CN116872204A - CBCA algorithm-based robot cluster dynamic alliance trapping task planning method - Google Patents
CBCA algorithm-based robot cluster dynamic alliance trapping task planning method Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
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Abstract
The invention provides a robot cluster dynamic alliance trapping task planning method based on a consistency alliance algorithm (CBCA), which belongs to the technical field of multi-robot collaborative task planning, and adopts the CBCA algorithm to carry out task planning on a robot cluster so as to realize the trapping task distribution of a plurality of dynamic targets by a plurality of mobile robots; the method comprises the steps that a trapping task rule is divided into two stages of task pre-allocation and trapping process re-planning, a temporary group is built by a robot in the pre-allocation stage according to a limited sensing range and a limited communication range, respective task packages are built, and conflicts are resolved through a consistency principle so as to build a trapping alliance; the loyalty of the robot is set so as to ensure the convergence of the task re-planning algorithm, the ordered and efficient trapping of the robot cluster to a plurality of dynamic targets is realized, and the average execution efficiency and average efficiency of the robot are improved.
Description
Technical Field
The invention relates to the technical field of multi-robot collaborative task planning, in particular to a robot cluster dynamic alliance enclosure task planning method based on a CBCA algorithm.
Background
The robot collaborative enclosure task planning algorithm has very important application value in target tracking interception, search rescue and competition countermeasures, and is one of research hotspots. At present, most of related researches on multi-robot trapping task planning are based on a centralized allocation algorithm, and a global coordinator performs unified planning. However, if a new task occurs in the environment or a new robot is added, the solving difficulty of the centralized algorithm can be increased sharply, and the expandability of the distribution algorithm and the solving efficiency are affected. Thus, the centralized algorithm is not suitable for dynamically changing acquisition task allocation scenarios.
In recent years, a consistency alliance algorithm (CBCA) in the task planning field does not need a centralized coordinator, and only performs optimization solution through information transfer and negotiation between adjacent participants, so that a robot can efficiently obtain a target distribution result and form an alliance for executing respective tasks, and therefore, the method has extensive research in robot cluster task planning. However, the consistency alliance algorithm is currently only applied to the scene of distributing the static task targets, and the information of all the task targets is required to be known in advance, so that the applicability of the scene with limited perception capability of the robot is insufficient, and improvement is needed.
Disclosure of Invention
According to the technical problem, the method for planning the robot cluster dynamic alliance trapping task based on the CBCA algorithm is provided. The invention designs a consistency alliance pre-allocation algorithm under the condition of limited sensing and communication capacity of the robot and a dynamic re-planning algorithm in the process of trapping, and provides a loyalty model to ensure the convergence of the algorithm, thereby realizing ordered and efficient trapping of a plurality of dynamic targets by a robot cluster and improving the average execution efficiency and average efficiency of the robot.
The invention adopts the following technical means:
a robot cluster dynamic alliance trapping task planning method based on a CBCA algorithm comprises the following steps:
s1, setting a trapping robot and a task target;
s2, constraint conditions are set for the trapping robot;
s3, constructing a robot kinematics model;
s4, navigating the motion of the robot by adopting an artificial potential field method, and guiding the robot to move from a starting point to an end point by the resultant force of attraction and repulsion while avoiding other robots in a motion track;
s5, constructing an objective function;
s6, setting CBCA algorithm parameters;
s7, constructing a task package based on the local detection information of the robot and the limited communication range, and forming a alliance to conduct collaborative enclosure task planning;
s8, conflict resolution based on local information interaction;
s9, re-planning the task.
Further, the step S1 specifically includes:
s11, setting a trapping robotRepresents n r Each robot is represented by a three-element method as:
X i ,E i ',l i >(1)
wherein ,Xi Is the position coordinate of the robot, E i ' list of resources carried by robot, l i A maximum number of tasks that can be received for robot i;
s12, setting task targets asRepresents n e A dynamically moving task object, each task represented by a four-element method:
X j ,E j ,L j ,V a >(2)
wherein ,Xj E is the position of the task target j Representing a list of resources required for the task, L j The number of robots required for task j indicates that task j requires multiple robotsTo complete the task of the enclosure and the catching,an initial value set for the task objective;
s13, representing various resource requirements of each task target by vectors as follows:
wherein ,representing the number of p-th resources required for trapping the task object j;
s14, in the trapping task, for different kinds of resources and the number carried by each trapping robot, the following z-dimensional vector is used for representing:
wherein ,indicating the number of p-th resources it carries.
Further, the step S2 specifically includes:
s21, setting mileage constraint of robots, wherein the movable maximum mileage of each robot is limited, namely:
wherein ,representing the maximum mileage that the robot can move;
s22, setting resource constraint of robots, wherein resources carried by each trapping robot are limited and consumed along with use, and considering that each robot needs to execute a plurality of allocated tasks in sequence, the resources need to meet the following requirements:
E(p i )≤E i '(6)
wherein ,E(pi ) Representing a robot i executing its task sequence p i Resources consumed by all targets;
s23, setting alliance resource constraint of robots, wherein the total amount of resources carried by the robot alliance constructed for the discovered target j is not less than the amount of resources required by the target, namely:
wherein ,Φj A robot alliance formed for the enclosure task j is shown.
Further, the robot kinematic model constructed in the step S3 specifically includes:
wherein ,representing the position coordinates of the ith robot at the kth step, < >>Represents the steering angle of the i-th robot, < ->Indicating the angular velocity of the i-th robot and Δt the control time step.
Further, the step S4 specifically includes:
s41, expressing the gravitation as:
wherein ,ka Represents the gravitational potential field scale factor, X i ＝(x i ,y i ) Representing the position coordinate of the ith robot, wherein the target point coordinate is that
S42, the repulsive force is expressed as:
wherein ,kr Is the repulsive force coefficient, d o Represents the radius of influence of the repulsive field, d (X i ,X q ) Is the distance X between robot i and other q-th robots q ＝(x q ,y q ) Coordinates of the mobile robot currently encountered; factors are introduced into the repulsive force functionThe aim that the target is unreachable due to overlarge repulsive force is avoided when the trapping robot goes to the destination; the repulsive force is composed of two parts, but the directions are different, +.>Is the force directed from robot i to the target, and +.>Is the force directed from the other mobile robot to robot i;
s43, when the capture robot reaches the vicinity of the target point, a capture formation is required to be formed around the target, and X is defined m ＝(x m ,y m ) Coordinates of an mth capture point surrounding the target; in the trapping range, the original gravitational field of the target point is changed into the gravitational field of the trapping point, and an attractive force model of the trapping point is obtained:
further, the step S5 specifically includes:
s51, the robot i executes the benefit r of the task object j ij (p i ) Decay with increasing time:
wherein ,Vaj The initial value of the jth target,sequence of all tasks that need to be completed in time order for robot i, t o Start time assigned to task, +.>Along the task path p for the robot i Execution target T j Is a predicted time of (2); beta is not less than 0 j Less than or equal to 1 is a task time discount factor reflecting the speed of the decrease of the target value along with time;
s52, robot i follows path p i The efficacy of (2) is as follows:
wherein ,ξij ＝d ij Gamma is the path cost of the robot, d ij For the linear distance between the robot i and the task j, gamma is a path cost coefficient;
s53, combining task allocation requirements, and describing an objective function as follows:
s.t.(5)(6)(7)
wherein ,xij E {1,0} is the decision variable in task allocation, x ij =1 indicates that the robot performs task j, x ij =0 means that the robot does not perform this task.
Further, the step S6 specifically includes:
s61, setting task package asIs a set of tasks assigned to robot i;
s62, setting a winning bid robot matrixIs n r ×n e Dimension matrix, wherein->Indicating that robot i considers robot k to be the bid winner of task j,otherwise->Z i Sum of non-zero element numbers of j th row ≡>Representing the total number of robots that robot i considers to perform task j;
s63, setting winning bid value matrix asStoring winning bid values for task j under the view of robot i>Each element in the matrix Z i One-to-one correspondence;
s64, setting a timestamp listRecording the moment when the robot i obtains updated information from other adjacent robots, wherein s ik The time when robot i obtains the latest information from robot k is shown.
Further, the step S7 specifically includes:
s71, when the robot i selects the task each time, selecting the task j with the largest gain increment, and adding the task j to the task table b i In (a) and (b); the specific rule is that each robot adds b according to the new task j i The gain of j' can be obtained as follows:
wherein n represents j' at p i P is equal to all possible positions in (1) i The I is denoted as the path list length;representing the insertion of j' into the path list p i Middle (f)n bits, the nth bit in the original path list and the elements after the nth bit keep the original sequence unchanged and sequentially move backwards;
s72, each target in the trapping task must be completed cooperatively by three or more robots, if the number of robots for executing the bidding task j meets the number of allied robots, namelyAt the moment, the number of robots for distributing and executing the task j meets the requirement, and the robots compare the marginal gain of the robots with the minimum winning bid-casting value of the current task; if it isThe robot i can bid and replace the minimum winning bid value with its own bid value; otherwise, agent i gives up task j; when the bid value of the robot i is the same as the minimum winning bid value, selecting a robot with small identity mark; the task package construction process ends when the task package is full or there are no tasks to choose from.
Further, the step S8 specifically includes:
s81, in a conflict resolution stage, if the identifiers of the winning bidders under the visual angles of the robot and the adjacent robot are the same and the time stamps are different, updating the respective winning bidder matrix so as to achieve consensus with the adjacent robot and confirm the final winning bidder;
s82, if the winning bidder identifiers of the robots and the adjacent robots in view are different, judging whether the number of winning bidders of the bidding tasks of the robots which collide has reached an upper limit, and if not, considering that the identities of all winning bidders are valid;
s83, if the number of the conflicting winners exceeds the upper limit of the number required by the task, each robot rejects other winners with minimum efficiency under the view angle and updates own winner matrix; if the robot is the winning bidder with minimum efficiency, losing the identity of the winning bidder, resetting the position of the task in the task packet sequence, and returning to the task packet construction stage again;
s84, each group is based onThe currently known task information utilizes the two steps to obtain the preallocation result in one group to form a plurality of alliances, which are expressed as
Further, the step S9 specifically includes:
s91, establishing a robot loyalty function model as follows:
wherein ,representing the percentage of each resource of the robot i to the total resources of the alliance, reflecting the importance of the robot i to the alliance; d, d ic ＝t ic Gamma is the mileage that the robot i has consumed in performing the current task, t ic The time for which the robot has traveled; mu (mu) i ＝S(b i ) Is the default cost of the robot i to select and replace other task targets, S (b) i ) Is the set of tasks b to be performed by robot i i This term represents the loss of revenue that would be needed by robot i to undertake all of its tasks not performed if it chooses to violate;
s92, assuming that a robot needs to change the execution sequence of the current task, the following formula needs to be satisfied:
S i (p i ')＞S i (p i )+σ i (23)
that is, the task path can be replaced only when a new task can bring enough efficiency to the robot, so that frequent task switching is avoided, and the convergence of an algorithm is ensured;
s93, based on the loyalty model, the task re-planning is specifically divided into the following cases:
case 1: if a certain alliance finds a new capturable target and no other alliances are capturing the target, determining whether to re-plan according to the capturing state of the alliance at present: if the current target is trapped, the original task path is kept continuously; otherwise, judging whether the value of the new target is greater than that of the current target, and selecting the target with greater value for capturing;
case 2: if the alliances which have no communication connection originally enter the communication range of each other in the respective capturing process, two cases are discussed at this time:
if a certain alliance has trapped the current task target, not participating in task re-planning;
if all the alliances do not have the trapping target, the alliances are combined into a new alliance, the task package is re-established, and consistency conflict resolution is carried out to obtain a new task path p i ' winning bid income S i (p i ') determining whether to perform the trapping task according to the new task path according to formula (23).
Compared with the prior art, the invention has the following advantages:
1. according to the CBCA algorithm-based robot cluster dynamic alliance task-planning method, a distributed consistency alliance algorithm is improved, task packages can be constructed based on local detection information of robots and limited communication range, and alliances are formed to conduct collaborative task-planning.
2. According to the robot cluster dynamic alliance trapping task planning method based on the CBCA algorithm, the trapping task planning is divided into two stages of global pre-allocation and trapping process re-planning, so that the adaptability of an allocation algorithm to dynamic change scenes is improved.
3. The CBCA algorithm-based robot cluster dynamic alliance enclosure task planning method provided by the invention provides a loyalty mathematical model of a robot to a task target, and ensures the convergence of a re-planning algorithm.
Based on the reasons, the method can be widely popularized in the fields of multi-robot collaborative task planning and the like.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to the drawings without inventive effort to a person skilled in the art.
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a state diagram of a trapping process according to an embodiment of the present invention.
FIG. 3 is a diagram showing a task target number n according to an embodiment of the present invention e Average system performance and execution time profile at=30.
Detailed Description
It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other. The invention will be described in detail below with reference to the drawings in connection with embodiments.
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the invention, its application, or uses. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present invention. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
The relative arrangement of the components and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless it is specifically stated otherwise. Meanwhile, it should be clear that the dimensions of the respective parts shown in the drawings are not drawn in actual scale for convenience of description. Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail, but are intended to be part of the specification where appropriate. In all examples shown and discussed herein, any specific values should be construed as merely illustrative, and not a limitation. Thus, other examples of the exemplary embodiments may have different values. It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further discussion thereof is necessary in subsequent figures.
In the description of the present invention, it should be understood that the azimuth or positional relationships indicated by the azimuth terms such as "front, rear, upper, lower, left, right", "lateral, vertical, horizontal", and "top, bottom", etc., are generally based on the azimuth or positional relationships shown in the drawings, merely to facilitate description of the present invention and simplify the description, and these azimuth terms do not indicate and imply that the apparatus or elements referred to must have a specific azimuth or be constructed and operated in a specific azimuth, and thus should not be construed as limiting the scope of protection of the present invention: the orientation word "inner and outer" refers to inner and outer relative to the contour of the respective component itself.
Spatially relative terms, such as "above … …," "above … …," "upper surface at … …," "above," and the like, may be used herein for ease of description to describe one device or feature's spatial location relative to another device or feature as illustrated in the figures. It will be understood that the spatially relative terms are intended to encompass different orientations in use or operation in addition to the orientation depicted in the figures. For example, if the device in the figures is turned over, elements described as "above" or "over" other devices or structures would then be oriented "below" or "beneath" the other devices or structures. Thus, the exemplary term "above … …" may include both orientations of "above … …" and "below … …". The device may also be positioned in other different ways (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted accordingly.
In addition, the terms "first", "second", etc. are used to define the components, and are only for convenience of distinguishing the corresponding components, and the terms have no special meaning unless otherwise stated, and therefore should not be construed as limiting the scope of the present invention.
As shown in fig. 1, the invention provides a robot cluster dynamic alliance trapping task planning method based on a CBCA algorithm, which comprises the following steps:
s1, setting a trapping robot and a task target;
s2, constraint conditions are set for the trapping robot;
s3, constructing a robot kinematics model;
s4, navigating the motion of the robot by adopting an artificial potential field method, and guiding the robot to move from a starting point to an end point by the resultant force of attraction and repulsion while avoiding other robots in a motion track;
s5, constructing an objective function;
s6, setting CBCA algorithm parameters;
s7, constructing a task package based on the local detection information of the robot and the limited communication range, and forming a alliance to conduct collaborative enclosure task planning;
s8, conflict resolution based on local information interaction;
s9, re-planning the task.
In specific implementation, as a preferred embodiment of the present invention, the step S1 specifically includes:
s11, setting a trapping robotRepresents n r Each robot is represented by a three-element method as:
X i ,E i ',l i >(1)
wherein ,Xi Is the position coordinate of the robot, E i ' list of resources carried by robot, l i A maximum number of tasks that can be received for robot i;
s12, setting task targets asRepresents n e A dynamically moving task object, each task represented by a four-element method:
X j ,E j ,L j ,V a >(2)
wherein ,Xj E is the position of the task target j Representing a list of resources required for the task, L j The number of robots required for task j, which means that task j requires multiple robots to complete the trapping task,an initial value set for the task objective;
s13, representing various resource requirements of each task target by vectors as follows:
wherein ,representing the number of p-th resources required for trapping the task object j;
s14, in the trapping task, for different kinds of resources and the number carried by each trapping robot, the following z-dimensional vector is used for representing:
wherein ,indicating the number of p-th resources it carries.
In specific implementation, as a preferred embodiment of the present invention, the step S2 specifically includes:
s21, setting mileage constraint of robots, wherein the movable maximum mileage of each robot is limited, namely:
wherein ,representing the maximum mileage that the robot can move;
s22, setting resource constraint of robots, wherein resources carried by each trapping robot are limited and consumed along with use, and considering that each robot needs to execute a plurality of allocated tasks in sequence, the resources need to meet the following requirements:
E(p i )≤E i '(6)
wherein ,E(pi ) Representing a robot i executing its task sequence p i Resources consumed by all targets;
s23, setting alliance resource constraint of robots, wherein the total amount of resources carried by the robot alliance constructed for the discovered target j is not less than the amount of resources required by the target, namely:
wherein ,Φj A robot alliance formed for the enclosure task j is shown.
In specific implementation, as a preferred embodiment of the present invention, the robot kinematic model constructed in the step S3 specifically includes:
wherein ,representing the position coordinates of the ith robot at the kth step, < >>Represents the steering angle of the i-th robot, < ->Indicating the angular velocity of the i-th robot and Δt the control time step.
In specific implementation, as a preferred embodiment of the present invention, the step S4 specifically includes:
s41, expressing the gravitation as:
wherein ,ka Represents the gravitational potential field scale factor, X i ＝(x i ,y i ) Representing the position coordinate of the ith robot, wherein the target point coordinate is that
S42, the repulsive force is expressed as:
wherein ,kr Is the repulsive force coefficient, d o Represents the radius of influence of the repulsive field, d (X i ,X q ) Is the distance X between robot i and other q-th robots q ＝(x q ,y q ) Coordinates of the mobile robot currently encountered; factors are introduced into the repulsive force functionThe aim that the target is unreachable due to overlarge repulsive force is avoided when the trapping robot goes to the destination; the repulsive force is composed of two parts, but the directions are different, +.>Is the force directed from robot i to the target, and +.>Is the force directed from the other mobile robot to robot i;
s43, when the capture robot reaches the vicinity of the target point, a capture formation is required to be formed around the target, and X is defined m ＝(x m ,y m ) Coordinates of an mth capture point surrounding the target; in the trapping range, the original gravitational field of the target point is changed into the gravitational field of the trapping point, and an attractive force model of the trapping point is obtained:
in specific implementation, as a preferred embodiment of the present invention, the step S5 specifically includes:
s51, the robot i executes the benefit r of the task object j ij (p i ) Decay with increasing time:
wherein ,Vaj The initial value of the jth target,sequence of all tasks that need to be completed in time order for robot i, t o Start time assigned to task, +.>Along the task path p for the robot i Execution target T j Is a predicted time of (2); beta is not less than 0 j Less than or equal to 1 is a task time discount factor reflecting the speed of the decrease of the target value along with time;
s52, robot i follows path p i The efficacy of (2) is as follows:
wherein ,ξij ＝d ij Gamma is the path cost of the robot, d ij For the linear distance between the robot i and the task j, gamma is a path cost coefficient;
s53, combining task allocation requirements, and describing an objective function as follows:
s.t.(5)(6)(7)
wherein ,xij E {1,0} is the decision variable in task allocation, x ij =1 indicates that the robot performs task j, x ij =0 means that the robot does not perform this task.
In specific implementation, as a preferred embodiment of the present invention, the step S6 specifically includes:
s61, setting task package asIs a set of tasks assigned to robot i;
s62, setting a winning bid robot matrixIs n r ×n e Dimension matrix, wherein->Indicating that robot i considers robot k to be the bid winner of task j, otherwise +.>Z i Sum of non-zero element numbers of j th row ≡>Representing the total number of robots that robot i considers to perform task j;
s63, setting winning bid value matrix asStoring winning bid values for task j under the view of robot i>Each element in the matrixZ i One-to-one correspondence;
s64, setting a timestamp listRecording the moment when the robot i obtains updated information from other adjacent robots, wherein s ik The time when robot i obtains the latest information from robot k is shown.
The multi-target trapping task distribution method based on the CBCA algorithm comprises the steps of task package construction, conflict resolution and the like, and as the scene that each trapping robot has limited detection capability is considered, attribute information, bidding information and the like of a task need to be transmitted through neighbors. Thus, each trapping robot forms a plurality of temporary subgroups, denoted as ψ, according to the detection range prior to task allocation s The method comprises all robots capable of forming a communication link, wherein the robots in the group can share task information and construct respective task packages based on the shared information, and the specific flow of the algorithm is shown in figure 1.
In specific implementation, as a preferred embodiment of the present invention, the step S7 specifically includes:
s71, when the robot i selects the task each time, selecting the task j with the largest gain increment, and adding the task j to the task table b i In (a) and (b); the specific rule is that each robot adds b according to the new task j i The gain of j' can be obtained as follows:
wherein n represents j' at p i P is equal to all possible positions in (1) i The I is denoted as the path list length;representing the insertion of j' into the path list p i The nth bit in the original path list and the following elements keep the original sequence unchanged and sequentially move backwards;
s72, three targets are needed in the trapping taskThe robots cooperate to complete if the number of robots for executing task j meets the number of allied robots, i.e.At the moment, the number of robots for distributing and executing the task j meets the requirement, and the robots compare the marginal gain of the robots with the minimum winning bid-casting value of the current task; if it isThe robot i can bid and replace the minimum winning bid value with its own bid value; otherwise, agent i gives up task j; when the bid value of the robot i is the same as the minimum winning bid value, selecting a robot with small identity mark; the task package construction process ends when the task package is full or there are no tasks to choose from.
In specific implementation, as a preferred embodiment of the present invention, the step S8 specifically includes:
s81, in a conflict resolution stage, if the identifiers of the winning bidders under the visual angles of the robot and the adjacent robot are the same and the time stamps are different, updating the respective winning bidder matrix so as to achieve consensus with the adjacent robot and confirm the final winning bidder;
s82, if the winning bidder identifiers of the robots and the adjacent robots in view are different, judging whether the number of winning bidders of the bidding tasks of the robots which collide has reached an upper limit, and if not, considering that the identities of all winning bidders are valid;
s83, if the number of the conflicting winners exceeds the upper limit of the number required by the task, each robot rejects other winners with minimum efficiency under the view angle and updates own winner matrix; if the robot is the winning bidder with minimum efficiency, losing the identity of the winning bidder, resetting the position of the task in the task packet sequence, and returning to the task packet construction stage again;
s84, each group obtains a preassigned result in one group based on the currently known task information by utilizing the two steps to form a plurality of alliances, which are expressed as
In specific implementation, as a preferred embodiment of the present invention, the step S9 specifically includes:
s91, establishing a robot loyalty function model as follows:
wherein ,representing the percentage of each resource of the robot i to the total resources of the alliance, reflecting the importance of the robot i to the alliance; d, d ic ＝t ic Gamma is the mileage that the robot i has consumed in performing the current task, t ic The time for which the robot has traveled; mu (mu) i ＝S(b i ) Is the default cost of the robot i to select and replace other task targets, S (b) i ) Is the set of tasks b to be performed by robot i i This term represents the loss of revenue that would be needed by robot i to undertake all of its tasks not performed if it chooses to violate;
s92, assuming that a robot needs to change the execution sequence of the current task, the following formula needs to be satisfied:
S i (p i ')＞S i (p i )+σ i (23)
that is, the task path can be replaced only when a new task can bring enough efficiency to the robot, so that frequent task switching is avoided, and the convergence of an algorithm is ensured;
s93, based on the loyalty model, the task re-planning is specifically divided into the following cases:
case 1: if a certain alliance finds a new capturable target and no other alliances are capturing the target, determining whether to re-plan according to the capturing state of the alliance at present: if the current target is trapped, the original task path is kept continuously; otherwise, judging whether the value of the new target is greater than that of the current target, and selecting the target with greater value for capturing;
case 2: if the alliances which have no communication connection originally enter the communication range of each other in the respective capturing process, two cases are discussed at this time:
if a certain alliance has trapped the current task target, not participating in task re-planning;
if all the alliances do not have the trapping target, the alliances are combined into a new alliance, the task package is re-established, and consistency conflict resolution is carried out to obtain a new task path p i ' winning bid income S i (p i ') determining whether to perform the trapping task according to the new task path according to formula (23).
Example 1
In the present invention, MATLAB 2020b simulation software is utilized to simulate and verify the proposed algorithm, and performance comparison is performed with the existing algorithm to verify the advantages of the proposed algorithm. The simulation experiment is carried out in an environment area of 10km multiplied by 10km, the radius of the detection range of each robot is 1.2km, the communication radius between the robots is 1.5km, and the maximum execution task number of the robots is l i And 3.
In this embodiment, the captured target remains in a silent state before being captured, and after the capturing robot starts capturing, the task target starts escaping in a direction of a central line of a maximum included angle formed between the position of the adjacent capturing robot and the target position.
TABLE 1 task goal initial parameters
Table 2 robot initial parameters
In this example 1, there are 5 targets and 15 robots in total in the task environment, and other parameters concerning the task targets and robots are shown in tables 1 and 2. Under the condition that the observation capability and the communication range of the robot are limited, the improved CBCA algorithm is applied to one trapping instance scene to illustrate the whole process of the multi-robot cooperative trapping. The initial positions of the robot and the trapped object are shown in fig. 2 (a), in which a blue triangle represents the robot, a red dot represents the object, and all the trapped robots in the task space are formed into two temporary subgroups ψ according to the detection range and the communication distance 1 、Ψ 2 . FIG. 2 (b) shows the result of each temporary group being assigned by CBCA algorithm, resulting in a federation of phi 1 ＝{R 1 ,R 15 ,R 5 }，Φ 2 ＝{R 2 ,R 4 ,R 6 }，Φ 3 ＝{R 7 ,R 8 ,R 9 }，Φ 4 ＝{R 13 ,R 12 ,R 11 ,R 10 }. Each trapping robot in fig. 2 (c) has begun trapping and members of two temporary teams enter the respective communication ranges, thus forming a new large alliance. The new federation performs a further mission planning by means of a re-planning strategy of the CBCA algorithm. As can be seen from FIG. 2 (d), R 9 Joining task T by re-planning the task path 5 In the execution of (2), R 15 Add to task T 3 In the execution of (2), R 9 and R15 If the new target is executed, the obtained performance is {204.39,104.53} greater than the performance obtained from the original target {110.56,36.32}. T (T) 1 Since T is originally executed in the alliance of (1) 1 R of task 15 Other tasks have been performed and T is not met at the time of rescheduling 1 The resource requirements of (1) are linked by the capture, thus T 1 The task is put aside waiting for other robot alliances to discover and re-plan the task. Fig. 2 (e) shows that each trapping robot has reached its own trapping point to trap each task object.
Example 2
In example 2, when the number of robots n in the environment r When changing, using the algorithm proposed in the present inventionCompared to the average system performance and average task completion time of a modified contractual network algorithm. As can be seen from fig. 3 (a), when the number of robots is changed, the average performance value of the proposed algorithm is higher than that of the contract net algorithm, and as the number of robots is increased, the performance of the proposed algorithm is always higher than that of the contract net algorithm, and when the number of robots is continuously increased, the curve shows a state of being stable. This is because with a sufficient number of robots, the task allocation algorithm gradually finds the best robot combination. Fig. 3 (b) shows a comparison of the average completion times of the robots for both algorithms. The average completion time of the two algorithms can be seen to be in a decreasing trend, because the number of robots in the environment is continuously increased, so that the distance between the task to be executed by the robot and the robot is relatively short, and the completion time of the robot for executing the trapping task is gradually decreased. Moreover, the average task completion time of the proposed algorithm is lower than that of the contract net algorithm, because the proposed algorithm reduces the interaction frequency between robots, so that the solving operation time is slightly reduced, and the trapping efficiency is improved.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (10)
1. A robot cluster dynamic alliance trapping task planning method based on a CBCA algorithm is characterized by comprising the following steps:
s1, setting a trapping robot and a task target;
s2, constraint conditions are set for the trapping robot;
s3, constructing a robot kinematics model;
s4, navigating the motion of the robot by adopting an artificial potential field method, and guiding the robot to move from a starting point to an end point by the resultant force of attraction and repulsion while avoiding other robots in a motion track;
s5, constructing an objective function;
s6, setting CBCA algorithm parameters;
s7, constructing a task package based on the local detection information of the robot and the limited communication range, and forming a alliance to conduct collaborative enclosure task planning;
s8, conflict resolution based on local information interaction;
s9, re-planning the task.
2. The method for planning the task of capturing by the dynamic alliance of the robot cluster based on the CBCA algorithm according to claim 1, wherein the step S1 specifically includes:
s11, setting a trapping robotRepresents n r Each robot is represented by a three-element method as:
<X i ,E′ i ,l i > (1)
wherein ,Xi Is the position coordinate of the robot, E' i Resource list carried by robot, l i A maximum number of tasks that can be received for robot i;
s12, setting task targets asRepresents n e A dynamically moving task object, each task represented by a four-element method:
<X j ,E j ,L j ,V a > (2)
wherein ,Xj E is the position of the task target j Representing a list of resources required for the task, L j The number of robots required for task j indicates that task j requires multiple robots to complete the task of trapping，An initial value set for the task objective;
s13, representing various resource requirements of each task target by vectors as follows:
wherein ,representing the number of p-th resources required for trapping the task object j;
s14, in the trapping task, for different kinds of resources and the number carried by each trapping robot, the following z-dimensional vector is used for representing:
wherein ,indicating the number of p-th resources it carries.
3. The method for planning the task of capturing by the dynamic alliance of the robot cluster based on the CBCA algorithm according to claim 1, wherein the step S2 specifically includes:
s21, setting mileage constraint of robots, wherein the movable maximum mileage of each robot is limited, namely:
wherein ,representing the maximum mileage that the robot can move;
s22, setting resource constraint of robots, wherein resources carried by each trapping robot are limited and consumed along with use, and considering that each robot needs to execute a plurality of allocated tasks in sequence, the resources need to meet the following requirements:
E(p i )≤E i ' (6) wherein E (p i ) Representing a robot i executing its task sequence p i Resources consumed by all targets;
s23, setting alliance resource constraint of robots, wherein the total amount of resources carried by the robot alliance constructed for the discovered target j is not less than the amount of resources required by the target, namely:
wherein ,Φj A robot alliance formed for the enclosure task j is shown.
4. The CBCA algorithm-based robot cluster dynamic alliance enclosure task planning method according to claim 1, wherein the robot kinematic model constructed in step S3 specifically comprises:
wherein ,representing the position coordinates of the ith robot at the kth step, < >>Represents the steering angle of the i-th robot, < ->Indicating the angular velocity of the i-th robot and Δt the control time step.
5. The method for planning the task of capturing by the dynamic alliance of the robot cluster based on the CBCA algorithm according to claim 1, wherein the step S4 specifically includes:
s41, expressing the gravitation as:
wherein ,ka Represents the gravitational potential field scale factor, X i ＝(x i ,y i ) Representing the position coordinate of the ith robot, wherein the target point coordinate is that
S42, the repulsive force is expressed as:
wherein ,kr Is the repulsive force coefficient, d o Represents the radius of influence of the repulsive field, d (X i ,X q ) Is robot i and otherDistance between q robots, X q ＝(x q ,y q ) Coordinates of the mobile robot currently encountered; factors are introduced into the repulsive force functionThe aim that the target is unreachable due to overlarge repulsive force is avoided when the trapping robot goes to the destination; the repulsive force is composed of two parts, but the directions are different, +.>Is the force directed from robot i to the target, and +.>Is the force directed from the other mobile robot to robot i;
s43, when the capture robot reaches the vicinity of the target point, a capture formation is required to be formed around the target, and X is defined m ＝(x m ,y m ) Coordinates of an mth capture point surrounding the target; in the trapping range, the original gravitational field of the target point is changed into the gravitational field of the trapping point, and an attractive force model of the trapping point is obtained:
6. the method for planning the task of capturing by the dynamic alliance of the robot cluster based on the CBCA algorithm according to claim 1, wherein the step S5 specifically includes:
s51, the robot i executes the benefit r of the task object j ij (p i ) Decay with increasing time:
wherein ,Vaj Initial stage of jth targetThe initial value of the product is calculated,sequence of all tasks that need to be completed in time order for robot i, t o Start time assigned to task, t (pi) Along the task path p for the robot i Execution target T j Is a predicted time of (2); beta is not less than 0 j Less than or equal to 1 is a task time discount factor reflecting the speed of the decrease of the target value along with time;
s52, robot i follows path p i The efficacy of (2) is as follows:
wherein ,ξij ＝d ij Gamma is the path cost of the robot, d ij For the linear distance between the robot i and the task j, gamma is a path cost coefficient;
s53, combining task allocation requirements, and describing an objective function as follows:
s.t.(5)(6)(7)
wherein ,xij E {1,0} is the decision variable in task allocation, x ij =1 indicates that the robot performs task j, x ij =0 means that the robot does not perform this task.
7. The method for planning the task of capturing by the dynamic alliance of the robot cluster based on the CBCA algorithm according to claim 1, wherein the step S6 specifically includes:
s61, setting task package asIs a set of tasks assigned to robot i;
s62, setting a winning bid robot matrixIs n r ×n e Dimension matrix, wherein->Indicating that robot i considers robot k to be the bid winner of task j, otherwise +.>Z i Sum of non-zero element numbers of j th row ≡>Representing the total number of robots that robot i considers to perform task j;
s63, setting winning bid value matrix asStoring winning bid values for task j under the view of robot i>Each element in the matrix Z i One-to-one correspondence;
s64, setting a timestamp listRecording the moment when the robot i obtains updated information from other adjacent robots, wherein s ik The time when robot i obtains the latest information from robot k is shown.
8. The method for planning the task of capturing by the dynamic alliance of the robot cluster based on the CBCA algorithm according to claim 1, wherein the step S7 specifically includes:
s71, when the robot i selects the task each time, selecting the task j with the largest gain increment, and adding the task j to the task table b i In (a) and (b); the specific rule is that each robot adds b according to the new task j i The gain of j' can be obtained as follows:
wherein n represents j' at p i P is equal to all possible positions in (1) i The I is denoted as the path list length;representing the insertion of j' into the path list p i The nth bit in the original path list and the following elements keep the original sequence unchanged and sequentially move backwards;
s72, each target in the trapping task must be completed cooperatively by three or more robots, if the number of robots for executing the bidding task j meets the number of allied robots, namelyAt the moment, the number of robots for distributing and executing the task j meets the requirement, and the robots compare the marginal gain of the robots with the minimum winning bid-casting value of the current task; if it isRobot i can bid and will be the mostThe small winning bid amount is replaced by the own bid amount; otherwise, agent i gives up task j; when the bid value of the robot i is the same as the minimum winning bid value, selecting a robot with small identity mark; the task package construction process ends when the task package is full or there are no tasks to choose from.
9. The method for planning the task of capturing by the dynamic alliance of the robot cluster based on the CBCA algorithm according to claim 1, wherein the step S8 specifically includes:
s81, in a conflict resolution stage, if the identifiers of the winning bidders under the visual angles of the robot and the adjacent robot are the same and the time stamps are different, updating the respective winning bidder matrix so as to achieve consensus with the adjacent robot and confirm the final winning bidder;
s82, if the winning bidder identifiers of the robots and the adjacent robots in view are different, judging whether the number of winning bidders of the bidding tasks of the robots which collide has reached an upper limit, and if not, considering that the identities of all winning bidders are valid;
s83, if the number of the conflicting winners exceeds the upper limit of the number required by the task, each robot rejects other winners with minimum efficiency under the view angle and updates own winner matrix; if the robot is the winning bidder with minimum efficiency, losing the identity of the winning bidder, resetting the position of the task in the task packet sequence, and returning to the task packet construction stage again;
s84, each group obtains a preassigned result in one group based on the currently known task information by utilizing the two steps to form a plurality of alliances, which are expressed as
10. The method for planning the task of capturing by the dynamic alliance of the robot cluster based on the CBCA algorithm according to claim 1, wherein the step S9 specifically includes:
s91, establishing a robot loyalty function model as follows:
wherein ,representing the percentage of each resource of the robot i to the total resources of the alliance, reflecting the importance of the robot i to the alliance; d, d ic ＝t ic Gamma is the mileage that the robot i has consumed in performing the current task, t ic The time for which the robot has traveled; mu (mu) i ＝S(b i ) Is the default cost of the robot i to select and replace other task targets, S (b) i ) Is the set of tasks b to be performed by robot i i This term represents the loss of revenue that would be needed by robot i to undertake all of its tasks not performed if it chooses to violate;
s92, assuming that a robot needs to change the execution sequence of the current task, the following formula needs to be satisfied:
S i (p i ')＞S i (p i )+σ i (23)
that is, the task path can be replaced only when a new task can bring enough efficiency to the robot, so that frequent task switching is avoided, and the convergence of an algorithm is ensured;
s93, based on the loyalty model, the task re-planning is specifically divided into the following cases:
case 1: if a certain alliance finds a new capturable target and no other alliances are capturing the target, determining whether to re-plan according to the capturing state of the alliance at present: if the current target is trapped, the original task path is kept continuously; otherwise, judging whether the value of the new target is greater than that of the current target, and selecting the target with greater value for capturing;
case 2: if the alliances which have no communication connection originally enter the communication range of each other in the respective capturing process, two cases are discussed at this time:
if a certain alliance has trapped the current task target, not participating in task re-planning;
if none of the federations have trapped targets, they merge into a new federation, reestablishing
Task package and consistency conflict resolution are carried out to obtain a new task path p i ' winning bid income S i (p i ')，
And judging whether to execute the trapping task according to the new task path according to a formula (23).
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