CN115167440A - Virtual navigation-following-based multi-robot formation control method - Google Patents
Virtual navigation-following-based multi-robot formation control method Download PDFInfo
- Publication number
- CN115167440A CN115167440A CN202210884676.6A CN202210884676A CN115167440A CN 115167440 A CN115167440 A CN 115167440A CN 202210884676 A CN202210884676 A CN 202210884676A CN 115167440 A CN115167440 A CN 115167440A
- Authority
- CN
- China
- Prior art keywords
- robot
- formation
- virtual
- following
- azimuth
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000015572 biosynthetic process Effects 0.000 title claims abstract description 265
- 238000000034 method Methods 0.000 title claims abstract description 46
- 230000009466 transformation Effects 0.000 claims abstract description 32
- 230000008859 change Effects 0.000 claims abstract description 15
- 238000005457 optimization Methods 0.000 claims abstract description 9
- 230000006870 function Effects 0.000 claims description 34
- 238000006243 chemical reaction Methods 0.000 claims description 19
- 230000004888 barrier function Effects 0.000 claims description 14
- 230000008878 coupling Effects 0.000 claims description 12
- 238000010168 coupling process Methods 0.000 claims description 12
- 238000005859 coupling reaction Methods 0.000 claims description 12
- 230000002159 abnormal effect Effects 0.000 claims description 8
- 230000007613 environmental effect Effects 0.000 claims description 8
- 239000000284 extract Substances 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 6
- 238000006073 displacement reaction Methods 0.000 claims description 5
- 230000002194 synthesizing effect Effects 0.000 claims description 3
- 230000008439 repair process Effects 0.000 claims description 2
- 238000012217 deletion Methods 0.000 claims 1
- 230000037430 deletion Effects 0.000 claims 1
- 238000005755 formation reaction Methods 0.000 description 217
- 239000000126 substance Substances 0.000 description 11
- 238000011156 evaluation Methods 0.000 description 9
- 238000010586 diagram Methods 0.000 description 8
- 230000008569 process Effects 0.000 description 8
- 238000005265 energy consumption Methods 0.000 description 7
- 230000000694 effects Effects 0.000 description 6
- 239000011159 matrix material Substances 0.000 description 6
- 230000009471 action Effects 0.000 description 3
- 230000008901 benefit Effects 0.000 description 3
- 238000004422 calculation algorithm Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 3
- 150000001875 compounds Chemical class 0.000 description 3
- 229910003460 diamond Inorganic materials 0.000 description 3
- 239000010432 diamond Substances 0.000 description 3
- 108010074864 Factor XI Proteins 0.000 description 2
- 239000003795 chemical substances by application Substances 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000013178 mathematical model Methods 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000008602 contraction Effects 0.000 description 1
- 238000001816 cooling Methods 0.000 description 1
- 230000000249 desinfective effect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 239000006185 dispersion Substances 0.000 description 1
- 239000002360 explosive Substances 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 230000002262 irrigation Effects 0.000 description 1
- 238000003973 irrigation Methods 0.000 description 1
- 238000012886 linear function Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000001988 toxicity Effects 0.000 description 1
- 231100000419 toxicity Toxicity 0.000 description 1
- 238000011426 transformation method Methods 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- 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
- G05D1/0287—Control of position or course in two dimensions specially adapted to land vehicles involving a plurality of land vehicles, e.g. fleet or convoy travelling
- G05D1/0291—Fleet control
- G05D1/0295—Fleet control by at least one leading vehicle of the fleet
Abstract
The invention relates to a virtual-piloting-following-based multi-robot formation control method, which comprises the following steps: establishing a virtual navigation-following motion structure; the method comprises the following steps that a piloting robot tracks the position of a virtual piloting robot in real time, and according to detected and extracted environment information including road boundaries and obstacles, formation transformation dynamic optimization is carried out to obtain an expected distance and an expected azimuth angle between a following robot and the virtual piloting robot; performing motion control on a plurality of robots in formation through a distributed and cooperatively coupled formation controller; enabling each robot to track the real-time position of the virtual pilot robot; converging the expected distance and azimuth angle between the pilot robot and the virtual pilot robot to zero through tracking; the distance and azimuth between the following robot and the virtual pilot robot are converged to a desired relative distance and azimuth. The invention can enable multiple robots to form a formation to set formation reference track to avoid obstacle travelling and formation change.
Description
Technical Field
The invention belongs to the technical field of robot formation control, and particularly relates to a virtual navigation-following-based multi-robot formation control method.
Background
In recent years, fire-fighting robots have also been increasingly used in actual combat. However, from the application effect, most of the existing fire-fighting robots need to rely on remote operation of background firefighters, and technical challenges such as narrow detection view, poor man-machine interaction, limited fire source positioning and the like exist, so that the existing fire-fighting robots cannot adapt to more complex disasters. Aiming at the fire protection requirements under complex fire environments of strong interference, high dynamic state and the like, in order to realize task capability expansion and overall fire protection efficiency improvement of a robot system, a plurality of intelligent fire protection robot cooperative operation modes are adopted to realize capability complementation and action coordination, and the intelligent fire protection robot system becomes a main development direction of future fire protection application. When the intelligent group fire-fighting robot executes the formation tracking task, due to the fact that various unknown obstacles exist on a running road and are restrained by edges on two sides of the road, the robot is guaranteed to run in a safe area and fast avoids the obstacles in the front in the running process, and meanwhile when the width of a channel changes, the formation robot can dynamically adjust the formation shape to pass through the channel in an optimal configuration mode. Therefore, an efficient, safe and reliable formation obstacle avoidance algorithm is needed to improve the flexibility of formation obstacle avoidance and the stability of a formation structure.
Disclosure of Invention
In view of the above analysis, the present invention aims to disclose a multi-robot formation control method based on virtual pilot-follow, which is used for solving the formation control problem of multiple robots.
The invention discloses a virtual navigation-following-based multi-robot formation control method, which comprises the following steps of:
establishing a virtual navigation-following motion structure; appointing one robot in the formation as a pilot robot, and the rest as following robots, and setting a virtual pilot robot to travel with the formation on a formation reference track point;
the method comprises the following steps that a piloting robot tracks the position of a virtual piloting robot in real time, and dynamic optimization of formation transformation is carried out according to detected and extracted environment information including road boundaries and obstacles, so that an expected distance and an expected azimuth angle between a following robot and the virtual piloting robot are obtained to adapt to the current environment;
performing motion control on a plurality of robots in formation through a distributed and cooperatively coupled formation controller; enabling each robot to track the real-time position of the virtual pilot robot; converging the expected distance and azimuth angle between the pilot robot and the virtual pilot robot to zero through tracking; the distance and azimuth between the following robot and the virtual pilot robot are converged to a desired relative distance and azimuth.
Further, the method for acquiring the expected distance and the expected azimuth angle between the following robot and the virtual pilot robot comprises the following steps:
the robot formation follows the virtual pilot robot to advance in an expected initial queue configuration;
the piloting robot detects and extracts road boundary information and obstacle information by using an environment sensing sensor carried by the piloting robot, and calculates a telescopic factor of a formation;
and determining a dequeue form conversion command according to the expansion factor of the form, and distributing the dequeue form conversion command to the following robot, so that the following robot obtains an expected distance and an expected azimuth angle between the following robot and the virtual pilot robot.
Furthermore, the navigation robot tracks the position of the virtual navigation robot in real time, perceives environmental information in real time through a multi-line laser radar carried by the navigation robot, extracts the boundary of a road, calculates the width of the road on two sides, and detects and identifies barrier information; and using the detected partial barrier boundary points as candidate boundary points, and performing missing repair on the road boundary points to obtain final boundary point information.
Further, the expansion layer is arranged by carrying out expansion processing on the contour of the road boundary and the obstacle, so that the distance between the robot in the formation and the road boundary and the obstacle is larger than the width of the expansion layer.
Further, the piloting robot takes the ratio between the measured minimum passing width and the expected formation width of the initial queue configuration as the expansion and contraction factor of the formation.
Further, determining the formation transformation command according to the scaling factor of the formation comprises:
when the expansion factor is larger than 1, outputting a queue form zero transformation instruction to enable the formation to keep the original queue form to continue to advance;
when the scaling factor is within a value interval of a first threshold value and 1; outputting a formation isomorphic conversion instruction to enable the formation to pass through a front channel after the formation contracts;
when the scaling factor is in the value range of the second threshold and the first threshold; outputting a queue shape heterogeneous conversion instruction to enable the formation to be converted into a new queue shape to pass through a front channel;
and when the expansion factor is not greater than the second threshold value, outputting a formation abnormal state instruction, and waiting for a next instruction when the formation cannot pass through a front channel.
Further, the formation control method of the distributed cooperative coupled formation controller includes:
determining the relative displacement and the relative speed between the formation robot and the virtual pilot robot according to the first-order integrator models of the virtual pilot robot and the formation robot;
establishing a circular motion control law, and guiding each formation robot to converge on a circle which takes the virtual pilot robot as a center and takes an expected distance between the formation robot and the virtual pilot robot as a radius in a smooth circular motion mode;
coupling a control law of azimuth positioning on the basis of a circular motion control law; and performing formation control with an obstacle avoidance function on each formation robot according to a circular motion control law and an obstacle avoidance control law which are coupled with azimuth positioning.
Further, the obstacle avoidance control law generates a repulsive force by setting a repulsive force potential field to avoid the obstacle.
Further, in the setting of the repulsive force field, the travel area of the robot is limited by establishing the repulsive force velocity acting on the road boundary; synthesizing the repulsive force speed acted by the barrier and the repulsive force speed acted by the road boundary to obtain a repulsive force resultant speed; and outputting a final obstacle avoidance control law by adjusting the direction of the repulsive force closing speed so as to solve the problems of local minimum value and unreachable target and enable the formation robot to avoid obstacles in a safe area of the road.
Further, a dynamic weight factor is established, and the ratio of the circular motion control law coupled with azimuth positioning to the obstacle avoidance control law is adjusted to obtain the control law finally output by the distributed cooperative coupling formation controller.
The invention can realize at least one of the following beneficial effects:
the invention discloses a virtual navigation-following-based multi-robot formation control method, which solves the problem of formation control of multiple robots.
The invention adopts a virtual navigation-following mode, and the navigation robot is not only responsible for the planning and coordination of the whole system, but also does not influence the motion state of other robots. The problems that tracking errors are accumulated easily and the whole system is broken down due to the fact that a following random device in a chain structure of a traditional navigator-following mode excessively depends on a navigator machine are solved.
The invention establishes the expansion factor of the formation, and also considers the abnormal state condition on the basis of the conventional formation conversion state, namely the condition when the width of the current square channel does not meet the passing condition of the formation individual, and needs to wait for the next step of instruction; the comprehensive performance evaluation index function for measuring the advantages and disadvantages of the formation transformation strategy is provided, the index can be independent of a dynamic model of the system, the expression form is simple and clear, and the comprehensive performance evaluation index function not only can be suitable for time-varying formation transformation of multiple fire-fighting robots, but also can be suitable for other multi-agent systems.
From the practical application, the invention considers that the road boundary and the obstacle parked at the roadside collide with the robot, and the robot keeps a safe distance with the road boundary and the obstacle as far as possible by adding expansion processing to the contour of the road boundary and the obstacle.
The invention establishes a distributed cooperative coupling formation controller, establishes a control law for increasing azimuth positioning on the basis of a circular motion control law, is combined with an obstacle avoidance control law, and can effectively avoid obstacles in the process of advancing according to the formation form.
The invention sets the repulsive force potential field to generate the repulsive force to avoid the barrier, can realize the barrier avoiding control of the nonlinear coupling dynamic system with high uncertainty by a very simple algorithm without depending on an accurate mathematical model of the system, and realizes the high-efficiency, safe and reliable formation barrier avoiding. And the road boundary effect is also taken into consideration, so that the robot can effectively avoid obstacles in a safe area of a road, and the problems of local optimization and unreachable target in the traditional artificial potential field method are effectively solved.
Drawings
The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention, wherein like reference numerals are used to designate like parts throughout.
FIG. 1 is a flowchart of a multi-robot formation control method based on virtual piloting-following according to an embodiment of the present invention;
FIG. 2 is a diagram of a coordinate system in an embodiment of the present invention;
FIG. 3 is a schematic diagram of a multi-robot formation geometry in an embodiment of the present invention;
FIG. 4 is a diagram illustrating an example of formation shapes in an embodiment of the present invention;
FIG. 5 is a schematic diagram of a path encountered during multi-robot formation walking in an embodiment of the present invention;
FIG. 6 is a flowchart of dynamic optimization of formation change in an embodiment of the present invention;
FIG. 7 is a diagram illustrating a problem that a local optimum and an object are unreachable in a conventional artificial potential field method according to an embodiment of the present invention;
FIG. 8 is a diagram illustrating an effect of an artificial potential field method according to an embodiment of the present invention;
FIG. 9 is a plot of the logarithmic barrier function in an embodiment of the present invention;
fig. 10 is a block diagram of a distributed and cooperatively coupled formation controller according to an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form a part hereof, and which together with the embodiments of the invention serve to explain the principles of the invention.
One embodiment of the invention discloses a virtual navigation-following-based multi-robot formation control method, which comprises the following steps as shown in figure 1:
s101, establishing a virtual navigation-following motion structure; one robot in the formation is designated as a pilot robot, the other robots are designated as following robots, and a virtual pilot robot is set to travel with the formation on a formation reference track point;
s102, the navigation robot tracks the position of the virtual navigation robot in real time, and performs formation transformation dynamic optimization according to detected and extracted environment information including road boundaries and obstacles to obtain an expected distance and an expected azimuth angle between the following robot and the virtual navigation robot so as to adapt to the current environment;
step S103, performing motion control on a plurality of robots in the formation through a distributed cooperative coupling formation controller; enabling each robot to track the real-time position of the virtual pilot robot; converging the expected distance and azimuth angle between the pilot robot and the virtual pilot robot to zero through tracking; the distance and azimuth between the following robot and the virtual pilot robot are converged to a desired relative distance and azimuth.
Consider a multi-robot formation consisting of n (n ≧ 2) fire-fighting robots. Due to the fact that the follower in the traditional navigator-follower mode excessively depends on the navigator, the chain structure easily causes the problems that tracking errors are accumulated, the whole system is broken down due to the fact that the navigator breaks down, and the like, and a virtual navigator-follower motion structure is established in the embodiment.
Specifically, in the present embodiment, formation control of a plurality of fire-fighting robot formations is taken as an example. The fire-fighting robot uses the EPSILON-D4G module (RTK) assisted by the built-in double-difference GNSS +4G module in an outdoor scene, and can realize stable centimeter-level positioning through a thousand-seek service. The positioning information provided by the EPSILON-D4G module comprises longitude, latitude, altitude, heading angle and the like. In order to facilitate the pose representation of the robot, the longitude, latitude and height data measured by RTK are required to be converted into the position of the robot under an inertial coordinate system, and the course angle is required to be converted into the yaw angle of the robot under a navigation coordinate system.
In order to describe the motion state of the fire fighting robot more accurately and conveniently, a suitable coordinate system and a conversion relation between coordinate systems are defined, and the defined coordinate system is shown in fig. 2. Where, Σ { O e ,X e ,Y e ,Z e Is the earth-centered-earth-fixed coordinate system (ECEF) with the origin of coordinates O e Coincident with the center of the earth, X e And Y e Lying in the equatorial plane but pointing towards the initial meridian and the east 90-degree meridian, Z respectively e The axis of rotation of the earth coincides with the axis of rotation of the earth; sigma { O n ,X n ,Y n ,Z n Fixedly connecting an inertial coordinate system with the ground, and establishing according to an northeast Earth (ENU) coordinate system, namely three axes X n 、Y n 、Z n Pointing to the east, north and above of geography respectively. II { O r ,X r ,Y r ,Z r Is a body coordinate system of the fire-fighting robot, wherein O r Is fixedly connected with the geometric center of the robot and has three axes X r ，Y r ，Z r And selecting according to the directions of the front, the left and the right of the robot movement respectively.
Firstly, the longitude and latitude and altitude data are converted into an ECEF Cartesian position through ellipsoid parameter conversion, and the conversion formula is as follows:
wherein λ is B 、λ L 、λ H Respectively representing the measured latitude, longitude and height, N is the curvature radius of the prime circle, L а And e represents the major semi-axis and the first eccentricity, respectively, of the earth's ellipse. Wherein L is а ＝6378137±2m，e 2 ＝0.0066943799013m。
Then, the ECEF coordinate system is transformed to the inertial coordinate system, and the transformation relationship is as follows:
wherein the content of the first and second substances,
as a reference point
Position under ECEF.
The position of the fire-fighting robot under the inertial coordinate system by taking the reference point as the origin can be obtained through the three formulas. Meanwhile, in order to obtain the yaw angle theta epsilon [ -pi, pi) of the robot under the inertial coordinate system, the north-pointing heading angle psi ∈ [0,2 pi) needs to be converted into an absolute yaw angle, and the conversion relation is as follows:
specifically, the step S101 of establishing the virtual navigation-following motion structure includes:
1) Establishing a multi-robot formation geometric structure;
as shown in fig. 3; wherein, the virtual pilot robot R 0 Traveling along with formation on formation reference track points and virtually piloting robot R 0 The motion track of the device consists of a series of continuously guided track points; appointing one robot as a piloting robot R 1 Tracking the position of the virtual piloting robot in real time, i.e. meeting the convergence of the desired distance and azimuth angle between the piloting robot and the virtual piloting robot to zero, i.e.
In addition, the other robots are following robots R i (i is more than or equal to 2), and connecting each following robot with a pilot robot R 1 Medical insuranceTo a desired distance
And azimuth angle
Conversion to and virtual piloting robot R 0 Maintaining corresponding expected values; the advantage of the formation structure design is that the piloting robot R 1 The system is not only responsible for planning and coordinating the whole system, but also does not influence the motion state of other robots.
Wherein the distance between the robot i and the virtual navigator is l i Not less than 0, in azimuth
The calculation formula is as follows:
wherein the content of the first and second substances,
x 0 ,y 0 is the position coordinate, x, of the virtual piloting robot in the inertial coordinate system i ,y i Position coordinates of the robot i in an inertial coordinate system;
according to the relative position of the robot i and the virtual pilot robot, a smooth arctangent function atan2 (y, x) is used for obtaining a unique value with the range of [ -pi, pi), and then a g (atan 2 (y, x)) function is used for converting the atan2 (y, x) range to [0,2 pi ].
Each robot was modeled as a first order integrator model:
wherein the position of the robot i in the inertial coordinate system
Is a control input to robot i, where u ix And u iy For it to be X under the inertial coordinate system n Direction and Y n A control input component of direction.
The virtual piloted robot is also modeled as a first order integrator model:
wherein the content of the first and second substances,
and the position and the speed of the virtual pilot robot in the inertial coordinate system.
Defining the relative displacement between the robot i and the virtual navigator as follows:
defining the relative speed between the robot i and the virtual pilot as:
define the distance between the robot i and the virtual pilot as
In an azimuth of
Define the expected distance between the robot i and the virtual pilot as
The desired azimuth angle is
Writing the expected distance and direction angle between the robot i and the virtual pilot into a vector form as follows:
if it is satisfied with
The formation structure is designed to be
2) Establishing a knowledge base of formation structure of multi-robot formation
The establishment of the formation structure of the multi-fire-fighting robot system is the basis for researching the multi-robot formation transformation, and the mathematical calculation in the multi-robot formation transformation process can be greatly simplified by establishing the mathematical relation on the spatial position of some typical formation structures. In the construction of the formation shape, a straight line (line), a wedge (wedge), a column (column), a triangle (triangle), a diamond (diamond), a circle (circle), etc. are mainly considered, as shown in fig. 4, and o is a formation target point, such as a fire source.
Firstly, suppose that n fire-fighting robots and a planned reference track (virtual pilot robot) are provided, a pilot robot in a certain robot formation in the formation is designated, and the other robots are following robots. Using direction in graph theoryThe acyclic graph is used for describing a telescopic formation, each robot is regarded as a vertex, and the relationship between two robots is regarded as an edge. Each robot has a unique ID number, where the virtual pilot robot is set to R 0 The pilot is set as R 1 The other following robots are sequentially set as R 2 ，R 3 ，…，R n . In order to represent the interrelationship between the robots and the shape parameters of the formation, the general formula of the formation parameter matrix is defined as follows:
F i d ＝[f i1 ,f i2 ,f i3 ] (10)
wherein, F d Is a parameter information matrix of a certain formation shape,
is the ID number of the robot. Each robot is represented by 3 sets of parameters, f i1 Number for robot i, f i2 For the expected distance to be kept between the robot i and the virtual pilot robot
f i3 For the desired azimuth between the robot i and the virtual piloting robot
The formation shape of the fire-fighting robot formation can be described as:
for the several typical formation shape, a formation knowledge base is established by different formation parameter matrixes, and the expected formation parameter matrix is expressed as follows:
(1) A word formation:
(2) A columnar formation:
(2) A triangular formation:
(4) Wedge formation:
(5) A diamond formation:
(6) A circular formation:
the above-mentioned established expected formation parameter matrix does not represent all structures of such formations, but is a special case of such shapes, such as a straight formation, and a new straight formation can be obtained by adjusting the sequence of the robots or the distance between each robot and the virtual pilot robot, but all formations can be adjusted
And
thus obtaining the product.
3) Selecting a formation form, acquiring formation form parameters from a multi-robot formation form structure knowledge base, and performing formation control to realize that a multi-robot system moves in an appointed formation form;
if the multi-fire-fighting robot system is required to move in a designated formation and effective formation transformation can be carried out according to the sensed environmental change, the pilot robot can track a series of track points planned in advance according to the formation controller with feedback control, and expected distance and azimuth angles are kept between each robot and the virtual pilot robot, namely the requirement of meeting the requirement
In a specific aspect of this embodiment, the multi-robot formation system is operated in coordination with the outdoor complex and varied environment, such as petrochemical plant, gas field, oil irrigation area, airport subway, warehouse, forest way and other high risk, high temperature, toxicity, strong radiation, flammability, explosive place and when the easily collapsed fire truck and personnel are not accessible, the fire fighting robot needs to be dispatched to perform its duties and enter the dangerous area for fire fighting and rescue, investigation and rescue, and cooling and disinfecting the chemical pollution place. The areas where the robots are grouped to walk are mostly narrow passages, galleries or passages with obstacles. Taking three robots as an example, a schematic diagram of channels encountered during the formation walking process of the multiple robots is analyzed, as shown in fig. 5.
In the embodiment, environmental information is sensed in real time by the aid of multi-line laser radars carried by pilot robots and following robots in the formation; wherein the content of the first and second substances,
the navigation robot tracks the position of the virtual navigation robot in real time, and the boundary of the structured road is extracted and the obstacle is detected and identified through the real-time perception environment information of the multi-line laser radar carried by the navigation robot.
The following robot and the piloting robot form a specific formation to execute formation tasks, and meanwhile, the self-carried multi-line laser radar is used for detecting and identifying obstacles.
In order to avoid obstacles according to environmental constraints, the formation width can be dynamically adjusted to adapt to different road widths, and the comprehensive performance of the multi-fire-fighting robot system for adapting to the current environment through formation change is optimal.
In step S102, the navigation robot tracks the position of the virtual navigation robot in real time, and performs formation transformation dynamic optimization according to the detected and extracted environment information including road boundaries and obstacles to obtain an expected distance and an expected azimuth angle between the following robot and the virtual navigation robot so as to adapt to the current environment;
the method specifically comprises the following steps:
1) The robot formation follows the virtual pilot robot to advance in an expected initial queue configuration;
specifically, the robot group quickly forms an expected initial configuration through the formation controller, and the robot group moves along with the reference track to execute a formation following task;
2) The piloting robot detects and extracts road boundary information and obstacle information by using an environment sensing sensor carried by the piloting robot, and calculates the expansion factor of the formation;
specifically, the navigation robot tracks the position of the virtual navigation robot in real time, and senses environmental information in real time through a multi-line laser radar carried by the navigation robot, extracts the boundary of a road, calculates the width of the road on two sides, and detects and identifies obstacle information; and the detected boundary points of partial obstacles are used as candidate boundary points for further discrimination, and the missing road boundary points are repaired to obtain boundary point information.
And the expansion layer is arranged by carrying out expansion processing on the contour of the road boundary and the obstacle, so that the distance between the robot and the road boundary and the obstacle in the formation is larger than the width of the expansion layer. Preferably, the width of the expansion layer is B/2; and B is the width of the robot.
In an actual road scene, since other obstacles such as vehicles stop at the road boundary position, and thus a part of rays emitted by the laser radar is blocked, and a complete road boundary point cannot be obtained, the detected boundary point of the part of obstacles can be used as a candidate boundary point, and further judgment can be made to "patch" the missing road boundary point, as shown in fig. 5. As can be seen from fig. 5, the positions of the road boundary points detected by the piloting robot by using the laser radar are as follows:
wherein the content of the first and second substances,
and
are respectively the first
And (4) measuring the distance and the angle obtained by the road boundary by using the effective laser.
Considering that the laser radar may be limited by the installation position, and only the front road width and boundary points need to be extracted during formation traveling, and data processing does not need to be carried out on the rear. Therefore, only the range of the angle alpha epsilon [ alpha ] of the laser radar in a specific angle range is required to be taken min ,α max ]The effective data of the method can not only eliminate some abnormal data and reduce the calculation amount, but also leave enough time for the formation adjustment.
Defining the nearest boundary point
A distance of
The scanning angle of the laser radar corresponding to rho is alpha, and then the scanning angle has the condition that beta = alpha-pi/2 + theta i And then obtain
Forward velocity v of robot to pilot 1 The vertical distance of (a) is:
L＝ρ|sinβ| (13)
definition D min =2L is the minimum passage width of the robot.
The piloting robot measures the minimum passing width D min And a desired queue width D of the initial queue configuration 0 The ratio of the two is used as the scaling factor xi of the formation;
wherein the content of the first and second substances,
in the formula (I), the compound is shown in the specification,
and
respectively at an initial time t 0 The desired distance and the desired azimuth angle between the time-following robot 1 and the virtual pilot robot.
3) And determining a dequeue form conversion command according to the expansion factor of the form, and distributing the dequeue form conversion command to the following robot, so that the following robot obtains an expected distance and an expected azimuth angle between the following robot and the virtual pilot robot.
Determining a formation transformation command sigma according to the scaling factor xi of the formation comprises the following steps:
wherein the first threshold value
Second threshold value
Sigma is equal to {0,1,2,3} is a queue shape transformation mode instruction,
the piloting robot issues the following four instructions according to the calculated xi to dynamically adjust the formation to adapt to the change of different channel widths:
(1) When the expansion factor is larger than 1, outputting a queue form zero transformation instruction sigma =0, indicating that a front channel is wide enough to enable the formation to keep the original queue form to continue to advance; at this time, the formation width is set to D = D 0 。
(2) When the scaling factor is within a value interval of a first threshold value and 1; outputting a formation isomorphic transformation instruction sigma =1, indicating that the robot formation continues to advance according to the original formationPossibly colliding with the road boundary, so that the formation contracts and then passes through a front passage; at this time, the formation width is set to D = D 0 ξ-B。
(3) When the scaling factor is in the value range of the second threshold and the first threshold; outputting a queue shape heterogeneous transformation instruction sigma =2, and enabling the formation to be transformed into a new queue shape to pass through a front channel;
indicating that the width of the road ahead only allows a single robot to pass through, and the columnar formation must be changed to be able to pass through. At this time, the formation width is set to D = B.
(4) And when the scaling factor is not greater than the second threshold value, outputting a queue abnormal state instruction sigma =3, and waiting for a next instruction when the formation cannot pass through a front channel. Namely, the master control platform is waited to issue whether to finish the formation task or execute a new formation path instruction.
From this, the expected distance and the expected azimuth angle between the pilot robot and the virtual pilot robot are as follows:
wherein, the first and the second end of the pipe are connected with each other,
and (4) representing an abnormal value, exiting the current formation task by the piloting robot, and waiting for a next instruction.
The expected distance and the expected azimuth angle between each following robot and the virtual pilot robot are as follows:
wherein the content of the first and second substances,
wherein i belongs to {2,3};
and (4) representing an abnormal value, exiting the current formation task along with the robot, and waiting for a next instruction. The g (-) function represents the transformation of the domain [ - π, π) to [0,2 π).
The specific queue shape transformation dynamic optimization flow is shown in fig. 6.
In order to further evaluate the performance of the time-varying formation transformation method, a new evaluation index function is established in the embodiment, and the formation transformation performance evaluation function comprises an energy loss rate, a formation adjustment time ratio and a formation distortion degree. Specifically, the evaluation index function is designed as follows:
(1) Energy loss function
At present, the robot is mostly powered by an onboard battery, and the consumption of electric energy plays a decisive role in the time for the robot to execute a task. And the smaller energy consumption in the formation conversion process can provide certain guarantee for the robot to realize continuous work for a longer time, which has important practical significance for the execution of the actual multi-robot formation task. The energy function of robot i can be modeled as:
wherein, the first and the second end of the pipe are connected with each other,E i (t) is the energy of the robot i at time t; z is a radical of i ：
Is a non-linear mapping.
Since the energy loss can be essentially represented by the length of the driving path, the energy consumption is zero when the robot is at rest, and the energy of the robot at the initial moment is
δ i Is the standard rate of energy consumption per unit length of travel. The energy function can be described as:
in the formula, v i Is the speed of robot i.
Then, robot i is at time [ t ] 0 ,t]The total energy consumption is:
then, the formation is at time [ t ] 0 ,t]The total energy loss rate function of the inner formation transform is set as:
in the formula,. DELTA.E r (t) and. DELTA.E t (t) are respectively the time of formation [ t ] 0 ,t]Actual energy consumption and theoretical energy consumption.
From this, f is e Smaller values of (t) indicate less energy consumption and longer time for the robot to continue operating.
(2) Formation adjustment time function
When the formation receives a formation change command, a certain adjustment time is required for changing from the current formation to the target formation, and in order to make the adjustment time as short as possible, the reason is thatIn this case, the queue form adjustment time efficiency is also one of the influencing factors that influence the efficiency of the whole task execution of the formation. The queue form adjustment time ratio is used as an important index for evaluating queue form transformation, and on one hand, the queue form adjustment time ratio can be used for describing the dispersion degree of the queue form structure; on the other hand, reflecting the response speed of formation change, the formation shape needs to be changed for a plurality of times when the formation passes through channels with different widths, and the convergence speed of the formation change from the current formation shape to the target formation shape is reflected from the side by the ratio of the total time of formation adjustment to the total running time. Then, the formation is at time [ t ] 0 ,t]The inner formation adjustment time ratio function is set as:
in the formula (I), the compound is shown in the specification,
is the number of times of conversion;
for robot i at time [ t 0 ,t]Total number of transitions; t is t j,s And t j,e Respectively the starting time and the ending time of the robot changing from the current formation to the target formation at the j-th change.
From this, f is r The smaller the value of (t), the less time it takes to represent the formation transformation, the higher the transformation efficiency.
(3) Distortion function of formation structure
Due to the change of the channel width in the process of advancing, the multi-robot system needs to change a proper formation to adapt to the environmental change, and the formation deformation of the multi-robot system is required to be as small as possible for the maximum optimized formation. And describing the distortion degree of the current formation and the expected formation of the formation by using the formation distortion degree as one of the functions for evaluating the performance of the formation transformation. The formation structure distortion function is:
from this, f is s Smaller values of (t) indicate less distortion of the formation, closer to the desired formation.
Because different multi-robot systems have different requirements on each performance function, for example, for a multi-fire-fighting robot system, the distortion degree of the formation is higher compared with the weight of the energy loss and the formation adjusting time, a weight factor is set for each performance function to distinguish the importance degree of each performance index. Then, the performance comprehensive evaluation index function of the formation transformation of the multi-robot system is as follows:
f(t)＝λ e f e (t)+λ a f a (t)+λ s f s (t) (26)
wherein λ is e ＞0、λ r ＞0、λ s The weight factors of the energy loss function, the formation adjusting time function and the formation stability function are more than 0 respectively, and the requirement of lambda is met e +λ r +λ s ＝1。
The environmental fitness function f (t) can be used as a comprehensive efficiency evaluation index for measuring the quality of the formation transformation strategy. Smaller values indicate a more optimal formation transformation strategy.
Specifically, the method for controlling the movement of the plurality of robots in the formation through the distributed and cooperatively coupled formation controller in step S103 includes:
1) Determining the relative displacement and the relative speed between the formation robot and the virtual pilot robot according to the first-order integrator models of the virtual pilot robot and the formation robot;
according to the first-order integrator models of the virtual pilot robot and the formation robot, determining that the relative displacement between the robot i and the virtual pilot is as follows:
the relative speed between robot i and the virtual navigator is:
2) Establishing a circular motion control law, and guiding each formation robot to converge to a circle which takes the virtual pilot robot as a center and takes an expected distance between the formation robot and the virtual pilot robot as a radius in a smooth circular motion mode;
specifically, the circular motion control law guides each robot to converge on a circle formed by taking the virtual pilot robot as a center in a smooth circular motion mode, and does not consider the specific position on the circle. The control is effected by rotating a fixed reference vector to target point p 0 For a counterclockwise rotation of the center, a circular trajectory with a varying rotation can be obtained. Then, the rotation change track is used as a reference track, a circular reference track can be obtained, and the robot tracks the track to do circular motion. The control objectives can be described as follows:
wherein the content of the first and second substances,
is a rotation angle phi i (t) is equal to the rotation matrix of [0,2 π),
is an initial time t 0 From the unit vector of the target pointing robot i, define:
then, the circular motion controller is designed to:
in the formula, k p And the position adjusting parameter is more than 0 and is used for adjusting the convergence speed of the robot circular track tracking.
In the embodiment, the realization of the control target of the control law (31) is proved;
theorem 1: let phi i (t) is a first-order continuous derivative function, and under the action of a control law (31), the robot i can finally track the expected relative distance with the radius being the distance from the virtual pilot robot
In a direction of
And moves around the object, i.e., t → ∞,
and is provided with
And (3) proving that: the position tracking error is defined as:
if equation (32 converges to zero, the difference equation for the error can be written as:
the expansion can continue to be such that the error converges to zero and the error dynamics equation can be written as
Wherein the content of the first and second substances,
e is obtained from the first differential equation of the error i (t)＝exp(-k p t)e i (t 0 ) When t → ∞, error e i (t) convergence to 0, with control target
Namely, it is
In particular when
In time, the robot can move around the target at a constant speed.
3) Coupling a control law of azimuth positioning on the basis of a circular motion control law; and performing formation control with an obstacle avoidance function on each formation robot according to a circular motion control law and an obstacle avoidance control law which are coupled with azimuth positioning.
To ensure that the robot can move to the designated position on the circle, custom assignment of the angular spacing of the desired formation can be achieved by changing the steering angle. According to formula (28), we can obtain
Then, the control law (31) can be rewritten as:
wherein the content of the first and second substances,
in view of practical applications, it is sometimes necessary to determine the specific position of the robot on its own circle. For example, when a multi-robot system monitors an area of interest, it is necessary to pre-plan the exact location of each robot to monitor a particular area. Therefore, it is necessary to design an azimuth interval distribution control law that enables the robot to reach a designated position on the circle. We introduce a non-linear function in the controller (36):
in the formula (I), the compound is shown in the specification,
the constant is an azimuth angle adjusting constant and is used for adjusting the convergence speed of the robot azimuth angle spacing.
Theorem 2: under the circular motion control law (36), all the azimuth positioning control laws of the robots are satisfied (38). The azimuth angle of the robot on the circle can be converged to a desired azimuth value, i.e. the desired azimuth angle
And (3) proving that: selecting a positive definite lyapunov function:
the two ends of equation (39) are differentiated with respect to time to obtain a difference equation:
with regard to the formula (40),
the guarantee is negative. Is provided with
With relative balance point
This indicates that the azimuthal convergence of each robot is guaranteed by the control law (38).
In summary, the control law
And
the circular motion controller is proved to be stable, and the distance and the azimuth angle between the robot i and the virtual pilot robot can be converged to the expected relative distance
And desired azimuth angle
Considering that members in the formation inevitably collide with obstacles and other members in the environment in the motion process or formation transformation, an effective obstacle avoidance control law is necessary to be designed, so that the robot can effectively avoid the obstacles, and the stability of the formation is ensured.
Specifically, the obstacle avoidance control law generates a repulsive force by setting a repulsive force potential field to avoid an obstacle.
The artificial potential field method is used as a classic obstacle avoidance control method and is widely applied to motion control of robots. In the embodiment, it is not necessary to set an additional gravitational field function to generate attractive force, because each robot has its own track to track, and the action of the traction by the target position can be regarded as that the gravitational potential field generates "gravitational velocity", so that the obstacle is avoided only by setting a repulsive force field to generate repulsive force, and the conventional repulsive force field function is defined as:
wherein k is o Is a repulsive gain factor. Rho 0 And > 0 is the obstacle avoidance response distance.
Is the obstacle avoidance region ρ 0 The number of obstacles in. Definition of
Wherein
Is the position of the obstacle k in the navigational coordinate system.
Then, since the repulsive force received by the robot is along the negative gradient of the repulsive force field function, the obstacle avoidance control law is expressed as follows:
the traditional artificial potential field method has the problems of local minimum and unreachable target. As shown in fig. 7
In the embodiment, a repulsion potential field is set by an improved manual potential field method to carry out obstacle avoidance control on multi-robot formation, and an obstacle avoidance control law is output;
in the setting of the repulsive force potential field, the travel area of the robot is limited by establishing the repulsive force speed acting on the road boundary; synthesizing the repulsive force velocity acted by the barrier and the repulsive force velocity acted by the road boundary to obtain a repulsive force resultant velocity; and outputting a final obstacle avoidance control law by adjusting the direction of the repulsive force resultant velocity so as to solve the problems of local minimum value and unreachable target and enable the formation robots to avoid obstacles in a safe area of a road.
The effect is shown in fig. 8.
It is assumed here that the inertia is establishedThe coordinate system being located in the middle of the road, i.e. both ends of the road with respect to X n And (4) symmetry.
The "repulsive velocity" set to the road boundary effect is:
wherein k is e And the repulsion gain coefficient acted on the road boundary is more than 0, W is the road width, and B is the robot width (the axle center distance of two driving wheels is equal to the width of the robot body). Definition of
Is the offset point p of the robot i i From the position of the closest point on the road boundary. Xi i Is a logarithmic barrier function, and the function image thereof is shown in fig. 9. It can be seen that when the robot approaches the road potential field area, the robot receives the 'repulsive velocity' from the normal direction of the road boundary, and the 'repulsive velocity' of the robot is larger as the robot is closer to the road boundary, and finally tends to infinity; when the robot is in the safe area, its "repulsive velocity" is 0.
Then, the "repulsive force resultant velocity" experienced by the robot i is:
as can be seen from fig. 9, the "repulsive velocity" has 2 functions, namely, accelerating and decelerating the robot to the target direction, and enabling the tangential force generated by the turning of the robot to bypass the obstacle, so that the closing velocity of the robot in the target direction is 0, and at this time, the robot is easy to fall into the local optimum and targetThe problem is not achieved. In this embodiment, the repulsive force closing speed is adjusted "
So that it becomes the "gravitational velocity" acting on the target "
The direction is vertical and far away from the direction of the obstacle, so that the repulsive force only changes the movement direction and does not change the speed of the object.
Then, the new "repulsive force-closing velocity" is set as:
wherein, the first and the second end of the pipe are connected with each other,
original "resultant velocity of repulsive force"
And "gravitational velocity"
Included angle therebetween
Original "resultant velocity of repulsive force"
And "gravitational velocity"
Included angle therebetween
Rotating matrix
Or
Finally, establishing a dynamic weight factor, and carrying out ratio adjustment on the circular motion control law and the obstacle avoidance control law coupled with azimuth positioning to obtain a final output control law u of the distributed cooperative coupling formation controller to the robot i output i ；
Wherein, the first and the second end of the pipe are connected with each other,
is a dynamic weighting factor used to adjust the internal ratio of the robot control input.
As shown in fig. 10, the controller in the figure is formed by coupling three parts, i.e., a part a, a part B, and a part C, where the part a is mainly used for executing a circular motion control law, the part B is mainly used for executing an azimuth positioning control law, and the part C is mainly used for executing an obstacle avoidance control law.
In conclusion, the virtual navigation-following-based multi-robot formation control method solves the problem of multi-robot formation control.
The invention adopts a virtual navigation-following mode, and the navigation robot is not only responsible for the planning and coordination of the whole system, but also does not influence the motion state of other robots. The problems that tracking errors are accumulated easily and the whole system is broken down due to the fact that a following random device in a chain structure of a traditional navigator-following mode excessively depends on a navigator machine are solved.
The invention establishes the expansion factor of the formation, and also considers the abnormal state condition on the basis of the conventional formation conversion state, namely the condition when the width of the current square channel does not meet the passing condition of the formation individual, and needs to wait for the next step of instruction; the comprehensive performance evaluation index function for measuring the advantages and disadvantages of the formation transformation strategy is provided, the index can be independent of a dynamic model of the system, the expression form is simple and clear, and the comprehensive performance evaluation index function not only can be suitable for time-varying formation transformation of multiple fire-fighting robots, but also can be suitable for other multi-agent systems.
From the practical application, the invention considers that the road boundary and the obstacle parked at the roadside collide with the robot, and the robot keeps a safe distance with the road boundary and the obstacle as far as possible by adding expansion processing to the contour of the road boundary and the obstacle.
The invention establishes the distributed cooperative coupling formation controller, establishes a control law for increasing azimuth positioning on the basis of a circular motion control law, is combined with an obstacle avoidance control law, and can effectively avoid obstacles in the process of advancing according to the formation.
The invention sets the repulsive force potential field to generate the repulsive force to avoid the barrier, can realize the barrier avoiding control of the nonlinear coupling dynamic system with high uncertainty by a very simple algorithm without depending on an accurate mathematical model of the system, and realizes the high-efficiency, safe and reliable formation barrier avoiding. And the effect of the road boundary is also taken into consideration, so that the robot can effectively avoid obstacles in a safe area of the road, and the problems of local optimization and unreachable targets existing in the traditional artificial potential field method are effectively solved.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Claims (10)
1. A virtual navigation-following-based multi-robot formation control method is characterized by comprising the following steps:
establishing a virtual navigation-following motion structure; appointing one robot in the formation as a pilot robot, and the rest as following robots, and setting a virtual pilot robot to travel with the formation on a formation reference track point;
the method comprises the following steps that a piloting robot tracks the position of a virtual piloting robot in real time, and dynamic optimization of formation transformation is carried out according to detected and extracted environment information including road boundaries and obstacles, so that an expected distance and an expected azimuth angle between a following robot and the virtual piloting robot are obtained to adapt to the current environment;
performing motion control on a plurality of robots in formation through a distributed and cooperatively coupled formation controller; enabling each robot to track the real-time position of the virtual pilot robot; converging the expected distance and azimuth angle between the pilot robot and the virtual pilot robot to zero through tracking; the distance and azimuth between the following robot and the virtual pilot robot are converged to a desired relative distance and azimuth.
2. The multi-robot formation control method according to claim 1, wherein the method for acquiring the desired distance and the desired azimuth between the following robot and the virtual pilot robot comprises:
the robot formation follows the virtual pilot robot to advance in an expected initial queue configuration;
the piloting robot detects and extracts road boundary information and obstacle information by using an environment sensing sensor carried by the piloting robot, and calculates a telescopic factor of a formation;
and determining a dequeue form conversion instruction according to the expansion factor of the form and distributing the dequeue form conversion instruction to the following robot, so that the following robot obtains an expected distance and an expected azimuth angle between the following robot and the virtual pilot robot.
3. The multi-robot formation control method according to claim 2,
the navigation robot tracks the position of the virtual navigation robot in real time, perceives environmental information in real time through a multi-line laser radar carried by the navigation robot, extracts the boundary of a road, calculates the width of the road on two sides, and detects and identifies barrier information; and using the detected boundary points of partial obstacles as candidate boundary points, and performing deletion repair on the road boundary points to obtain final boundary point information.
4. The multi-robot formation control method according to claim 3,
the expansion layer is arranged by carrying out expansion processing on the contour of the road boundary and the obstacle, so that the distance between the robot in the formation and the road boundary and the obstacle is larger than the width of the expansion layer.
5. The multi-robot formation control method according to claim 2,
and the piloting robot is used as a stretching factor of the formation according to the ratio of the measured minimum passing width to the expected formation width of the initial formation configuration.
6. The multi-robot formation control method according to claim 5, wherein determining a formation change instruction according to a scaling factor of a formation comprises:
when the expansion factor is larger than 1, outputting a queue form zero transformation instruction to enable the formation to keep the original queue form to continue to advance;
when the scaling factor is within a value interval of a first threshold value and 1; outputting a formation isomorphic conversion instruction to enable the formation to pass through a front passage after the formation is contracted;
when the scaling factor is in the value interval of the second threshold and the first threshold; outputting a queue shape heterogeneous conversion instruction to enable the formation to be converted into a new queue shape to pass through a front channel;
and when the expansion factor is not greater than the second threshold value, outputting a queue abnormal state instruction, and waiting for a next instruction when the formation cannot pass through a front channel.
7. A multi-robot formation control method according to any one of claims 1 to 6, wherein the formation control method of the distributed cooperative-coupled formation controller comprises:
determining the relative displacement and the relative speed between the formation robot and the virtual pilot robot according to the first-order integrator models of the virtual pilot robot and the formation robot;
establishing a circular motion control law, and guiding each formation robot to converge to a circle which takes the virtual pilot robot as a center and takes an expected distance between the formation robot and the virtual pilot robot as a radius in a smooth circular motion mode;
coupling a control law of azimuth positioning on the basis of a circular motion control law; and performing formation control with an obstacle avoidance function on each formation robot according to a circular motion control law and an obstacle avoidance control law which are coupled with azimuth positioning.
8. The multi-robot formation control method according to claim 7, wherein the obstacle avoidance control law performs obstacle avoidance by setting a repulsive force potential field to generate a repulsive force.
9. The multi-robot formation control method according to claim 8, wherein in setting the repulsive potential field, a travel area of the robot is limited by establishing a repulsive speed acting on a road boundary; synthesizing the repulsive force speed acted by the barrier and the repulsive force speed acted by the road boundary to obtain a repulsive force resultant speed; and outputting a final obstacle avoidance control law by adjusting the direction of the repulsive force resultant velocity so as to solve the problems of local minimum value and unreachable target and enable the formation robots to avoid obstacles in a safe area of a road.
10. The multi-robot formation control method according to claim 9,
and establishing a dynamic weight factor, and carrying out ratio adjustment on the circular motion control law and the obstacle avoidance control law coupled with azimuth positioning to obtain the control law finally output by the distributed cooperative coupling formation controller.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210884676.6A CN115167440A (en) | 2022-07-25 | 2022-07-25 | Virtual navigation-following-based multi-robot formation control method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210884676.6A CN115167440A (en) | 2022-07-25 | 2022-07-25 | Virtual navigation-following-based multi-robot formation control method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115167440A true CN115167440A (en) | 2022-10-11 |
Family
ID=83497269
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210884676.6A Pending CN115167440A (en) | 2022-07-25 | 2022-07-25 | Virtual navigation-following-based multi-robot formation control method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115167440A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116300970A (en) * | 2023-05-17 | 2023-06-23 | 斯润天朗（北京）科技有限公司 | Autonomous vehicle formation method and device |
| CN116400604A (en) * | 2023-06-06 | 2023-07-07 | 浙江大学海南研究院 | Spherical formation control method for multi-agent system with collision avoidance performance |
| CN116483096A (en) * | 2023-06-25 | 2023-07-25 | 中国第一汽车股份有限公司 | Vehicle formation control method, system, cloud platform and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180074520A1 (en) * | 2016-09-13 | 2018-03-15 | Arrowonics Technologies Ltd. | Formation flight path coordination of unmanned aerial vehicles |
| CN111694357A (en) * | 2020-06-19 | 2020-09-22 | 国网福建省电力有限公司福州供电公司 | Robot walking path planning method based on genetic algorithm and artificial potential field method |
| CN111813121A (en) * | 2020-07-13 | 2020-10-23 | 陕西理工大学 | Multi-mobile-robot formation obstacle avoidance method based on distance-angle priority |
| CN113110495A (en) * | 2021-05-08 | 2021-07-13 | 广东省智能机器人研究院 | Formation control method of mobile robots under consideration of external interference |
-
2022
- 2022-07-25 CN CN202210884676.6A patent/CN115167440A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180074520A1 (en) * | 2016-09-13 | 2018-03-15 | Arrowonics Technologies Ltd. | Formation flight path coordination of unmanned aerial vehicles |
| CN111694357A (en) * | 2020-06-19 | 2020-09-22 | 国网福建省电力有限公司福州供电公司 | Robot walking path planning method based on genetic algorithm and artificial potential field method |
| CN111813121A (en) * | 2020-07-13 | 2020-10-23 | 陕西理工大学 | Multi-mobile-robot formation obstacle avoidance method based on distance-angle priority |
| CN113110495A (en) * | 2021-05-08 | 2021-07-13 | 广东省智能机器人研究院 | Formation control method of mobile robots under consideration of external interference |
Non-Patent Citations (6)
| Title |
|---|
| SHUFENG WANG: "Artificial potential field algorithm for path control of unmanned ground vehicles formation in highway", 《ELECTRONICS LETTERS》, 4 October 2018 (2018-10-04) * |
| 任立敏: "障碍环境下多移动机器人动态优化队形变换", 《机器人》, 30 September 2013 (2013-09-30), pages 539 - 540 * |
| 夏长高: "融合B 样条与改进APF 的路径规划算法研究", 《重庆理工大学学报》, 15 June 2022 (2022-06-15), pages 49 * |
| 王刚: "Cooperative Target Enclosing and Tracking Control with Obstacles Avoidance for Multiple Nonholonomic Mobile Robots", 《APPLIED SCIENCES》, 11 March 2022 (2022-03-11), pages 1 - 8 * |
| 王刚: "面向多智能消防机器人的编队避障控制方法", 《机器人》, 11 December 2023 (2023-12-11) * |
| 王树凤: "基于人工势场和虚拟领航者的智能车辆编队控制", 《上海交通大学学报》, 31 March 2020 (2020-03-31) * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116300970A (en) * | 2023-05-17 | 2023-06-23 | 斯润天朗（北京）科技有限公司 | Autonomous vehicle formation method and device |
| CN116300970B (en) * | 2023-05-17 | 2023-09-29 | 斯润天朗（北京）科技有限公司 | Autonomous vehicle formation method and device |
| CN116400604A (en) * | 2023-06-06 | 2023-07-07 | 浙江大学海南研究院 | Spherical formation control method for multi-agent system with collision avoidance performance |
| CN116400604B (en) * | 2023-06-06 | 2023-08-18 | 浙江大学海南研究院 | Spherical formation control method for multi-agent system with collision avoidance performance |
| CN116483096A (en) * | 2023-06-25 | 2023-07-25 | 中国第一汽车股份有限公司 | Vehicle formation control method, system, cloud platform and storage medium |
| CN116483096B (en) * | 2023-06-25 | 2023-09-22 | 中国第一汽车股份有限公司 | Vehicle formation control method, system, cloud platform and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115167440A (en) | Virtual navigation-following-based multi-robot formation control method | |
| CN110262495B (en) | Control system and method capable of realizing autonomous navigation and accurate positioning of mobile robot | |
| CN105955268B (en) | A kind of UUV moving-target sliding mode tracking control methods considering Local obstacle avoidance | |
| Lacaze et al. | Path planning for autonomous vehicles driving over rough terrain | |
| CN110398969A (en) | Automatic driving vehicle adaptive prediction time domain rotating direction control method and device | |
| CN104881025B (en) | A kind of reactive navigation control method of underground mine vehicle | |
| CN109947123A (en) | A kind of unmanned plane path trace and automatic obstacle avoiding method based on line of sight guidance rule | |
| CN112684807A (en) | Unmanned aerial vehicle cluster three-dimensional formation method | |
| CN106406338A (en) | Omnidirectional mobile robot autonomous navigation apparatus and method based on laser range finder | |
| CN111258316A (en) | Robot trajectory planning method for trend perception in dynamic environment | |
| Jian et al. | Putn: A plane-fitting based uneven terrain navigation framework | |
| CN114442621A (en) | Autonomous exploration and mapping system based on quadruped robot | |
| Fu et al. | Path planning and decision making for autonomous vehicle in urban environment | |
| Chen et al. | Path planning for autonomous vehicle based on a two-layered planning model in complex environment | |
| CN114022824A (en) | Narrow environment-oriented quadruped robot motion planning method | |
| Li et al. | Hybrid filtering framework based robust localization for industrial vehicles | |
| CN114200945A (en) | Safety control method of mobile robot | |
| Funk et al. | Autonomous electric race car design | |
| Yang et al. | Mobile robot motion control and autonomous navigation in GPS-denied outdoor environments using 3D laser scanning | |
| CN115032999A (en) | Dynamic optimization formation transformation method for multi-robot formation | |
| CN114942642A (en) | Unmanned automobile track planning method | |
| Hu et al. | Path planning of UGV based on Bézier curves | |
| Brock et al. | Coordination and control of multiple autonomous vehicles. | |
| CN117249817A (en) | Light autonomous navigation system and method for pipeline inspection robot in field environment | |
| Jordan et al. | Real-time model based path planning for wheeled vehicles |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |