CN108897315A - A kind of Multi-agent Team Formation - Google Patents
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- 230000015572 biosynthetic process Effects 0.000 title claims abstract description 41
- 238000001514 detection method Methods 0.000 claims abstract description 22
- 210000004227 basal ganglia Anatomy 0.000 claims abstract description 20
- 230000009471 action Effects 0.000 claims abstract description 9
- 238000004364 calculation method Methods 0.000 claims abstract description 7
- 210000002569 neuron Anatomy 0.000 claims description 57
- 210000001905 globus pallidus Anatomy 0.000 claims description 36
- 210000004281 subthalamic nucleus Anatomy 0.000 claims description 23
- 210000003710 cerebral cortex Anatomy 0.000 claims description 12
- VYFYYTLLBUKUHU-UHFFFAOYSA-N dopamine Chemical compound NCCC1=CC=C(O)C(O)=C1 VYFYYTLLBUKUHU-UHFFFAOYSA-N 0.000 claims description 12
- 230000006870 function Effects 0.000 claims description 11
- 238000013178 mathematical model Methods 0.000 claims description 7
- 229960003638 dopamine Drugs 0.000 claims description 6
- 238000012360 testing method Methods 0.000 claims description 5
- 238000004458 analytical method Methods 0.000 claims description 4
- 230000005764 inhibitory process Effects 0.000 claims description 4
- 238000012937 correction Methods 0.000 claims description 3
- 230000005284 excitation Effects 0.000 claims description 3
- 230000002401 inhibitory effect Effects 0.000 claims description 3
- 230000010354 integration Effects 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 3
- 238000003786 synthesis reaction Methods 0.000 claims description 3
- 238000005070 sampling Methods 0.000 claims description 2
- 238000007689 inspection Methods 0.000 claims 1
- 238000005755 formation reaction Methods 0.000 abstract description 30
- 238000000034 method Methods 0.000 abstract description 22
- 238000005381 potential energy Methods 0.000 description 4
- 230000008901 benefit Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 241000208340 Araliaceae Species 0.000 description 2
- 241001566735 Archon Species 0.000 description 2
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 description 2
- 235000003140 Panax quinquefolius Nutrition 0.000 description 2
- 238000003556 assay Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 235000008434 ginseng Nutrition 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 230000008859 change Effects 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 239000011521 glass Substances 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
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- 230000003252 repetitive effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000009897 systematic effect Effects 0.000 description 1
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
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Abstract
The invention discloses a kind of Multi-agent Team Formations, and in particular to a kind of Multi-agent Team Formation with action selection function, reliability be difficult to ensure the problems such as poor with the robustness for solving system in traditional formation method.The specific steps are：Step 1: setting detection income calculation method relevant to position；Step 2: defining the concrete behavior of robot；Step 3: determining the number of channels of basal ganglion, basal ganglion channel pattern is established, initializes relevant parameter；Step 4：Correct the channel pattern parameter of basal ganglion.The present invention is for Collaborative Control of forming into columns in multirobot Detection task.
Description
Technical field
The present invention relates to the formation fields of robot, and in particular to a kind of multi-robot formation with action selection function
Method.
Background technique
Robot replaces people to complete the repetitive operation under complicated, dangerous environment, accelerates the development of society.In recent years
Carry out social market and new demand is proposed to the function of robot, robot needs to complete increasingly complex Detection task even
Job task, but in face of complicated, when efficient, the completing parallel of the task, single robot can not be competent at, and need more
Robot collaboration.
Robot team formation control is a kind of multiple robots during reaching target formation, can form target team
Shape, and it is adapted to a kind of control method of specific environmental constraints.Existing common method mainly have based on navigate-with
With person's method, virtual architecture method, the method for Behavior-based control, based on graph theory, the method for potential energy method.
Wherein pilotage people-follower's method basic thought is that may exist in the fleet system of multirobot composition
One or more pilot robot, other non-pilotage people robots are to follow robot, follow robot with its opposite neck
Navigate the position relative distance of robot, be used as input control quantity in relative angle so that following robot and pilot robot
Target value is infinitely approached in relative position.The control structure of navigator's follower method is fairly simple, but due to pilot robot and with
There is no position feedback between random device people, and be the control of pilotage people robot single-point, causes to be easy to appear robot and fall behind
The robustness of situation, system is poor.
The basic thought of virtual architecture method is to regard the system of multirobot composition as an imaginary rigid structure, each
Coordinate of the robot under reference frame is constant, i.e., the relative position between robot is constant.
The basic thought of the method for Behavior-based control is to regard the links of multi-robot formation by individual machine people as
Multiple basic acts are constituted, it is only necessary to which the control method for studying each basic act can be more by the combination of basic act control
Robot, which is formed, forms into columns.Basic act generally comprises target following, avoids obstacle, avoids collision, form into columns generation and holding of forming into columns
Deng.The method of Behavior-based control is controlled by each robot mutual perception, systematic comparison distributed AC servo system easy to accomplish, is had fine
Robustness.But since accurate mathematics model analysis can not be combined, cause cannot highly effective guarantee system it is reliable
Property.
Potential energy method potential field method is to form control method with the conventional method another formation unfortunately, it is by formation
All steps formed are combined into together, and close potential function is closed by building, determine that the control law of robot will by potential field function
Other robot acts on its different force, in conjunction with desired formation figure, moves robot towards expected formation figure, when
When respectively reaching formation figure, the potential energy glass of whole system is small.In formation forming process, formation target point is dynamic change,
Belong to dynamic formation forming method, robot is to reach an equilibrium state and constantly adjusting mutual distance；But team
The rapidity and controllability that shape is formed can not be held, due to being to form formation according to the formation figure of relative distance, then illustrating
The mutual relative position of robot asked be in fact it is pre-determined, this just makes the target position of robot may not be best
, and the formation figure due to keeping a relative position fixed in entire formation target, it is assumed that a robot fails to reach finger
Determine formation point, other robot will always be in the state of dynamical research minimum potential energy point and move always.
In view of the above-mentioned problems, proposing a kind of Multi-agent Team Formation of Behavior-based control selection.
Summary of the invention
It is an object of the invention to the reliabilities of system during the multi-robot formation of solution not can guarantee with robustness
The problem of.
In order to solve the above technical problem, the present invention provides a kind of Multi-agent Team Formations of Behavior-based control selection, lead to
It crosses and introduces a kind of basal ganglion action selection formation strategy, enhance the reliability and robustness of system.
Specific technical solution is：
Robot is comprehensively considered to the sum of the distance of movement for forming formation target position dissum, the suitable shifting of robot
Dynamic speed vi, robot is adjusted to the time T of object-oriented postureadjust, the time T of robot motion to target pointmove, machine
The detection income of device people in-position after the next sampling period carries out action selection.
Tcost=Tmove+Tadjust
Step 1: setting detection income calculation method relevant to position.
(1) income area in target acquisition position is set：The setting of target acquisition position income area is specifically following condition：Income
It is circular several piece fan-shaped region that area, which is with target acquisition center,.
(2) income calculation method is detected：In the closer sector in income area, the detection for setting the robot is checked and accepted
Benefit is fixed income relevant to target point significance level；In the farther away annulus in income area, being somebody's turn to do for the robot is being set
Test point income is the dynamic income that distance is inversely proportional between robot and target point.And upon completion of the assays, current work
The detection income in period will be reset.
Step 2: defining the concrete behavior of robot：Robot is defined with function according to the specific structure of robot to be expert at
For selection after the completion of it is possible that concrete behavior.The excitation of each behavior is inhibited relationship to carry out limitation and sets analysis,
If a certain robot behavior can be excited, verified using basal ganglion channel pattern；If the robot row being excited
Can then to execute this behavior by verifying.
Step 3: determining the number of channels of basal ganglion, basal ganglion channel pattern is established, initializes related ginseng
Number.
Basal ganglion mathematical model includes corpus straitum, globus pallidus outer core, subthalamic nucleus, core group globus pallidus kernel.
(1) mathematical model in behavior channel is established：Each channel is indicated with a leakage integral neuron：
Wherein, x is the state of neuron, and u is the input of neuron, and y is the output of neuron, and H is jump function, remaining
For model parameter.
yi C=Si
Cerebral cortex integration, processing obtain combining the synthesis importance index S of each factor in step 1i, wherein i is logical
Taoist monastic name, yi CCharacterize the output in i-th of channel in cerebral cortex.
The state of corpus straitum D1 can be described as:
Wherein, ui SD1It is corpus straitum D1, the neuron input in the channel i, ai SD1It is the state of the channel neuron, yi SD1It is
The output of the channel neuron, wCSD1It is weight of the cerebral cortex to corpus straitum D1.1+ λ-description is dopamine to corpus straitum D1
Incentive action, εSD1For the output threshold value of corpus straitum D1.
The state of corpus straitum D2 can be described as:
Wherein, ui SD2It is corpus straitum D2, the neuron input in the channel i, ai SD2It is the state of the channel neuron, yi SD2It is
The output of the channel neuron, wCSD2It is weight of the cerebral cortex to corpus straitum D2,1- λ-description is dopamine to corpus straitum D2
Inhibiting effect, εSD2For the output threshold value of corpus straitum D2.
Globus pallidus outer core can be described as：
Wherein, ui GPeIt is globus pallidus outer core, the neuron input in the channel i, ai GPeIt is the state of the channel neuron, yi GPe
It is the output of the channel neuron, wSD2GPeIt is weight of the corpus straitum D2 to globus pallidus outer core, εGPeIt is the output of globus pallidus outer core
Threshold value.
The model of subthalamic nucleus can be described as:
Wherein, ui STNIt is subthalamic nucleus, the neuron input in the channel i, ai STNIt is the state of the channel neuron, yi STNIt is
The output of the channel neuron, wGPeSTNIt is weight of the globus pallidus outer core to subthalamic nucleus, εSTNIt is the output threshold value of subthalamic nucleus.
According to anatomically studying, subthalamic nucleus very disperses the projection of globus pallidus kernel, therefore grey in description
When the mathematical model of Archon kernel, the input of globus pallidus kernel is enabled to go out including the subthalamic nucleus in other left and right channels.
It is described as：
Wherein, ui GPiIt is globus pallidus kernel, the neuron input in the channel i, ai GPiIt is the state of the channel neuron, yi GPi
It is the output of the channel neuron, wSD1GPiIt is weight of the corpus straitum D1 to globus pallidus kernel, wSTNGPiIt is subthalamic nucleus to pale
The output weight of ball kernel, εGPiIt is the output threshold value of globus pallidus kernel, it should be noted that wCSD1、wCSD2、wSTNGPiFor positive value
Characterize incentive connection, wSD1GPi、wSD2GPe、wGPeSTNFor negative value, inhibition connection is characterized.
Step 4：Correct the channel pattern parameter of basal ganglion：When the overall operation situation of robot is inclined with user
When well to be deviated under conditions of standard, the correction to model parameters in step 3 is carried out.
Compared with prior art, the present invention its remarkable advantage is：(1) a kind of dynamic machine of Behavior-based control selection is introduced
Device people's formation method, efficiently solves the disadvantages of robustness is not strong, and reliability is not strong in existing formation detection method；(2) pass through
Open parameter and every weight modification channel, so that the present invention has more extensive versatility；(2) target detection income is introduced
Variable can promote the income that multirobot carries out Detection task on the whole, improve running efficiency of system indirectly.
Detailed description of the invention
Fig. 1 is that robot Detection task of the present invention detects income range schematic diagram.
Fig. 2 is basal ganglion schematic diagram of the present invention.
Specific embodiment
The present invention is based on the Multi-agent Team Formations of action selection, include the following steps：
Step 1: setting detection income calculation method relevant to position.
(1) income area in target acquisition position is set：The setting of target acquisition position income area is specifically following condition：Income
It is circular several piece fan-shaped region that area, which is with target acquisition center,.
(2) income calculation method is detected：In the closer sector in income area, the detection for setting the robot is checked and accepted
Benefit is fixed income relevant to target point significance level；In the farther away annulus in income area, being somebody's turn to do for the robot is being set
Test point income is the dynamic income that distance is inversely proportional between robot and target point.And upon completion of the assays, current work
The detection income in period will be reset.
Wherein ηprofitFor the dynamic income of current target point, ηkFor the fixed income of current target point, r be robot away from
With a distance from target detection point, r1For the shortest distance in fixed test income area and target detection point, r2For dynamic income area and mesh
Mark the longest distance of test point.
Step 2: defining the concrete behavior of robot：Robot is defined with function according to the specific structure of robot to be expert at
For selection after the completion of it is possible that concrete behavior.The excitation of each behavior is inhibited relationship to carry out limitation and sets analysis,
If a certain robot behavior can be excited, verified using basal ganglion channel pattern；If the robot row being excited
Can then to execute this behavior by verifying.Design robot posture behavior rotates clockwise, rotates clockwise, advancing, after
It moves back, search for, six kinds of backhaul.
Wherein siCharacterize the significance level of each behavior, ηi kFor constant to be adjusted.
Step 3: determining the number of channels of basal ganglion, basal ganglion channel pattern is established, initializes related ginseng
Number.
Basal ganglion mathematical model includes corpus straitum, globus pallidus outer core, subthalamic nucleus, core group globus pallidus kernel.
(1) mathematical model in behavior channel is established：Each channel is indicated with a leakage integral neuron：
Wherein, x is the state of neuron, and u is the input of neuron, and y is the output of neuron, and H is jump function, remaining
For model parameter.
yi C=Si
Cerebral cortex integration, processing obtain combining the synthesis importance index S of each factor in step 1i, wherein i is logical
Taoist monastic name, yi CCharacterize the output in i-th of channel in cerebral cortex.
The state of corpus straitum D1 can be described as:
Wherein, ui SD1It is corpus straitum D1, the neuron input in the channel i, ai SD1It is the state of the channel neuron, yi SD1It is
The output of the channel neuron, wCSD1It is weight of the cerebral cortex to corpus straitum D1.1+ λ-description is dopamine to corpus straitum D1
Incentive action, εSD1For the output threshold value of corpus straitum D1.
The state of corpus straitum D2 can be described as:
Wherein, ui SD2It is corpus straitum D2, the neuron input in the channel i, ai SD2It is the state of the channel neuron, yi SD2It is
The output of the channel neuron, wCSD2It is weight of the cerebral cortex to corpus straitum D2,1- λ-description is dopamine to corpus straitum D2
Inhibiting effect, εSD2For the output threshold value of corpus straitum D2.
Globus pallidus outer core can be described as：
Wherein, ui GPeIt is globus pallidus outer core, the neuron input in the channel i, ai GPeIt is the state of the channel neuron, yi GPe
It is the output of the channel neuron, wSD2GPeIt is weight of the corpus straitum D2 to globus pallidus outer core, εGPeIt is the output of globus pallidus outer core
Threshold value.
The model of subthalamic nucleus can be described as:
Wherein, ui STNIt is subthalamic nucleus, the neuron input in the channel i, ai STNIt is the state of the channel neuron, yi STNIt is
The output of the channel neuron, wGPeSTNIt is weight of the globus pallidus outer core to subthalamic nucleus, εSTNIt is the output threshold value of subthalamic nucleus.
According to anatomically studying, subthalamic nucleus very disperses the projection of globus pallidus kernel, therefore grey in description
When the mathematical model of Archon kernel, the input of globus pallidus kernel is enabled to go out including the subthalamic nucleus in other left and right channels.
It is described as：
Wherein, ui GPiIt is globus pallidus kernel, the neuron input in the channel i, ai GPiIt is the state of the channel neuron, yi GPi
It is the output of the channel neuron, wSD1GPiIt is weight of the corpus straitum D1 to globus pallidus kernel, wSTNGPiIt is subthalamic nucleus to pale
The output weight of ball kernel, ε GPi are the output threshold values of globus pallidus kernel, and value is it should be noted that wCSD1、wCSD2、wSTNGPiIt is positive
The connection of value characterization incentive, wSD1GPi、wSD2GPe、wGPeSTNFor negative value, inhibition connection is characterized.
Step 4：Correct the channel pattern parameter of basal ganglion：When the overall operation situation of robot is inclined with user
When well to be deviated under conditions of standard, the correction to model parameters in step 3 is carried out.
Finally robot behavior is selected according to the output of globus pallidus kernel.
The present invention can also be in different function, different number scale, difference detection target, Different Exercise Mode and target detection
It is applied in the robot cluster of mode.Specific embodiments of the present invention are described above.It is to be appreciated that of the invention
Be not limited to above-mentioned particular implementation, those skilled in the art can make within the scope of the claims various modifications or
Modification, this is not affected the essence of the present invention.
Claims (6)
1. a kind of Multi-agent Team Formation, which is characterized in that include the following steps：
Step 1：Set detection income calculation method relevant to position；
Step 2：Define the concrete behavior of robot；
Step 3：The number of channels for determining basal ganglion establishes basal ganglion channel pattern, initializes relevant parameter；
Step 4：Correct the channel pattern parameter of basal ganglion.
2. Multi-agent Team Formation according to claim 1, which is characterized in that the step 1 is specially：
Robot is comprehensively considered to the sum of the distance of movement for forming formation target position dissum, the suitable mobile speed of robot
Spend vi, robot is adjusted to the time T of object-oriented postureadjust, the time T of robot motion to target pointmove, robot
The detection income of in-position, can obtain after the next sampling period：
Tcost=Tmove+Tadjust；
(1) income area in target acquisition position is set：Actual conditions are：It is circular several piece fan that income area, which is with target acquisition center,
Shape region；
(2) income calculation method is detected：In the closer sector in income area, set the test point income of the robot as
Fixed income relevant to target point significance level；In the farther away annulus in income area, the detection of the robot is set
Point income is the dynamic income that distance is inversely proportional between robot and target point, after the completion of detection, resets the inspection of current work
Survey income.
3. Multi-agent Team Formation according to claim 1, which is characterized in that the step 2 is specially：According to machine
The specific structure of people and function define robot after the completion of action selection it is possible that concrete behavior, to each behavior
Excitation inhibition relationship carries out limitation and sets analysis, if a certain robot behavior can be excited, uses basal ganglion channel
Model is verified；If the robot behavior being excited can execute this behavior by verifying.
4. Multi-agent Team Formation according to claim 1, which is characterized in that in the step 3, basal ganglia joint number
Learning model includes corpus straitum, globus pallidus outer core, subthalamic nucleus, core group globus pallidus kernel.
5. Multi-agent Team Formation according to claim 1, which is characterized in that the step 3 is specially：(1) it establishes
The mathematical model in behavior channel：Each channel is indicated with a leakage integral neuron：
In above formula, x is the state of neuron, and u is the input of neuron, and y is the output of neuron, and H is jump function, remaining is
Model parameter；
yi C=Si
Cerebral cortex integration, processing obtain combining the synthesis importance index S of each factor in step 1i, i is channel number in formula,
yi CIndicate the output in i-th of channel in cerebral cortex；The state of corpus straitum D1 can be described as:
In above formula, ui SD1It is corpus straitum D1, i is that the neuron in channel inputs, ai SD1It is the state of the channel neuron, yi SD1It is
The output of the channel neuron, wCSD1It is weight of the cerebral cortex to corpus straitum D1；1+ λ-description is dopamine to corpus straitum D1
Incentive action, εSD1For the output threshold value of corpus straitum D1；The state of corpus straitum D2 can be described as:
In above formula, ui SD2It is corpus straitum D2, i is that the neuron in channel inputs, ai SD2It is the state of the channel neuron, yi SD2It is
The output of the channel neuron, wCSD2It is weight of the cerebral cortex to corpus straitum D2,1- λ-description is dopamine to corpus straitum D2
Inhibiting effect, εSD2For the output threshold value of corpus straitum D2；Globus pallidus outer core can be described as：
In above formula, ui GPeIt is globus pallidus outer core, i is that the neuron in channel inputs, ai GPeIt is the state of the channel neuron, yi GPe
It is the output of the channel neuron, wSD2GPeIt is weight of the corpus straitum D2 to globus pallidus outer core, εGPeIt is the output of globus pallidus outer core
Threshold value；The model of subthalamic nucleus can be described as:
In above formula, ui STNIt is subthalamic nucleus, i is that the neuron in channel inputs, ai STNIt is the state of the channel neuron, yi STNIt is
The output of the channel neuron, wGPeSTNIt is weight of the globus pallidus outer core to subthalamic nucleus, εSTNIt is the output threshold value of subthalamic nucleus；
It enables the input of globus pallidus kernel go out including the subthalamic nucleus in other left and right channels, can obtain：
In above formula, ui GPiIt is globus pallidus kernel, i is that the neuron in channel inputs, ai GPiIt is the state of the channel neuron, yi GPi
It is the output of the channel neuron, wSD1GPiIt is weight of the corpus straitum D1 to globus pallidus kernel, wSTNGPiIt is subthalamic nucleus to pale
The output weight of ball kernel, εGPiIt is the output threshold value of globus pallidus kernel, wCSD1、wCSD2、wSTNGPiFor positive value, indicate that incentive connects
It connects, wSD1GPi、wSD2GPe、wGPeSTNFor negative value, inhibition connection is indicated.
6. Multi-agent Team Formation according to claim 1, which is characterized in that the step 4 is specially：Work as robot
Overall operation situation when being deviated under conditions of using user preference as standard, parameters are carried out to model in step 3
Correction.
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| CN110119151A (en) * | 2019-05-24 | 2019-08-13 | 陕西理工大学 | A kind of target point distribution method of robot formation forming |
| CN111077887A (en) * | 2019-12-12 | 2020-04-28 | 南京理工大学 | Multi-robot comprehensive obstacle avoidance method adopting piloting following method |
| CN111082709A (en) * | 2019-12-18 | 2020-04-28 | 南京理工大学 | Electric anti-backlash control method based on basal ganglia |
| CN111207754A (en) * | 2020-02-28 | 2020-05-29 | 上海交通大学 | Particle filter-based multi-robot formation positioning method and robot equipment |
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| CN110119151A (en) * | 2019-05-24 | 2019-08-13 | 陕西理工大学 | A kind of target point distribution method of robot formation forming |
| CN110119151B (en) * | 2019-05-24 | 2022-02-11 | 陕西理工大学 | Target point distribution method for formation of robot formation |
| CN111077887A (en) * | 2019-12-12 | 2020-04-28 | 南京理工大学 | Multi-robot comprehensive obstacle avoidance method adopting piloting following method |
| CN111077887B (en) * | 2019-12-12 | 2022-06-28 | 南京理工大学 | Multi-robot comprehensive obstacle avoidance method adopting piloting following method |
| CN111082709A (en) * | 2019-12-18 | 2020-04-28 | 南京理工大学 | Electric anti-backlash control method based on basal ganglia |
| CN111207754A (en) * | 2020-02-28 | 2020-05-29 | 上海交通大学 | Particle filter-based multi-robot formation positioning method and robot equipment |
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