the presence of prohibited configurations, comprising:
(a) means for specifying goal system configurations by selecting a targeting configuration that is in turn correlated with one or more of the system configurations; (b) means for creating an environment network, a system configuration network, and a targeting configuration network that respectively encode quantized representations of environmental information, the system configurations, and the targeting configurations; (c) means for correlating the environmental network with the system configuration network and also correlating the targeting configuration network with the system configuration network so as to be able to specify prohibited system configurations and goal system configurations respectively; (d) means for finding a trajectory from one system configuration; e.g., the current system configuration, to a goal system configuration that excludes prohibited system configurations.

2. The system of claim 1 wherein said system is especially adapted to find collision-free paths for a robot arm in a workspace, whereby said means (d) comprises means for finding a collision-free trajectory for said robot arm from one configuration space neuron to another configuration space neuron.

3. Adaptive robot architecture for a robot having a body and at least one arm having "J"-degrees-of-freedom, a "J"-degree-of-freedom manipulator, arm links, and arm joints, said arm joints being revolute and/or prismatic, said architecture comprising:
(a) environment sensor means immovable relative to the first joint of the arm, for sensing the relative position of the arm and the location of workspace obstacles; (b) joint displacement sensors, for outputting the amount that the associated links of the arms are offset from their rest position; (c) means, for selecting target points and orienting the environment sensors towards the target points, said means being connected to said relative position environment sensors, for receiving eye angle feedback; (d) means, for orienting said relative position sensors, towards the tip of the robot arms, by tracking said means, said means being connected to said relative position environment sensors;
(e) means, for incrementing each joint by small random amounts, for the purpose of taking the robot arm through a representative set of configurations, said means being connected to said joints for controlling movement; and (f) means, for generating a sequence of joint system configuration vectors for moving said arm from its current position to one of several possible permitted target configuration, said means simultaneously receiving joint setting feedback from said system configuration sensors, eye angle feedback from said targeting configuration sensors, and information regarding the position of objects in the workspace from environmental sensors.

4. The adaptive robot architecture of claim 3 wherein said environment sensor means comprises at least two stereo cameras or range finders.

5. The adaptive robot architecture of claim 4 wherein said joint displacement sensors comprise joint position or setting sensors.

6. The adaptive robot architecture of claim 5 wherein said means for selecting target points comprises task level controller.

7. The adaptive robot architecture of claim 6 wherein said means for orienting said relative position sensor, comprises an end-effector tracker.

8. The adaptive robot architecture of claim 5 wherein said means for incrementing each joint setting comprises a random movement generator for incrementing each joint setting by small angular amounts.

9. The adaptive robot architecture of claim 8 wherein said random movement generator is connected to receive joint setting feedback from said setting sensors.

10. The adaptive robot architecture of claim 9 wherein said means (f) comprises an adaptive arm controller simultaneously receiving joint setting feedback from said arm, eye angle feedback from said stereo cameras and information regarding the position of objects in the workspace.

11. The adaptive robot architecture of claim 10 wherein said adaptive arm controller includes the following three levels of neural processors; an obstacle network providing obstacle position data for indicating all occupied positions in the workspace of said arm; an arm configuration network providing joint setting vector data for indicating the current position of said arm; and an eye configuration network for providing eye angle vector data for indicating where said arm is to go.

12. The adaptive robot architecture of claim 10 wherein said adaptive arm controller comprises a neuroplanner, said neuroplanner being implemented by: a pixelated environment network of an "n"-dimensional space, said environment network being indicative of environment data of a plurality of neurons in the active state and a plurality of neurons in the inactive state; a Kohonen system configuration network, said system configuration network being indicative of a system configuration of a plurality of neurons in the inactive state, a plurality of neurons in the active state, a plurality of neurons in the intermediate system configurations in the solution path and at least one neuron in the initial system configuration, scud pixelated environment network being connected to said Kohonen system configuration network by controls from said active neurons on said pixelated network to inhibited neurons on said Kohonen system configuration network; and a Kohonen targeting configuration network, said targeting configuration network being indicative of the targeting configuration, and comprising a plurality of neurons in the inactive state and at least one neuron in the active state, said targeting configuration network being connected to said system configuration network by controls from an active neuron on said targeting configuration network to selected inhibited and/or active neurons on said system configuration network, whereby said arm trajectory Is a sequence of weight vectors (arm configurations).

13. The adaptive robot architecture of claim 10 wherein said adaptive arm controller comprises a neuroplanner, said neuroplanner being implemented by: a pixelated environment network of an "n"-dimensional space, said environment network being indicative of environment data to provide a volumetric representation of a workspace and including a plurality of neurons in the active state and a plurality of neurons in the inactive state; a Kohonen arm map, said arm network being indicative of a system configuration, and including a plurality of neurons in the inactive state, and at least one neuron in the initial system configuration to provide an indication of the current arm configuration; and a Kohonen eye network, said eye network being indicative of a current configuration, and including a plurality of neurons in the inactive state, a plurality of neurons in the active state, a plurality of neurons in the inhibited state, a plurality of neurons in the intermediate arm configuration in solution trajectory and at least one neuron in the active state indicative of the current eye configuration, said eye network being connected to said pixelated environment network by controls from the active neurons on the pixelated environment network to selected inhibited neurons on the eye network, and said arm network being connected to said eye network by controls from the current arm configuration neuron on said arm network to selected active neurons;
whereby the eye trajectory is a sequence of weight vectors (eye configurations).

14. The adaptive robot architecture of claim 10 wherein said adaptive arm controller comprises a neuroplanner, said neuroplanner controller permitting said end-effector to be targeted in the presence of obstacles by pointing at the target location, the neuroplanner architecture including sensors to detect motor limits, collision detectors to prevent self-intersection during adaption and calibration, and a simple vision facility that can observe the arm, whereby said controller is independent of the link design.

15. The adaptive robot architecture of claim 10 wherein said adaptive arm controller comprises a neuroplanner said neuroplanner integrating three vector spaces; an environment space; a system configuration space; and a targeting configuration space; each said space is being represented by a quantized network of "d"-dimensional volume elements (hypercubes); each volume element (of pre-specified dimension) being represented by a point denoting the centre of the volume; said "d"-dimensional point therefore representing an infinite number of points in a neighbourhood; each point in said neighbourhood being quantized to the unique centre point.

16. The adaptive robot architecture of claim 15 wherein said environment neurons represent workspace occupancy; wherein said targeting neurons represent stereo camera configurations; and wherein said system configuration network elements represent arm configurations.

17. The adaptive robot architecture of claim 15 wherein said system configuration network is topologically organized, whereby given a volume element, it is possible to determine which other volumes are neighbours, by explicitly connecting them in order to determine a path of neurons in said system configuration network.

18. The adaptive robot architecture of claim 15 wherein said targeting configuration network is used to specify targets wherein each neuron denotes a configuration of the targeting stereo cameras;
wherein said system configuration network is used to specify paths, wherein each neuron denotes an arm configuration and a succession of topologically neighbouring neurons denotes a path; and wherein said environment network serves to constrain or inhibit illegal system configurations, wherein each neuron denotes a volume element denoting occupancy; whereby said neuroplanner system configuration network represents joint (configuration) space.

19. A robotic controller comprising: a quantized representation of a workspace, a quantized representation of the arm and a quantized representation of targeting sensor configurations, each such quantized volume comprising a quantization neuron; whereby said workspace is a quantized 3-dimensional rectilinear volume, a set of 3-dimensional quantization neurons that cover the workspace; whereby said arm and targeting configuration spaces are as quantized "n"-dimensional and "m"-dimensional rectilinear volumes respectively, quantization neurons that cover their respective configurations spaces; and whereby said targeting system being adapted to specify horizontal position, vertical position, and depth.

20. The robotic controller of claim 19 comprising: an obstacle sensing subsystem, consisting of a collection of obstacle quantization neurons; and arm control subsystem consisting of a collection of arm configuration network quantization neurons; and a targeting subsystem consisting of a collection of targeting quantization neurons.

21. The robotic controller of claim 19 wherein each said quantization neuron comprises a computing element that stores four data values: a unique identifier (id); a state indicator, one of neutral, activated, or deactivated; a k-vector [j1,j2,...,jk], the centroid of an k-tuple of intervals [j1?e1, j2?e2,...,jn?ek] representing a regionof k-dimensional space; k is the neuron's dimension, [j2...jk] is the neuron centroid, and 2ei is the interval width for dimension; and a list containing the ids of other quantization neurons.

22. The robotic controller of claim 21 wherein when the list associated with neuron "p" contains the id of a neuron "q", then "p" is connected to "q", and "p" may be connected to another neuron "r" by adding "r" to the list associated with "p".

23. The robotic system of claim 19 wherein obstacle neurons are connected to arm neurons;
wherein arm neurons are connected to obstacle neurons; wherein targeting neurons are connected to arm neurons; and wherein arm neurons are also connected to other arm neurons to keep track of neighbours.

24. The robotic system of claim 20 wherein each of the quantization neurons comprises an individual processor or a software data structure contained in a single computer.

25. The robotic system of claim 20 wherein each of the three subsystems encodes its own specific domain in its neurons, as follows: the domain of the obstacle sensing system is the 3-dimensional volume of the workspace of the robot and each obstacle neuron contains a 3-tuple that represents one of the equal-sized subvolumes of the quantized domain; the domain of the arm control subsystem is the "n"-dimensional configuration space of the robot's arm, and each arm neuron contains an n-tuple that represents one of the equal-sized sub-divisions of the quantized arm configuration space; and the domain of the targeting system is the "m"-dimensional configuration space of the robot's targeting motors, typically m is 3, and each targeting neuron contains an m-tuple that represents one of the equal-sized subvolumes of the domain.

26. A method for initializing the robotic system of claim 19 in which the following information is provided: the number of dimensions n for the arm control subsystem, for each system, the interval width for each domain dimension, for the obstacle domain, the number of degrees for the arm control and targeting motor domains; and for each subsystem, the number of quantization neurons being equal to the product of the number of divisions in each dimension, said method comprising (a) creating neurons for each system, each neuron having unique ids and centroids that cover the space, and for each arm neuron "c", moving said robot arm to the neuron centroid of "c" and performing the following: (a) for each obstacle neuron "o" that intersects with the volume of the arm, (i) connect "o" to "c", by storing the id of "o" in "c" 's list; (b) orienting the targeting configuration sensors to point at the arm tip and determining the corresponding targeting neuron "t"; connecting "t" to "c", by storing the id of "c" in "t" 's list; and for each arm neuron "c", connect "c" to its neighbours, by connecting "c" to "d" if "d" differs from "c" by one interval width in one dimension.

27. The method of claim 26 for operating said robotic system in which the following information is provided: the current arm configuration; and the targeting configuration for the point to which the arm tip is to be moved, in which the running phase executes as follows: preparing the system by performing the following: (a) placing all neurons in a neutral state; (b) activating the arm neuron corresponding "t" the current arm configuration; (c) activating the targeting neuron corresponding to the targeting configuration; (d) activating the obstacle neurons corresponding to occupied volumes of the workspace; (e) deactivating all obstacle neurons in the list associated with the current arm neuron; and planning a path as follows: activating all arm neurons in the list associated with the active targeting neuron; (g) deactivating all arm neurons in the list associated with active obstacle neurons are deactivated; (h) determining a shortest path of consecutive neighbours from the current arm neuron to any other active arm neuron that includes only intermediate neutral neurons; and then moving said arm through the successive positions in the path's neuron centroids via linearly interpolated joint movements.

28. A method of training a robotic system of claim 19, in two stages composing:
1. generating a representative set of arm and eye configurations by randomly moving the arm and articulated camera platform, by orienting the cameras to focus on the end-effector and using the associated configurations as training, data for the Kohonen algorithm to self-organize the arm and eye networks; and after self-organization, generating a new representative set of arm and eye configurations as above, by using the Kohonen algorithm, to determine the corresponding arm and eye neurons "a" and "e" respectively and performing the following:
(a) growing a connection from "o" to "a" and from "a" to "o" for each environment neuron "o" in the arm self-image;
(b) growing an obstacle connection from "o" to "e", and growing a connection from "e" to "o" for each environment neurons "o" in the eye self-image;
(c) growing a lines-of-sight connection from "o" to ''e" for each environment neuron "o" in the lines-of-sight self-image; and (d) connecting the permanently illegal configurations neuron to the corresponding arm or eye neuron if "o" or "e" is at the limit of movement.

29. A method of planning the system in the arm space for the robotic system of claim 19, comprising:
1. placing all neurons in the inactive state, orienting the articulated cameras, and initializing the environment network by activating areas occupied by objects and inactivating all other areas;

2. determining the arm and eye neurons corresponding to the current arm and eye configurations by Kohonen competition;
3. inhibiting the environment neurons connected to (its self-image) the current arm neuron;
4. simultaneously the active eye neuron activating a set of arm neurons potential goal neurons) by the active eye neuron and inhibiting all connected arm neurons (arm configurations that intersect obstacles) by the active environment neurons;
5. finding a minimal path from the current arm neuron to any one of the active goal neurons that includes only inactive (not inhibited) neurons by the path planner and 6. representing the solution arm trajectory by the weights of these neurons.

30. A method of initialization of a neural architecture which is in principle capable of autonomously learning the kinematics and workspace constraints for any general robot manipulator, dispensing with the need for specialized kinematic programming; which autonomously learns hand-eye co-ordination, providing a natural form of task level control in which a target point is selected by "glancing" at it with visual sensors; which learns to produce collision-free movements of the arm;
in which the obstacle sets can be changed without re-learning; in which, the robot can be moved without requiring either reorganization or relearning; and in which more than one arm configuration is proposed for a given target; which method comprises:
(1) a space quantization stage comprising: treating the neurons for each subsystem (each having unique ids and centroids) than cover the space, wherein for a K-dimensional space, creating a set of neurons: with centroids of the form [J1 ,J2,...,Jk] where each j1 is one of the values from the sequence l1+ei,1i+3ei, li+5ei, ... , ui-ei where 2ei is the interval width and li and ui are the lowerbound and upperbound respectively for dimension i, whereby the smaller the interval width, the more accurate the resulting controller will be;
(2) a subsystems correlation stage comprising: for each arm neuron "c", moving the arm to the neuron centroid of "c" and performing the following: (a) for each obstacle neuron "o" that intersects with the volume of the arm correlate obstacles with conflicting arm configurations by connecting "o" to "c"; and storing the id of "c" in "o" 's list, correlating arm configurations with the self-image by connecting "c" to "o" (a self-image connection) and storing the id of "o" in "c" 's list; correlating targeting configurations with goal arm configurations by the orienting targeting configuration sensors to point at the arm tip and determining the corresponding targeting neuron "t"
and connecting "t" to "o" (a goal connection) by storing the id of "o" in "t" 's goal arm neuron list;
and (3) a neighbour connection stage comprising: for each arm neuron "c", connecting "c" to "d"
if "d" differs from "c" by one interval width in one dimension, whereby a solution path is an ordered sequence of discrete arm configurations which are neighbours in the arm configuration space; wherein neighbours, means that the corresponding centroids differ in at most one dimension by one interval width.

31. The running phase of the method of claim 30 comprising the following:
(1) performing a preparation stage by first placing all neurons in a neutral state; and then performing (b), (c) and (d) in parallel; (b) activating the arm neuron corresponding to the current arm configuration; (c) activating the targeting neuron corresponding to the targeting configuration;
and (d) activating the obstacle neurons corresponding to occupied volumes of the workspace;
(2) performing a self-image deactivation stage by deactivating all obstacle neurons in the list associated with the current arm neuron;
(3) performing a planning stage by performing (a) and (b) in parallel: (a) activating potential goal arm positions by activating all arm neurons in the list associated with the active targeting neuron; and (b) deactivating arm positions conflicting with obstacles by deactivating all arm neurons in the list associated with active obstacle neurons; and then (c) finding a solution trajectory by determining a shortest path of consecutive neighbours from the current arm neuron to any other active arm neuron that includes only intermediate neutral neurons; and (4) performing an arm movement stage by moving the arm through the successive positions in the path's neuron centroids via linearly interpolated joint movements, whereby the linear interpolation ensures a smooth movement.