CN111240332A - Multi-target enclosure method for cooperative operation of swarm robots in complex convex environment - Google Patents

Multi-target enclosure method for cooperative operation of swarm robots in complex convex environment Download PDF

Info

Publication number
CN111240332A
CN111240332A CN202010056177.9A CN202010056177A CN111240332A CN 111240332 A CN111240332 A CN 111240332A CN 202010056177 A CN202010056177 A CN 202010056177A CN 111240332 A CN111240332 A CN 111240332A
Authority
CN
China
Prior art keywords
target
robot
dynamic
enclosure
task
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
Application number
CN202010056177.9A
Other languages
Chinese (zh)
Inventor
张红强
吴亮红
周少武
刘朝华
陈磊
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Hunan University of Science and Technology
Original Assignee
Hunan University of Science and Technology
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Hunan University of Science and Technology filed Critical Hunan University of Science and Technology
Priority to CN202010056177.9A priority Critical patent/CN111240332A/en
Publication of CN111240332A publication Critical patent/CN111240332A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Manipulator (AREA)

Abstract

The invention discloses a multi-target enclosure method for cooperative operation of swarm robots under a complex convex environment, which comprises the steps of designing a motion model of multiple targets and dynamic obstacles under the complex convex environment, constructing a multi-target simplified virtual stress model through the study on the enclosure behavior under the complex environment, and providing a dynamic multi-target self-organization task allocation method and a specific process of cooperative self-organization dynamic multi-target enclosure based on the stress model.

Description

Multi-target enclosure method for cooperative operation of swarm robots in complex convex environment
Technical Field
The invention relates to the technical field of chasing and trapping, in particular to a multi-target trapping method for cooperative operation of swarm robots in a complex convex environment.
Background
The swarm robot system is a mobile distributed system, has the characteristic of high density, and has robustness, expandability and flexibility. These important features make swarm robotic systems more promising for large-scale tasks than single or multi-robot systems.
The challenge of realizing the dynamic multi-target enclosure of the large-scale swarm robots is how to realize the task allocation of each robot in a self-organized manner when the dynamic multi-targets escape, so that not only enough robots of each dynamic target participate in the enclosure, but also the robots of the farthest targets participate in the enclosure, thereby ensuring the enclosure success rate; the information required during task allocation is local information as little as possible, the time for allocating the tasks is very short, the task allocation algorithm is as simple as possible, otherwise, the tasks are not allocated completely, and the targets can escape; how to avoid collision among robots of different enclosure targets and how to reduce the movement distance; how to keep a multi-target formation and successfully avoid obstacles in an unknown dynamic convex obstacle environment, and the motion of an individual is controlled by little local information.
Disclosure of Invention
In view of the above, the invention provides a multi-target enclosure method for cooperative operation of swarm robots in a complex convex environment, which utilizes a designed motion model and a multi-target simplified virtual stress model of multiple targets and dynamic obstacles in the complex convex environment, and only needs to know position information of the multiple targets and two nearest neighbors and task information of the two nearest neighbors facing to the direction of a multi-target center, so that multi-target enclosure can be realized, and the method has the advantages of good obstacle avoidance performance, robustness, expandability and flexibility, simplicity, high efficiency and easiness in realization.
On one hand, the invention provides a multi-target enclosure method for cooperative operation of swarm robots in a complex convex environment, which comprises the following steps:
s100, constructing a swarm robot motion model and a related function, wherein the swarm robot is composed of m identical incomplete mobile wheeled robots hjComposition, j ═ 1,2, …, m, correlation in this stepThe function includes robot hjKinematic equation of (a), robot, static or dynamic obstacle and non-robot h in the course of enclosurejA force application function of the enclosed target and the object and a bionic intelligent obstacle avoidance mapping function for the convex obstacle;
s200, constructing a multi-target enclosure task model and related functions through an enclosure environment, a dynamic multi-target and a dynamic barrier model, wherein the related functions in the step comprise a motion equation of a target in a complex environment and a motion equation of a dynamic barrier in a given barrier environment;
s300, constructing a multi-target simplified virtual stress model;
s400, determining a specific process of performing dynamic multi-target enclosure by the swarm robots in a cooperative manner under the environment of unknown complex convex dynamic obstacles based on the multi-target simplified virtual stress model.
Further, the robot h in step S100jThe kinematic equation of (a) is as follows:
Figure BDA0002372916220000021
in the formula, vj(t) and ωj(t) are robots h, respectivelyjLinear and angular velocities of, and
Figure BDA0002372916220000022
Figure BDA0002372916220000023
respectively a maximum linear velocity and a maximum angular velocity,
Figure BDA0002372916220000024
for a robot hjThe linear velocity in the x-axis direction,
Figure BDA0002372916220000025
for a robot hjThe linear velocity in the y-axis direction,
Figure BDA0002372916220000026
for a robot hjThe angular velocity of (a);
the force application functions are respectively as follows:
Figure BDA00023729162200000211
Figure BDA0002372916220000027
in the formulae (2) and (3),
Figure BDA0002372916220000028
representing a target tpTo robot h of enclosingjMagnitude of the applied force of fo(d) Robot h for representing close-neighbor object O pair enclosurejD represents the distance between two points, c1、d1And
Figure BDA0002372916220000029
for optimizing the robot hjA path of movement of cr、c2And
Figure BDA00023729162200000210
i is 1,2,3 and 4 respectively representing that the object is the robot hjStatic, dynamic obstacles and non-robots hjSpecific parameters for use in confining the target, nc、l、dspRespectively representing the current capture step number, the step number of starting to move to an effective capture circle and the distance between the robot and the target when starting to move to the effective capture circle, wherein the effective capture circle uses the target tpAs a center of a circle, crA circumference formed by a radius;
the bionic intelligent obstacle avoidance mapping function is as follows:
Figure BDA0002372916220000031
wherein, sigma is a real number, (0 is not less than sigma and not more than 1), (-1 is not less than sigma and less than 0) is a judgment condition, and is 1 when satisfied, otherwise is 0.
Further, the dynamic multi-objective and dynamic obstacle model is built through the following processes:
1) in the global coordinate system XOY, positional information of the robot and the obstacle is set to OK＝(xK,yK) K ∈ { T, H, S, U }, which includes the target T ═ TpP is 0,1, …, e, H is HjJ is 1,2, …, m, and S is SjJ-1, 2, …, α and dynamic obstacle U-Uj:j＝1,2,…,β}；
2) Determining within a target potential domain
Figure BDA0002372916220000032
The set of all robots is:
Figure BDA0002372916220000033
wherein the content of the first and second substances,
Figure BDA0002372916220000034
is the radius of the potential domain of the target,
Figure BDA0002372916220000035
is a target tpThe abscissa of the (c) axis of the (c),
Figure BDA0002372916220000036
is a target tpOrdinate of (a), xjFor a robot hjAbscissa of (a), yjFor a robot hjThe ordinate of (a);
3) the static obstacles to be avoided are respectively
Figure BDA0002372916220000037
Figure BDA0002372916220000038
And
Figure BDA0002372916220000039
Figure BDA00023729162200000310
as static obstacles sjThe abscissa of the (c) axis of the (c),
Figure BDA00023729162200000311
as static obstacles sjThe ordinate of (a) is,
Figure BDA00023729162200000312
as dynamic obstacles uiThe abscissa of the (c) axis of the (c),
Figure BDA00023729162200000313
as dynamic obstacles uiThe ordinate of (a) is,
Figure BDA00023729162200000314
is the distance at which the target begins to avoid the static obstacle.
Further, the equation of motion of the target in the complex environment in step S200 is:
Figure BDA0002372916220000041
in the formula (I), the compound is shown in the specification,
Figure BDA0002372916220000042
the acceleration of the object is represented as the acceleration,
Figure BDA0002372916220000043
respectively the walking speed and the maximum speed of the object,
Figure BDA0002372916220000044
is the speed of the object or objects,
Figure BDA0002372916220000045
representing a target tpThe magnitude of the potential of (a) is,
Figure BDA0002372916220000046
representing a target tpThe sum of the perceived potentials is,
Figure BDA0002372916220000047
is the initial wandering direction angle of the target,
Figure BDA0002372916220000048
representing a target tpThe angle of the direction of movement of (a),
Figure BDA0002372916220000049
representing a target tpThe forward direction of the potential angle of (1) represents the direction in which prey groups and static and dynamic obstacles are perceived by prey, namely the escape direction, and the reverse direction of the potential angle of (1) represents the direction in which prey groups and static and dynamic obstacles are perceived by prey, namely the confrontation direction, and represents the direction in which prey groups and dynamic obstacles are perceived by prey;
the equation of motion of a dynamic obstacle in a given obstacle environment is:
Figure BDA00023729162200000410
in the formula uiA dynamic obstacle is represented, which is,
Figure BDA00023729162200000411
the speed of the dynamic obstacle is represented and,
Figure BDA00023729162200000412
the movement angle and the speed of the dynamic barrier are respectively set for the initial setting,
Figure BDA00023729162200000413
respectively the potential of a static obstacle and its azimuth relative to a dynamic obstacle,
Figure BDA00023729162200000414
the potential of the dynamic obstacle and its azimuth relative to the dynamic obstacle, d, respectively3Index parameter, d, representing enhanced avoidance of static obstacles4An index parameter representing enhanced avoidance of dynamic obstacles.
Further, the multi-objective simplified virtual stress model in step S300 is specifically established through the following processes:
sit in the wholeIn the notation XOY, robot hjThe target t can be obtainedpAnd two nearest neighbor objects Oaj,ObjAnd position information of itself, where (p ═ 1, …, e) at robot hjIn a relative coordinate system x ' O ' y ' as an origin, the robot hjSubject to a target tpAnd the action of attraction or repulsion of two nearest neighbor objects Oaj,ObjRespectively, is recorded as
Figure BDA00023729162200000415
fajAnd fbjWhen is coming into contact with
Figure BDA00023729162200000416
Target tpGenerates an attractive force when
Figure BDA00023729162200000417
Target tpGenerating repulsive force, hjIs subjected to the overall force fx'y'jIs a component from the y' axis
Figure BDA00023729162200000418
And the component f of the x' axisabjComposition and fabjIs fajAnd fbjProjections f on the x' axis respectivelyaj(||paj||)·φ(cos(γfajx') And f) andbj(||pbj||)·φ(cos(γfbjx') A sum of γ) offajx'And gammafbjx'Separate robot hjOf the two nearest neighbor object Oaj,ObjAngle of repulsion of, paj,pbjAre respectively two nearest neighbor objects Oaj,ObjThe position vector of (2).
Further, the specific process of the swarm robots cooperatively performing the dynamic multi-target enclosure in step S400 is as follows:
s401, setting track control and obstacle avoidance parameters, and initializing swarm robots;
s402, based on robot hjCarrying out task allocation on two nearest neighbors in the multi-target center direction within 180 degrees;
s403, judging the robot hjWhether the task allocation is finished or not, if so, the step S404A is carried out, otherwise, the step S404B is carried out;
S404A, exchanging and enclosing targets;
S404B, taking a multi-target center as a capture target;
s405, calculating corresponding parameters according to the multi-target simplified virtual stress model;
s406, calculating a desired velocity vector vjeDesired direction of motion thetajeRobot hjTo the desired direction of movement thetajeRequired time tntjActual achievable velocity vjfAnd a desired velocity vector vjeCompensated velocity vjc；
S407, moving for a time step:
s408, repeating steps S402 to S407 until j ═ m;
s409, judging whether all individuals meet the following conditions:
Figure BDA0002372916220000051
and paj||-||pbj|||＜ε2Wherein, in the step (A),
Figure BDA0002372916220000052
show that robot h is caughtjTo the target tpDistance of (p)ajIndicates the neighbor OajTo the robot for enclosure hjDistance of (p)bjIndicates the neighbor ObjTo the robot for enclosure hjA distance of ∈ of1Enclosure robot h showing settingsjTo the target tpAs the center of a circle, crFor the magnitude of the distance error, epsilon, over the effective circumference of the radius2Robot h showing set enclosurejTo the nearest neighbors OajAnd ObjIf the error of the distance difference is large, ending; otherwise, the process returns to step S402.
Further, the step S402 specifically includes the following steps:
s4020, judging robot hjIf the task is already distributed, if so, then the process proceedsGo to step S4029; otherwise, the step S4021 is entered;
s4021 and calculating robot hjThe number f of neighbors facing the multi-target central direction within 180 DEGn；
S4022, judgment fnIf the number is 0, if so, the task 1 is the robot hjStep S4029, otherwise, step S4023 is performed;
s4023, judgment fnIf the number of the neighbor tasks is 1 and the tasks of the neighbor are not allocated, if so, the robot hjThe hunting task is temporarily not allocated, is set to 0, and the step S4029 is performed, otherwise, the step S4024 is performed;
s4024, judgment fnIf the number of the tasks is 1 and the tasks of the neighbor are already allocated, if so, judging whether the number representation of the tasks of the neighbor is equal to the total target number, otherwise, entering the step S4025;
s4025, judgment fnIf the number of tasks is more than or equal to 2 and the tasks of two nearest neighbors are not distributed, if so, the robot hjThe hunting task is temporarily not allocated, is set to 0, and the step S4029 is performed, otherwise, the step S4026 is performed;
s4026, judgment fnWhether the number of the tasks is more than or equal to 2 and the two nearest neighbors distribute the same task is judged, if yes, whether the number representation of the tasks is equal to the total target number is judged, otherwise, the step S4027 is carried out;
s4027, judging whether the maximum value of the task numbers of the two nearest neighbors is equal to the total target number, if so, taking the task 1 as the robot hjStep S4029, otherwise, step S4028;
s4028, adding 1 to the maximum value of the two nearest neighbor task numbers to obtain a robot hjThe enclosure task of (1);
and S4029, ending.
Further, it is determined in steps S4024 and S4026 whether the numerical representation of the task is equal to the total number of targets, and if so, the task 1 is the robot hjAnd (4) performing the enclosure task, and entering the step S4029, otherwise, performing the robot hjThe enclosure task adds 1 to the number of the task and enters the stepStep S4029.
Further, step S404A specifically includes the following steps:
S404A0, setting a flag bit of a starting exchange target;
S404A1, judging whether the target flag bit of the start exchange is 1, if yes, entering the step S404A2, otherwise, entering the step S404A 7;
S404A2, judging robot hjWhether the trapping task is allocated or not, if yes, the step S404A3 is carried out, otherwise, the step S404A7 is carried out;
S404A3, judging robot hjNumber f of neighbors in 180-degree range facing to multi-target center directionnWhether the value is greater than or equal to 1, if so, the step S404a4 is performed, otherwise, the step S404a7 is performed;
S404A4, judging robot hjWhether the previous nearest neighbor has already allocated a task, if yes, go to step S404a5, otherwise, go to step S404a 7;
S404A5, judging whether the sum of the target distances after the exchange is smaller than that before the exchange, if so, going to S404A6, otherwise, going to S404A 7;
S404A6 and robot hjExchanging the capture target with the nearest neighbor in front;
and S404A7, ending.
Further, the step S404a0 of setting the start swap target flag is implemented by the following processes:
(1) judging whether the flag bit of the starting exchange target is 0, if so, entering the step (2), otherwise, entering the step (5);
(2) judge robot hjWhether the captive robot exists in the range of 180 degrees on the back facing to the central direction of the multiple targets or not is judged, if yes, the step (5) is carried out, and otherwise, the step (3) is carried out;
(3) judge robot hjIf so, entering the step (4), otherwise, entering the step (5);
(4) setting a flag bit of a starting exchange target to be 1;
(5) and (6) ending.
The invention provides a multi-target enclosure method for cooperative operation of swarm robots under a complex convex environment, which comprises the steps of designing a motion model of multiple targets and dynamic obstacles under the complex convex environment, constructing a multi-target simplified virtual stress model through the study on the enclosure behavior under the complex environment, and providing a dynamic multi-target self-organization task allocation method and a specific process of cooperative self-organization dynamic multi-target enclosure based on the stress model.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an embodiment of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart of a multi-target enclosure method for cooperative operation of swarm robots in a complex convex environment according to an embodiment of the present invention;
FIG. 2a is a diagram of a multi-objective simplified virtual stress model under one condition;
FIG. 2b is a diagram of a multi-objective simplified virtual stress model under another situation;
FIG. 3 is a flow chart of a swarm robot self-organization cooperative dynamic multi-target enclosure method;
FIG. 4 shows a robot hjA task allocation flow chart;
FIG. 5 is a flow chart of an exchange trapping target;
fig. 6 is a flow chart of setting the start swap target flag bit.
Detailed Description
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict. The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
Fig. 1 is a flowchart of a multi-target enclosure method for cooperative operation of swarm robots in a complex convex environment according to an embodiment of the present invention. As shown in fig. 1, a multi-target enclosure method for cooperative operation of swarm robots in a complex convex environment comprises the following steps:
s100, constructing a swarm robot motion model and a related function, wherein the swarm robot is composed of m identical incomplete mobile wheeled robots hjComposition, j ═ 1,2, …, m, and the correlation function in this step includes robot hjKinematic equation of (a), robot, static or dynamic obstacle and non-robot h in the course of enclosurejA force application function of the enclosed target and the object and a bionic intelligent obstacle avoidance mapping function for the convex obstacle;
preferably, the robot hjThe kinematic equation of (a) is as follows:
Figure BDA0002372916220000081
in the formula, vj(t) and ωj(t) are robots h, respectivelyjLinear and angular velocities of, and
Figure BDA0002372916220000091
Figure BDA0002372916220000092
respectively a maximum linear velocity and a maximum angular velocity,
Figure BDA0002372916220000093
for a robot hjThe linear velocity in the x-axis direction,
Figure BDA0002372916220000094
for a robot hjThe linear velocity in the y-axis direction,
Figure BDA0002372916220000095
for a robot hjThe angular velocity of (a);
the force application functions are respectively as follows:
Figure BDA0002372916220000096
Figure BDA0002372916220000097
in the formulae (2) and (3),
Figure BDA0002372916220000098
representing a target tpTo robot h of enclosingjMagnitude of the applied force of fo(d) Robot h for representing close-neighbor object O pair enclosurejD represents the distance between two points, c1、d1And
Figure BDA0002372916220000099
for optimizing the robot hjA path of movement of cr、c2And
Figure BDA00023729162200000910
i is 1,2,3 and 4 respectively representing that the object is the robot hjStatic, dynamic obstacles and non-robots hjSpecific parameters for use in confining the target, nc、l、dspRespectively representing the current capture step number, the step number of starting to move to an effective capture circle and the distance between the robot and the target when starting to move to the effective capture circle, wherein the effective capture circle uses the target tpAs a center of a circle, crA circumference formed by a radius; it should be noted that two conditions are used to determine when to move to the effective capture circle, and that the condition (d > s)p) The device is used for protecting the enclosure robot from being too close to a target and preventing damage: when (d > s)p) Failure, i.e. when the speed of the robot is sometimes difficult to approach the target over a certain distance range, (n)cLess than l) forcibly stopping the quick chasing target under the condition of starting to move to the effective surrounding circle, tpDifferent targets are pointed according to different p values;
the bionic intelligent obstacle avoidance mapping function is as follows:
Figure BDA00023729162200000911
wherein, sigma is a real number, (0 is not less than sigma and not more than 1), and (-1 is not less than sigma and less than 0) is a judgment condition, and is 1 when the judgment condition is met, or is 0 otherwise;
s200, constructing a multi-target enclosure task model and related functions through an enclosure environment, a dynamic multi-target and a dynamic barrier model, wherein the related functions in the step comprise a motion equation of a target in a complex environment and a motion equation of a dynamic barrier in a given barrier environment; specifically, the motion equation of the target in the complex environment is as follows:
Figure BDA0002372916220000101
in the formula (I), the compound is shown in the specification,
Figure BDA0002372916220000102
the acceleration of the object is represented as the acceleration,
Figure BDA0002372916220000103
respectively the walking speed and the maximum speed of the object,
Figure BDA0002372916220000104
is the speed of the object or objects,
Figure BDA0002372916220000105
representing a target tpThe magnitude of the potential of (a) is,
Figure BDA0002372916220000106
representing a target tpThe sum of the perceived potentials is,
Figure BDA0002372916220000107
is the initial wandering direction angle of the target,
Figure BDA0002372916220000108
representing a target tpThe angle of the direction of movement of (a),
Figure BDA0002372916220000109
representing a target tpThe forward direction of the potential angle of (1) represents the direction in which prey groups and static and dynamic obstacles are perceived by prey, namely the escape direction, and the reverse direction of the potential angle of (1) represents the direction in which prey groups and static and dynamic obstacles are perceived by prey, namely the confrontation direction, and represents the direction in which prey groups and dynamic obstacles are perceived by prey;
the equation of motion of a dynamic obstacle in a given obstacle environment is:
Figure BDA00023729162200001010
in the formula uiA dynamic obstacle is represented, which is,
Figure BDA00023729162200001011
the speed of the dynamic obstacle is represented and,
Figure BDA00023729162200001012
the movement angle and the speed of the dynamic barrier are respectively set for the initial setting,
Figure BDA00023729162200001013
respectively the potential of a static obstacle and its azimuth relative to a dynamic obstacle,
Figure BDA00023729162200001014
the potential of the dynamic obstacle and its azimuth relative to the dynamic obstacle, d, respectively3Index parameter, d, representing enhanced avoidance of static obstacles4An index parameter representing enhanced avoidance of dynamic obstacles;
s300, constructing a multi-target simplified virtual stress model, specifically, establishing the model through the following steps:
in the global coordinate system XOY, the robot hjThe target t can be obtainedpAnd two nearest neighbor objects (which may be robots, static or dynamic obstacles) Oaj,ObjAnd position information of itself, where (p ═ 1, …, e) at robot hjIn a relative coordinate system x ' O ' y ' as an origin, the robot hjSubject to a target tpAnd the action of attraction or repulsion of two nearest neighbor objects Oaj,ObjRespectively, is recorded as
Figure BDA00023729162200001015
fajAnd fbjWhen is coming into contact with
Figure BDA00023729162200001016
Target tpGenerating attractive force, see in particular FIG. 2a, when
Figure BDA0002372916220000111
Target tpGenerating a repulsive force, see in particular fig. 2 b; h isjIs subjected to the overall force fx'y'jIs a component from the y' axis
Figure BDA0002372916220000112
And the component f of the x' axisabjComposition and fabjIs fajAnd fbjProjections f on the x' axis respectivelyaj(||paj||)·φ(cos(γfajx') And f) andbj(||pbj||)·φ(cos(γfbjx') A sum of γ) offajx'And gammafbjx'Are respectively a robot hjOf the two nearest neighbor object Oaj,ObjIs inclined by a repulsive force ofjPosition vector in relative coordinate system x 'O' y
Figure BDA0002372916220000113
paj,pbjAre respectively two nearest neighbor objects Oaj,ObjThe position vector of (a), wherein:
Figure BDA0002372916220000114
paj＝(xj-xaj)+i(yj-yaj) (8)
pbj＝(xj-xbj)+i(yj-ybj) (9)
in the formula (I), the compound is shown in the specification,
Figure BDA0002372916220000115
is a target tpThe abscissa of the (c) axis of the (c),
Figure BDA0002372916220000116
is a target tpOrdinate of (a), xjFor a robot hjAbscissa of (a), yjFor a robot hjOrdinate of (a), xajIs a nearest neighbor object OajAbscissa of (a), yajIs a nearest neighbor object OajOrdinate of (a), xbjIs a nearest neighbor object ObjAbscissa of (a), ybjIs a nearest neighbor object ObjThe ordinate of (c).
S400, determining a specific process of performing dynamic multi-target enclosure by the swarm robots in a cooperative manner under the environment of unknown complex convex dynamic obstacles based on the multi-target simplified virtual stress model.
As a preferred embodiment of the present invention, the dynamic multi-objective and dynamic obstacle model in step S200 is established by the following procedure:
1) in the global coordinate system XOY, positional information of the robot and the obstacle is set to OK＝(xK,yK) K ∈ { T, H, S, U }, which includes the target T ═ TpP is 0,1, …, e, H is HjJ is 1,2, …, m, and S is SjJ-1, 2, …, α and dynamic obstacle U-Uj:j＝1,2,…,β}；
2) Determining within a target potential domain
Figure BDA0002372916220000117
The set of all robots is:
Figure BDA0002372916220000118
wherein the content of the first and second substances,
Figure BDA0002372916220000119
is the radius of the potential domain of the target,
Figure BDA00023729162200001110
is a target tpThe abscissa of the (c) axis of the (c),
Figure BDA00023729162200001111
is a target tpOrdinate of (a), xjFor a robot hjAbscissa of (a), yjFor a robot hjThe ordinate of (a);
3) the static obstacles to be avoided are respectively
Figure BDA0002372916220000121
Figure BDA0002372916220000122
And
Figure BDA0002372916220000123
Figure BDA0002372916220000124
as static obstacles sjThe abscissa of the (c) axis of the (c),
Figure BDA0002372916220000125
as static obstacles sjThe ordinate of (a) is,
Figure BDA0002372916220000126
as dynamic obstacles uiThe abscissa of the (c) axis of the (c),
Figure BDA0002372916220000127
as dynamic obstacles uiThe ordinate of (a) is,
Figure BDA0002372916220000128
is the distance at which the target begins to avoid the static obstacle.
Meanwhile, as shown in fig. 3, the specific process of the swarm robots cooperatively performing dynamic multi-target enclosure in step S400 of the invention is as follows:
s401, setting track control and obstacle avoidance parameters, and initializing swarm robots;
s402, based on robot hjFacing multiple target central direction 180 °The task allocation is performed in the two nearest neighbors within the range, and preferably, as shown in fig. 4, the step specifically includes the following steps:
s4020, judging robot hjIf the task is already allocated, the step S4029 is carried out; otherwise, the step S4021 is entered;
s4021 and calculating robot hjThe number f of neighbors facing the multi-target central direction within 180 DEGn；
S4022, judgment fnIf the number is 0, if so, the task 1 is the robot hjStep S4029, otherwise, step S4023 is performed;
s4023, judgment fnIf the number of the neighbor tasks is 1 and the tasks of the neighbor are not allocated, if so, the robot hjThe hunting task is temporarily not allocated, is set to 0, and the step S4029 is performed, otherwise, the step S4024 is performed;
s4024, judgment fnIf the number of the tasks is 1 and the tasks of the neighbor are already allocated, if so, judging whether the number representation of the tasks of the neighbor is equal to the total target number, otherwise, entering the step S4025;
s4025, judgment fnIf the number of tasks is more than or equal to 2 and the tasks of two nearest neighbors are not distributed, if so, the robot hjThe hunting task is temporarily not allocated, is set to 0, and the step S4029 is performed, otherwise, the step S4026 is performed;
s4026, judgment fnWhether the number of the tasks is more than or equal to 2 and the two nearest neighbors distribute the same task is judged, if yes, whether the number representation of the tasks is equal to the total target number is judged, otherwise, the step S4027 is carried out;
s4027, judging whether the maximum value of the task numbers of the two nearest neighbors is equal to the total target number, if so, taking the task 1 as the robot hjStep S4029, otherwise, step S4028;
s4028, adding 1 to the maximum value of the two nearest neighbor task numbers to obtain a robot hjThe enclosure task of (1);
s4029, ending;
in addition, steps S4024 and S4026Judging whether the number representation of the task is equal to the total target number, if so, the task 1 is the robot hjAnd (4) performing the enclosure task, and entering the step S4029, otherwise, performing the robot hjThe enclosure task is that the number of the task is added with 1 and the step S4029 is carried out;
s403, judging the robot hjWhether the task allocation is finished or not, if so, the step S404A is carried out, otherwise, the step S404B is carried out;
S404A, exchanging and enclosing targets;
S404B, taking a multi-target center as a capture target;
s405, calculating corresponding parameters according to the multi-target simplified virtual stress model;
s406, calculating a desired velocity vector vjeDesired direction of motion thetajeRobot hjTo the desired direction of movement thetajeRequired time tntjActual achievable velocity vjfAnd a desired velocity vector vjeCompensated velocity vjc(ii) a Specifically, the above parameters are solved by the following formula:
Figure BDA0002372916220000131
in the formula, vx,y,jIndicates when the target is stationary hjThe required velocity vector of (a) is,
Figure BDA0002372916220000132
indicating robot hjThe velocity vector of the sensing target, Γ is the period of operation, θjeAnd thetajbefRespectively the desired direction of movement of the next step and the direction of movement of the previous step, tntjIs calculated according to
Figure BDA0002372916220000133
And
Figure BDA0002372916220000134
time required for steering, tntj1Is pressed against
Figure BDA0002372916220000135
Accelerate to
Figure BDA0002372916220000136
Required time, tntj2Is by achieving
Figure BDA0002372916220000141
Rear to steering thetajeThe time required for the operation of the apparatus,
Figure BDA0002372916220000142
is the velocity vector of the individual perceptual target,
Figure BDA0002372916220000143
is the velocity vector of the individual perception multi-target center, vjcIs according to a desired velocity vector vjeThe compensated speed;
s407, moving by one time step, specifically, by the following equation (11) or (12):
when gamma is less than or equal to tntjWhen the temperature of the water is higher than the set temperature,
Figure BDA0002372916220000144
namely, the robot only turns;
when gamma is greater than tntjWhen the temperature of the water is higher than the set temperature,
Figure BDA0002372916220000145
at this time, the motion strategy of the robot is firstly turned to the expected motion direction thetajeThen according to the actual achievable speed vjfMoving;
in addition, the functions not containing time in equations (11) and (12) are both calculated amounts at time k Γ and remain unchanged at [ k Γ, (k +1) Γ),
s408, repeating steps S402 to S407 until j ═ m;
s409, judging whether all individuals meet the following conditions:
Figure BDA0002372916220000146
and paj||-||pbj|||＜ε2Wherein, in the step (A),
Figure BDA0002372916220000147
show that robot h is caughtjTo the target tpDistance of (p)ajIndicates the neighbor OajTo enclose and catch robot hjDistance of (p)bjIndicates the neighbor ObjTo enclose and catch robot hjA distance of ∈ of1Enclosure robot h showing settingsjTo the target tpAs the center of a circle, crFor the magnitude of the distance error, epsilon, over the effective circumference of the radius2Enclosure robot h showing settingsjTo the nearest neighbors OajAnd ObjIf the error of the distance difference is large, ending; otherwise, the process returns to step S402.
Furthermore, it should be mentioned that, as shown in fig. 5, step S404A specifically includes the following steps:
s404a0, setting a start exchange target flag bit, specifically, as shown in fig. 6, the step is implemented by the following processes:
(1) judging whether the flag bit of the starting exchange target is 0, if so, entering the step (2), otherwise, entering the step (5);
(2) judge robot hjWhether the captive robot exists in the range of 180 degrees on the back facing to the central direction of the multiple targets or not is judged, if yes, the step (5) is carried out, and otherwise, the step (3) is carried out;
(3) judge robot hjIf so, entering the step (4), otherwise, entering the step (5);
(4) setting a flag bit of a starting exchange target to be 1;
(5) and (6) ending.
S404A1, judging whether the target flag bit of the start exchange is 1, if yes, entering the step S404A2, otherwise, entering the step S404A 7;
S404A2, judging robot hjIf the trapping task has been allocated, if so, the process proceeds to step S404a3, otherwise,step S404a7 is entered;
S404A3, judging robot hjNumber f of neighbors in 180-degree range facing to multi-target center directionnWhether the value is greater than or equal to 1, if so, the step S404a4 is performed, otherwise, the step S404a7 is performed;
S404A4, judging robot hjWhether the previous nearest neighbor has already allocated a task, if yes, go to step S404a5, otherwise, go to step S404a 7;
S404A5, judging whether the sum of the target distances after the exchange is smaller than that before the exchange, if so, going to S404A6, otherwise, going to S404A 7;
S404A6 and robot hjExchanging the capture target with the nearest neighbor in front;
and S404A7, ending.
Through the arrangement, the invention firstly designs a motion model of multiple targets and dynamic barriers in a complex convex environment, then constructs a multiple-target simplified virtual stress model through the research on the trapping behavior in the complex environment, and provides a dynamic multiple-target self-organization task allocation method and a specific process of collaborative self-organization dynamic multiple-target trapping based on the stress model.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (10)

1. A multi-target enclosure method for cooperative operation of swarm robots in a complex convex environment is characterized by comprising the following steps:
s100, constructing a swarm robot motion model and a related function, wherein the swarm robot is composed of m identical incomplete mobile wheeled robots hjThe components of the composition are as follows,j is 1,2, …, m, and the correlation function in this step includes the robot hjKinematic equation of (a), robot, static or dynamic obstacle and non-robot h in the course of enclosurejA force application function of the enclosed target and the object and a bionic intelligent obstacle avoidance mapping function for the convex obstacle;
s200, constructing a multi-target enclosure task model and related functions through an enclosure environment, a dynamic multi-target and a dynamic barrier model, wherein the related functions in the step comprise a motion equation of a target in a complex environment and a motion equation of a dynamic barrier in a given barrier environment;
s300, constructing a multi-target simplified virtual stress model;
s400, determining a specific process of performing dynamic multi-target enclosure by the swarm robots in a cooperative manner under the environment of unknown complex convex dynamic obstacles based on the multi-target simplified virtual stress model.
2. The multi-target enclosure method for cooperative operation of swarm robots in complex convex environment according to claim 1, wherein the robots h in step S100jThe kinematic equation of (a) is as follows:
Figure FDA0002372916210000011
in the formula, vj(t) and ωj(t) are robots h, respectivelyjLinear and angular velocities of, and
Figure FDA0002372916210000012
Figure FDA0002372916210000013
respectively a maximum linear velocity and a maximum angular velocity,
Figure FDA0002372916210000014
for a robot hjThe linear velocity in the x-axis direction,
Figure FDA0002372916210000015
for a robot hjThe linear velocity in the y-axis direction,
Figure FDA0002372916210000016
for a robot hjThe angular velocity of (a);
the force application functions are respectively as follows:
Figure FDA0002372916210000017
Figure FDA0002372916210000018
in the formulae (2) and (3),
Figure FDA0002372916210000019
representing a target tpTo robot h of enclosingjMagnitude of the applied force of fo(d) Robot h for representing close-neighbor object O pair enclosurejD represents the distance between two points, c1、d1And
Figure FDA0002372916210000021
for optimizing the robot hjA path of movement of cr、c2And
Figure FDA0002372916210000022
i is 1,2,3 and 4 respectively representing that the object is the robot hjStatic, dynamic obstacles and non-robots hjSpecific parameters for use in confining the target, nc、l、dspRespectively representing the current capture step number, the step number of starting to move to an effective capture circle and the distance between the robot and the target when starting to move to the effective capture circle, wherein the effective capture circle uses the target tpAs a center of a circle, crA circumference formed by a radius;
the bionic intelligent obstacle avoidance mapping function is as follows:
Figure FDA0002372916210000023
wherein, sigma is a real number, (0 is not less than sigma and not more than 1), (-1 is not less than sigma and less than 0) is a judgment condition, and is 1 when satisfied, otherwise is 0.
3. The multi-target enclosure method for cooperative operation of swarm robots in a complex convex environment according to claim 2, wherein the dynamic multi-target and dynamic obstacle models are established through the following processes:
1) in the global coordinate system XOY, positional information of the robot and the obstacle is set to OK＝(xK,yK) K ∈ { T, H, S, U }, which includes the target T ═ TpP is 0,1, …, e, H is HjJ is 1,2, …, m, and S is SjJ-1, 2, …, α and dynamic obstacle U-Uj:j＝1,2,…,β}；
2) Determining within a target potential domain
Figure FDA0002372916210000024
The set of all robots is:
Figure FDA0002372916210000025
wherein the content of the first and second substances,
Figure FDA0002372916210000026
is the radius of the potential domain of the target,
Figure FDA0002372916210000027
is a target tpThe abscissa of the (c) axis of the (c),
Figure FDA0002372916210000028
is a target tpOrdinate of (a), xjFor a robot hjAbscissa of (a), yjFor a robot hjThe ordinate of (a);
3) the static obstacles to be avoided are respectively
Figure FDA0002372916210000029
Figure FDA00023729162100000210
And
Figure FDA00023729162100000211
Figure FDA00023729162100000212
as static obstacles sjThe abscissa of the (c) axis of the (c),
Figure FDA00023729162100000213
as static obstacles sjThe ordinate of (a) is,
Figure FDA00023729162100000214
as dynamic obstacles uiThe abscissa of the (c) axis of the (c),
Figure FDA00023729162100000215
as dynamic obstacles uiThe ordinate of (a) is,
Figure FDA0002372916210000031
is the distance at which the target begins to avoid the static obstacle.
4. The multi-target enclosure method for cooperative operation of swarm robots in complex convex environment according to claim 3, wherein the motion equation of the target in complex environment in step S200 is as follows:
Figure FDA0002372916210000032
in the formula (I), the compound is shown in the specification,
Figure FDA0002372916210000033
the acceleration of the object is represented as the acceleration,
Figure FDA0002372916210000034
respectively the walking speed and the maximum speed of the object,
Figure FDA0002372916210000035
is the speed of the object or objects,
Figure FDA0002372916210000036
representing a target tpThe magnitude of the potential of (a) is,
Figure FDA0002372916210000037
representing a target tpThe sum of the perceived potentials is,
Figure FDA0002372916210000038
is the initial wandering direction angle of the target,
Figure FDA0002372916210000039
representing a target tpThe angle of the direction of movement of (a),
Figure FDA00023729162100000310
representing a target tpThe forward direction of the potential angle of (1) represents the direction in which prey groups and static and dynamic obstacles are perceived by prey, namely the escape direction, and the reverse direction of the potential angle of (1) represents the direction in which prey groups and static and dynamic obstacles are perceived by prey, namely the confrontation direction, and represents the direction in which prey groups and dynamic obstacles are perceived by prey;
the equation of motion of a dynamic obstacle in a given obstacle environment is:
Figure FDA00023729162100000311
in the formula uiA dynamic obstacle is represented, which is,
Figure FDA00023729162100000312
indicating the speed of a dynamic obstacle，
Figure FDA00023729162100000313
The movement angle and the speed of the dynamic barrier are respectively set for the initial setting,
Figure FDA00023729162100000314
respectively the potential of a static obstacle and its azimuth relative to a dynamic obstacle,
Figure FDA00023729162100000315
the potential of the dynamic obstacle and its azimuth relative to the dynamic obstacle, d, respectively3Index parameter, d, representing enhanced avoidance of static obstacles4An index parameter representing enhanced avoidance of dynamic obstacles.
5. The multi-target enclosure method for cooperative operation of swarm robots in a complex convex environment according to claim 4, wherein the multi-target simplified virtual stress model in step S300 is specifically established through the following processes:
in the global coordinate system XOY, the robot hjThe target t can be obtainedpAnd two nearest neighbor objects Oaj,ObjAnd position information of itself, where (p ═ 1, …, e) at robot hjIn a relative coordinate system x ' O ' y ' as an origin, the robot hjSubject to a target tpAnd the action of attraction or repulsion of two nearest neighbor objects Oaj,ObjRespectively, is recorded as
Figure FDA0002372916210000041
fajAnd fbjWhen is coming into contact with
Figure FDA0002372916210000042
Target tpGenerates an attractive force when
Figure FDA0002372916210000043
Target tpGenerating repulsive force, hjIs subjected to the overall force fx'y'jIs a component from the y' axis
Figure FDA0002372916210000044
And the component f of the x' axisabjComposition and fabjIs fajAnd fbjProjections f on the x' axis respectivelyaj(||paj||)·φ(cos(γfajx') and f)bj(||pbj||)·φ(cos(γfbjx')) of a plurality of groups, wherein γ isfajx'And gammafbjx'Separate robot hjOf the two nearest neighbor object Oaj,ObjAngle of repulsion of, paj,pbjAre respectively two nearest neighbor objects Oaj,ObjThe position vector of (2).
6. The multi-target enclosure method for cooperative swarm robot operation in a complex convex environment according to claim 5, wherein the specific process of cooperative swarm robot enclosure in step S400 is as follows:
s401, setting track control and obstacle avoidance parameters, and initializing swarm robots;
s402, based on robot hjCarrying out task allocation on two nearest neighbors within a range of 180 degrees in the multi-target center direction;
s403, judging the robot hjWhether the task allocation is finished or not, if so, the step S404A is carried out, otherwise, the step S404B is carried out;
S404A, exchanging and enclosing targets;
S404B, taking a multi-target center as a capture target;
s405, calculating corresponding parameters according to the multi-target simplified virtual stress model;
s406, calculating a desired velocity vector vjeDesired direction of motion thetajeRobot hjTo the desired direction of movement thetajeRequired time tntjActual achievable velocity vjfAnd a desired velocity vector vjeCompensated velocity vjc；
S407, moving for a time step:
s408, repeating steps S402 to S407 until j ═ m;
s409, judging whether all individuals meet the following conditions:
Figure FDA0002372916210000045
and paj||-||pbj|||＜ε2Wherein, in the step (A),
Figure FDA0002372916210000046
show that robot h is caughtjTo the target tpDistance of (p)ajIndicates the neighbor OajTo the robot for enclosure hjDistance of (p)bjIndicates the neighbor ObjTo the robot for enclosure hjA distance of ∈ of1Robot h showing set enclosurejTo the target tpAs the center of a circle, crFor the magnitude of the distance error, epsilon, over the effective circumference of the radius2Robot h showing set enclosurejTo the nearest neighbors OajAnd ObjIf the error of the distance difference is large, ending; otherwise, the process returns to step S402.
7. The multi-target enclosure method for cooperative operation of swarm robots in a complex convex environment according to claim 6, wherein the step S402 specifically comprises the following steps:
s4020, judging robot hjIf the task is already allocated, the step S4029 is carried out; otherwise, the step S4021 is entered;
s4021 and calculating robot hjThe number f of neighbors facing the multi-target center direction within 180 DEGn；
S4022, judgment fnIf the number is 0, if so, the task 1 is the robot hjStep S4029, otherwise, step S4023 is performed;
s4023, judgment fnIf the number of the neighbor tasks is 1 and the tasks of the neighbor are not allocated, if so, the robot hjThe enclosure task of (1) is not allocated temporarilySetting to 0, and entering step S4029, otherwise, entering step S4024;
s4024, judgment fnIf the number of the tasks is 1 and the tasks of the neighbor are already allocated, if so, judging whether the number representation of the tasks of the neighbor is equal to the total target number, otherwise, entering the step S4025;
s4025, judgment fnIf the number of tasks is more than or equal to 2 and the tasks of two nearest neighbors are not distributed, if so, the robot hjThe hunting task is temporarily not allocated, is set to 0, and the step S4029 is performed, otherwise, the step S4026 is performed;
s4026, judgment fnWhether the number of the tasks is more than or equal to 2 and the two nearest neighbors distribute the same task is judged, if yes, whether the number representation of the tasks is equal to the total target number is judged, otherwise, the step S4027 is carried out;
s4027, judging whether the maximum value of the task numbers of the two nearest neighbors is equal to the total target number, if so, taking the task 1 as the robot hjStep S4029, otherwise, step S4028;
s4028, adding 1 to the maximum value of the two nearest neighbor task numbers to obtain a robot hjThe enclosure task of (1);
and S4029, ending.
8. The multi-target trapping method for cooperative work of swarm robots in a complex convex environment according to claim 7, wherein the steps S4024 and S4026 are to determine whether the number representation of the task is equal to the total number of targets, if yes, the task 1 is the robot hjAnd (4) performing the enclosure task, and entering the step S4029, otherwise, performing the robot hjThe hunting task of (1) is added to the task number, and the process proceeds to step S4029.
9. The multi-target enclosure method for cooperative operation of swarm robots in complex convex environment according to claim 6, wherein step S404A comprises the following steps:
S404A0, setting a flag bit of a starting exchange target;
S404A1, judging whether the target flag bit of the start exchange is 1, if yes, entering the step S404A2, otherwise, entering the step S404A 7;
S404A2, judging robot hjWhether the trapping task is allocated or not, if yes, the step S404A3 is carried out, otherwise, the step S404A7 is carried out;
S404A3, judging robot hjNumber f of neighbors in 180 DEG range facing to multi-target center directionnWhether the value is greater than or equal to 1, if so, the step S404a4 is performed, otherwise, the step S404a7 is performed;
S404A4, judging robot hjWhether the previous nearest neighbor has already allocated a task, if yes, go to step S404a5, otherwise, go to step S404a 7;
S404A5, judging whether the sum of the target distances after the exchange is smaller than that before the exchange, if so, going to S404A6, otherwise, going to S404A 7;
S404A6 and robot hjExchanging the capture target with the nearest neighbor in front;
and S404A7, ending.
10. The multi-target trapping method for cooperative operation of swarm robots in complex convex environment according to claim 9, wherein the step S404a0 of setting the start exchange target flag is implemented by the following processes:
(1) judging whether the flag bit of the starting exchange target is 0, if so, entering the step (2), otherwise, entering the step (5);
(2) judge robot hjWhether the captive robot exists in the range of 180 degrees of the back face facing to the multi-target center direction or not is judged, if yes, the step (5) is carried out, and otherwise, the step (3) is carried out;
(3) judge robot hjIf so, entering the step (4), otherwise, entering the step (5);
(4) setting a flag bit of a starting exchange target to be 1;
(5) and (6) ending.
CN202010056177.9A 2020-01-18 2020-01-18 Multi-target enclosure method for cooperative operation of swarm robots in complex convex environment Pending CN111240332A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010056177.9A CN111240332A (en) 2020-01-18 2020-01-18 Multi-target enclosure method for cooperative operation of swarm robots in complex convex environment

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010056177.9A CN111240332A (en) 2020-01-18 2020-01-18 Multi-target enclosure method for cooperative operation of swarm robots in complex convex environment

Publications (1)

Publication Number Publication Date
CN111240332A true CN111240332A (en) 2020-06-05

Family

ID=70863977

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010056177.9A Pending CN111240332A (en) 2020-01-18 2020-01-18 Multi-target enclosure method for cooperative operation of swarm robots in complex convex environment

Country Status (1)

Country Link
CN (1) CN111240332A (en)

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112462779A (en) * 2020-11-30 2021-03-09 汕头大学 Group robot dynamic capture control method and system based on gene regulation network
CN113253738A (en) * 2021-06-22 2021-08-13 中国科学院自动化研究所 Multi-robot cooperation trapping method and device, electronic equipment and storage medium
CN113268893A (en) * 2021-07-19 2021-08-17 中国科学院自动化研究所 Group trapping method and device based on communication maintenance constraint
CN113485340A (en) * 2021-07-12 2021-10-08 汕头大学 Distributed enclosure control method and system for group robots
CN115629600A (en) * 2022-08-01 2023-01-20 北方工业大学 Multi-machine cooperative trapping method in complex dynamic security environment based on buffer voronoi diagram

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105589333A (en) * 2016-02-03 2016-05-18 电子科技大学 Grouped surrounding control method of multi-agent system
CN105843227A (en) * 2016-04-15 2016-08-10 上海大学 Task intensity dynamic adjustment based multiple robots cooperating task hunting allocation algorithm
CN107562074A (en) * 2017-07-24 2018-01-09 哈尔滨工程大学 A kind of method for allocating tasks surrounded and seize towards MAUVS
CN110377059A (en) * 2019-08-29 2019-10-25 哈尔滨工程大学 A kind of more AUV person of surrounding and seize cooperative control methods based on lion group's algorithm

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105589333A (en) * 2016-02-03 2016-05-18 电子科技大学 Grouped surrounding control method of multi-agent system
CN105843227A (en) * 2016-04-15 2016-08-10 上海大学 Task intensity dynamic adjustment based multiple robots cooperating task hunting allocation algorithm
CN107562074A (en) * 2017-07-24 2018-01-09 哈尔滨工程大学 A kind of method for allocating tasks surrounded and seize towards MAUVS
CN110377059A (en) * 2019-08-29 2019-10-25 哈尔滨工程大学 A kind of more AUV person of surrounding and seize cooperative control methods based on lion group's algorithm

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
张红强: "基于简化虚拟受力模型的群机器人自组织协同围捕研究" *
张红强;章兢;周少武;曾照福;吴亮红;: "基于简化虚拟受力模型的未知复杂环境下群机器人围捕" *

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112462779A (en) * 2020-11-30 2021-03-09 汕头大学 Group robot dynamic capture control method and system based on gene regulation network
CN112462779B (en) * 2020-11-30 2023-07-25 汕头大学 Dynamic group robot trapping control method and system based on gene regulation network
CN113253738A (en) * 2021-06-22 2021-08-13 中国科学院自动化研究所 Multi-robot cooperation trapping method and device, electronic equipment and storage medium
CN113485340A (en) * 2021-07-12 2021-10-08 汕头大学 Distributed enclosure control method and system for group robots
CN113268893A (en) * 2021-07-19 2021-08-17 中国科学院自动化研究所 Group trapping method and device based on communication maintenance constraint
CN115629600A (en) * 2022-08-01 2023-01-20 北方工业大学 Multi-machine cooperative trapping method in complex dynamic security environment based on buffer voronoi diagram
CN115629600B (en) * 2022-08-01 2023-12-12 北方工业大学 Multi-machine collaborative trapping method based on buffer Wino diagram in complex dynamic security environment

Similar Documents

Publication Publication Date Title
CN111240332A (en) Multi-target enclosure method for cooperative operation of swarm robots in complex convex environment
CN111240333A (en) Multi-target enclosure method for cooperative operation of swarm robots in complex non-convex environment
Shiltagh et al. Optimal path planning for intelligent mobile robot navigation using modified particle swarm optimization
Van Den Berg et al. Optimal reciprocal collision avoidance for multi-agent navigation
CN110554607B (en) Cooperative control method and system with obstacle avoidance and navigation protection tasks for multi-Euler-Lagrange system
CN111381600A (en) UUV path planning method based on particle swarm optimization
CN111766783A (en) Cluster system-oriented formation enclosure tracking method capable of converging in limited time
Darintsev et al. Methods of a heterogeneous multi-agent robotic system group control
Tian Research on robot optimal path planning method based on improved ant colony algorithm
Fink et al. Composition of Vector Fields for Multi-Robot Manipulation via Caging.
Jang et al. Hindsight intermediate targets for mapless navigation with deep reinforcement learning
Ronzhin et al. Automatic control of robotic swarm during convex shape generation
CN115248591A (en) UUV path planning method based on hybrid initialization Hui wolf particle swarm algorithm
Moazami et al. Design, modeling, and control of norma: a slider & pendulum-driven spherical robot
CN114154383A (en) Multi-robot-source search scheme generation method and system based on cognitive search strategy
Zhao et al. Hybrid navigation method for multiple robots facing dynamic obstacles
CN111007848B (en) Multi-agent cooperative operation control method based on bounded space
OV et al. Path planning in swarm robots using particle swarm optimisation on potential fields
Zhao et al. Research on motion planning for an indoor spray arm based on an improved potential field method
Shukla et al. Multi robot path planning parameter analysis based on particle swarm optimization (PSO) in an intricate unknown environments
Jain et al. A hybridization of gravitational search algorithm and particle swarm optimization for odor source localization
Rusdinar et al. Modeling of vision based robot formation control using fuzzy logic controller and extended Kalman filter
Emrani et al. An adaptive leader-follower formation controller for multiple AUVs in spatial motions
Fiorelli Cooperative vehicle control, feature tracking and ocean sampling
Zhang et al. Herding an adversarial swarm in three-dimensional spaces

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
WD01 Invention patent application deemed withdrawn after publication

Application publication date: 20200605

WD01 Invention patent application deemed withdrawn after publication