A robot cluster formation and cooperative hunting method based on target allocation

Abstract

The application discloses a kind of robot cluster formation and cooperative hunting method based on target allocation, belong to robot control technical field.For different geometric configuration target formation, construct different initial target allocation strategy, based on differential model to robot dynamic motion control until realizing target formation;In motion control, introduce global target redistribution mechanism, neighborhood global information sharing mechanism and target exchange mechanism, effectively eliminate the delay of convergence caused by individual robot distance target point too far, significantly reduce the construction time of formation.On the basis of this cluster formation method, the application derives a kind of cooperative hunting method, by designing clustering rule to dynamically generate target point, while achieving hunting target, can adaptively switch hunting formation, significantly enhance the flexibility and environmental adaptability of hunting task in complex environment, when formation formation or the position set of escapee changes, the method can quickly migrate and apply.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of robot control, and particularly relates to a robot cluster formation and cooperative hunting method based on target assignment. BACKGROUND

[0002] In recent years, robot cluster formation and hunting technology has attracted much attention due to its great application prospects in search and rescue, environmental monitoring, intelligent logistics and other fields. Among them, cluster formation aims to form a predetermined geometric configuration through multi-robot cooperative control to perform complex cooperative tasks; hunting technology focuses on tracking, surrounding or capturing targets in a specific area, and typical applications include search and rescue of trapped personnel in disaster scenarios and monitoring and driving away of wild animals in ecological protection. Given its wide application value, efficient and robust cluster formation and hunting algorithms have become a key research direction for domestic and foreign research institutions.

[0003] However, existing cluster formation algorithms still face many challenges in practical applications. On the one hand, traditional algorithms usually adopt a unified control strategy, which leads to a significant increase in the overall formation time and reduces the formation efficiency of individual robots due to the initial position being too far from the target point. On the other hand, existing hunting algorithms rely on fixed hunting formations, which are difficult to adaptively adjust according to the motion state of the escaping target, thereby affecting the effectiveness and persistence of the hunting effect. Such adaptability and robustness problems are particularly prominent in dynamic environments. SUMMARY

[0004] The present application aims to solve the above technical defects and proposes a robot cluster formation and cooperative hunting method based on target assignment. The goal is to optimize the cluster target selection strategy to improve the formation speed, shorten the total movement distance of the cluster, reduce energy consumption, and dynamically adjust the hunting formation according to the target escape behavior to achieve flexible and efficient hunting.

[0005] The specific technical solutions of the present application are as follows:

[0006] In a first aspect, the present application proposes a robot cluster formation method based on target assignment, comprising:

[0007] assigning an initial target index to each robot according to the target formation of the cluster formation, and performing dynamic motion control on the robot based on a differential model until the target formation is achieved; introducing a global target reassignment mechanism, a global information sharing mechanism within the neighborhood, and a target exchange mechanism during the process of achieving the target formation;

[0008] The global target re-allocation mechanism refers to that when the target formation is a closed polygon with adjacent target points equidistantly distributed and the proportion of robots that have reached the target points exceeds a first threshold, robots that have not reached the target points and are away from the target points by more than a second threshold are recorded as lagging robots, the target points closest to the lagging robots are selected as starting points, the target points closest to the starting points and not occupied are selected as ending points, the robots on the paths from the starting points to the ending points are sequentially allocated target points and locked, and the target re-allocation is realized to accelerate the convergence of the formation;

[0009] The global information sharing mechanism in the neighborhood refers to that the global information in the neighborhood is shared through the neighborhood communication of the robots, the unallocated target points discovered by the robots are marked as candidate target points, and the gradient values of the robots are updated to dynamically allocate the candidate target points.

[0010] The target exchange mechanism refers to that in the neighborhood, the target points of the robots are iteratively optimized to minimize the total time and total path distance of the formation.

[0011] Further, the initial target index is allocated to each robot according to the target formation of the cluster formation, including:

[0012] When the target formation is a polygon combination with the center line parallel to the coordinate axis, the robots are first grouped according to the clustering of the coordinate positions, and then the center allocation strategy or the random allocation strategy in the group is executed.

[0013] The rest of the target formation adopts the center allocation strategy.

[0014] The center allocation strategy specifically refers to that the cluster center position of the robots is first calculated, and then the target points are sequentially allocated to the nearest idle robots according to the distance from the target points to the cluster center position from far to near.

[0015] Further, the dynamic motion control of the robots is performed based on the differential model until the target formation is achieved, including:

[0016] The guide speed component and the collision avoidance speed component of the robot are calculated and combined, and then the combined speed is obtained by weighting the speed at the previous moment.

[0017] The combined speed is converted into left and right wheel speeds, the amplitude constraint of the wheel speed is introduced, the linear speed and angular speed of the robot are solved through the forward kinematics model, and the position and attitude of the robot are updated.

[0018] Further, in the global target re-allocation mechanism, the robots are unlocked after reaching the re-allocated target points, and the target points in the locked state do not participate in the target exchange mechanism.

[0019] In the second aspect, the present application provides a robot cooperative hunting method based on the robot cluster formation method described above, including the following steps:

[0020] (1) Based on the distance matrix of the trapper and the escapee, a number-balanced trapper is assigned to each escapee, and the corresponding relationship between the trapper and the escapee is unchanged throughout the trapping process;

[0021] (2) Dynamic target point generation: based on the distance matrix of the escapee position, dynamic clustering grouping is performed, the center and radius of each cluster are calculated, the cluster center position is adjusted through global iterative optimization to avoid trapping circle overlap, and target points equal to the number of trappers are generated in the circumferential direction of the trapping circle;

[0022] (3) Initial target point allocation: if the cluster exists in the historical grouping, the historical target point allocation scheme is reused, otherwise, the distance priority principle is used to allocate the initial target point to the trapper within the cluster, and when the target points conflict, the target point is allocated to the trapper farthest from the target point, and the remaining trappers reselect the nearest target point from the remaining target points;

[0023] (4) Motion control: using the above robot cluster formation method, based on the global target re-allocation mechanism, the global information sharing mechanism within the neighborhood, and the target exchange mechanism, the trapper corresponding to each trapping circle is controlled to move towards the target point direction to form the target formation of the trapping circle;

[0024] (5) Real-time trapping state judgment, if the trapping is completed, it is ended, otherwise, step (2) is returned at each updated target position moment until the trapping is completed.

[0025] Further, the process of global iterative optimization adjusting the cluster center position includes:

[0026] Detect the center and radius of the new cluster and the existing cluster, judge whether the trapping circle of the new cluster overlaps with the trapping circle of the existing cluster, if yes, translate the new cluster center along the center line vector direction of the conflict cluster until all the trapping circles of the clusters are not overlapped.

[0027] Further, in the process of dynamic clustering grouping, it also includes:

[0028] Judge whether the current cluster is included in the cluster grouping at the previous moment, if yes, and the changes of the cluster center and the radius are both less than the threshold, the cluster center and the radius of the cluster in the last round of cluster grouping are used, otherwise, the calculated cluster center and radius are used.

[0029] Thirdly, the present application provides a robot cooperative trapping system for realizing the above-mentioned robot cooperative trapping method.

[0030] Fourthly, the present application provides a computer readable storage medium, which stores a computer program, when the computer program is executed by a processor, the above-mentioned robot cluster formation method based on target allocation is realized.

[0031] In a fifth aspect, the present application provides a computer electronic device, comprising a memory and a processor;

[0032] The memory is configured to store a computer program;

[0033] The processor is configured to implement the target-based robot swarm formation method as described above when executing the computer program.

[0034] The present application has the following beneficial effects:

[0035] (1) To solve the problem of slow formation of the formation caused by the late arrival of a few robots at the target position, the present application constructs a global target redistribution mechanism, optimizes the swarm target selection strategy in real time, effectively shortens the time required for formation and improves the overall system performance; At the same time, the global information sharing mechanism and target exchange mechanism in the neighborhood are introduced to optimize the dynamic motion control logic.

[0036] (2) Based on the target space distribution characteristics of the escapee, the present application designs a dynamic target point generation method based on clustering rules. This method can achieve the surrounding capture target while realizing the adaptive switching of the surrounding formation, significantly enhancing the flexibility and environmental adaptability of the surrounding task in complex environments.

[0037] (3) The present application has good portability. When the formation formation or the escapee position set changes, only the corresponding target position parameters need to be adjusted to realize the rapid migration and application of the method, avoiding large-scale redesign and debugging. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 is the overall flowchart of the swarm formation method.

[0039] Figure 2 is the principle diagram of the center allocation algorithm for initial target allocation.

[0040] Figure 3 is the complete running flowchart of a single robot in a cycle.

[0041] Figure 4 is the overall flowchart of the cooperative hunting method.

[0042] Figure 5 is the flowchart of the initial allocation algorithm of the hunting group.

[0043] Figure 6 is the algorithm flowchart for calculating the escapee clustering and the surrounding target point set.

[0044] Figure 7 is the flowchart of the clustering internal target allocation algorithm.

[0045] Figure 8 is a schematic diagram of the formation of the "ZJU" formation process of the cluster formation.

[0046] Figure 9 is a schematic diagram of the switching process from the "ZJU" formation to the "ROBOT" formation.

[0047] Figure 10 is a schematic diagram of the switching process from the "circular" formation to the "eagle" formation.

[0048] Figure 11 is a schematic diagram of the clustering merging process due to the change in the position of the escapee in the hunting process.

[0049] Figure 12 is a schematic diagram of the clustering decomposition process due to the change in the position of the escapee in the hunting process.

[0050] Figure 13 is a schematic diagram of the process from the "escapee / hunter position initialization" to the "cooperative hunting task completion".

[0051] Figure 14 is a schematic diagram of the successful completion of the hunting task by the hunters due to the change in the position of the escapee after the completion of the hunting task. DETAILED DESCRIPTION

[0052] The functions of the cluster formation and cooperative hunting of the present application will be further described in conjunction with specific embodiments. It should be understood by those skilled in the art that the embodiments described below are only for illustration of the present application and should not be considered as limiting the scope of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0053] The present application proposes a robot cluster formation and cooperative hunting method based on target allocation, aiming to optimize the target point selection of the robot cluster through strategies such as differentiated initial target allocation method and global target re-allocation mechanism, thereby improving the formation speed, reducing the total movement distance of the cluster in the formation process, and reducing the energy consumption, and based on the real-time position distribution characteristics of the escapee, using the clustering idea to dynamically generate a set of hunting target points, realizing the adaptive reconstruction of the hunting formation.

[0054] As shown in Figure 1 , a robot cluster formation method based on target allocation sequentially completes a series of preset target formations, and for each target formation, the following steps are adopted:

[0055] S1: According to the target formation of the cluster formation, select a suitable initial target allocation scheme for the robot cluster, so that each robot is allocated an initial target index.

[0056] Specifically, if the target formation is a configuration composed of multiple simple polygons on a two-dimensional plane, and the line connecting the centers of each polygon is parallel to the X-axis or Y-axis, then an initial coarse target allocation is performed based on the X / Y coordinates of each center and the X / Y coordinates of the robots in the cluster. Robots corresponding to the number of target points are selected for each polygon, and then the final target index is determined within each polygon using a random allocation or center allocation strategy. For example... Figure 2 As shown, the pentagram formation composed of circles represents the target formation, with each circle representing a target point. The triangular array represents the robot cluster, with each triangle representing a robot. The center allocation strategy is implemented as follows: the position center of the robot cluster is calculated, and then the nearest idle robot is selected for pairing with each target point in order from farthest to nearest, in order to minimize the variance of the time it takes for each robot within the cluster to reach its target point and accelerate the cluster formation speed.

[0057] This invention addresses the shape characteristics of target formations by employing a differentiated initial target allocation mechanism. By minimizing the initial distance between the robot and the target point, it effectively avoids generating robot-target point combinations with excessively large distances, thus establishing an optimized foundation for subsequent rapid formation.

[0058] S2: Dynamic motion control of the robot based on the differential speed model.

[0059] In this step, a two-dimensional differential speed model is used as the simulation model to update the position and attitude of the robots in the cluster at the current time step. Specifically, the guiding component and collision avoidance component of each robot's velocity are calculated, synthesized, and weighted with the velocity at the previous moment to obtain a new resultant velocity. The left and right wheel velocities are calculated and limited based on the current orientation angle and the constructed differential speed inverse kinematics model. Finally, the linear velocity and angular velocity are calculated using the forward kinematics model to update the cluster's position and orientation angle, thus updating the robot position and attitude. The derivation of the forward and inverse kinematics models of the differential speed model used is as follows:

[0060] Positive kinematics:

[0061]

[0062]

[0063] Inverse kinematics:

[0064]

[0065]

[0066] in, , These are the wheel speeds of the robot's left and right wheels, respectively. , linear and angular velocity of the robot respectively, wheel base of the robot, vertical distance from the robot center to the line connecting the left and right wheels, thus it can be seen that this model is universal for any differential model, and only the values of and need to be modified in the algorithm to complete the algorithm migration.

[0067] S3: Start global target re-allocation for the lagging robot.

[0068] Check whether the cluster meets the global adjustment target allocation judgment condition, if it does, reconfigure the target allocation; set a distance threshold to determine whether the robot has reached the target point, if the distance between the two is less than the threshold, it is determined to have arrived, as in formula (5) of the application, the threshold is set to 0.01.

[0069]

[0070] When the target formation is a simple polygon and the proportion of robots that have reached the target point exceeds the threshold (i.e. most robots), in order to avoid a small number of robots still being too far from their target points and causing the formation to be slow, the target of the robot needs to be changed to the nearest target point A, and then according to the characteristic that the distance between adjacent target points of a simple polygon is equal, search clockwise / counterclockwise until the nearest unoccupied target point B is found, and then re-allocate the target points of the robots on the path from A to B in sequence to achieve the effect of moving the whole formation, thereby greatly reducing the time of formation.

[0071] In this embodiment, the closed polygon with adjacent target points equally distributed is defined as a simple polygon, and represents the proportion of robots that have reached the target position exceeding the set threshold (preferably the threshold is 0.9, which can be adjusted as needed). When the global adjustment trigger condition is met, global target re-allocation is performed, and the robot that is too far from the target point is allocated the spatially nearest neighbor target point, and then the target is updated in sequence according to the direction found. In addition, the robot index involved in global target updating needs to be set with a target lock mechanism to prevent it from being selected as a target again in the subsequent target exchange process because it is too close to the currently occupied target point. The locked target point cannot be modified by the exchange rule, and the lock is released only when the robot reaches the new target point, and the target exchange permission is reopened.

[0072] S4: Realize global information sharing in the neighborhood through robot neighborhood communication and gradient algorithm, mark the unassigned target point as the candidate target point, so that it is selected as the target as soon as possible through global information sharing, and comb the gradient of the robot in the neighborhood, and update the gradient value of the robot to dynamically assign the candidate target point. The design of the global information sharing mechanism in the neighborhood is to solve the multi-robot target conflict problem. When multiple robots compete for the same target point, the robot closer to the target point has higher priority and obtains the assignment right; if the priority of the robot is low, the candidate target point is empty, and a new target is randomly selected for it, otherwise, the candidate target point is selected as the new target.

[0073] In this step, considering that under the initial assignment, there may be a situation that multiple robots are assigned to the same target point, resulting in target conflict, and there may also be an "unassigned target point". In a neighborhood, when a robot A finds an "unassigned target point", its gradient grad A is updated to 0, and it is set as its own candidate target point. If there is another robot B in the neighborhood that does not find an "unassigned target point" in its neighborhood, it will search all neighbors in the neighborhood, find the neighbor with the lowest gradient (assuming it is A), and update its candidate target point to A's candidate target point, and update the gradient to grad A +1; the gradient algorithm is to let each robot occupy the existing unassigned target point resource as soon as possible through information sharing.

[0074] S5: Design target exchange rules based on neighborhood communication, minimize the total time and total path distance of the cluster formation through iterative exchange operation.

[0075] In one specific implementation of the present application, Figure 3 is the complete running process of a single robot in a cycle. The motion process of the robot needs to perform motion control to update the position and attitude of the robot, and judge whether the condition of triggering global adjustment target is met. If the condition is met, find the nearest unoccupied target point, adjust the target in order or reverse order, and set the target lock for the robot participating in global update target; if the condition is not met, directly enter the next step. Next, check whether the target is locked. If it is not locked, update the gradient and perform the target exchange algorithm; otherwise, wait for the target to be unlocked.

[0076] As shown in Figure 4 , a cooperative hunting method based on the above cluster formation method, comprising the following steps:

[0077] S1: Initial assignment of hunting grouping.

[0078] After initializing the positions of the hunters and the evaders, the initial allocation of the hunting groups is performed according to the principle of proximity. For the evaders allocated with a number of hunters greater than the expected number, the nearest expected number of targets are selected, and if the number is insufficient, the nearest hunters not allocated with hunting targets are supplemented.

[0079] As shown in Figure 5 , in the initial allocation of the hunting groups, the distance between each hunter and each evader is first calculated to obtain a hunter-evader distance matrix; each hunter is allocated with the nearest evader, and then only the nearest num_group hunters of each evader are retained, where num_group is usually the integer part of the quotient of the number of hunters divided by the number of evaders. At this time, there may be some evaders allocated with a number of hunters greater than num_group, and there may also be evaders allocated with a number of hunters less than num_group. In this case, the excess hunters are first de-allocated, and then the nearest hunters not allocated with hunting targets are selected as a supplement for the evaders allocated with a number of hunters less than num_group, until each evader is allocated with a number of hunters meeting the requirements. Under this allocation, each evader is allocated with a number of hunters balanced, and the correspondence between the hunters and the evaders does not change during the entire hunting process.

[0080] S2: Hunting condition determination.

[0081] It is determined whether the hunting condition is met, and if yes, the hunting is stopped or a new hunting formation is established; otherwise, it is determined whether the target position needs to be updated.

[0082] For a traditional multi-target hunting task, each evader is finally surrounded by different hunters to complete the hunting in a hunting circle, and the criterion for determining whether the hunting condition is met is as follows: define the evader set , , where represents any one evader, and let be the set of hunter robots surrounding the evader . , represents the maximum distance between the evader and all its hunters. For , the maximum distance between the evader and its hunters can be expressed as: When the hunting is completed: for , it needs to satisfy , , which is the set success hunting distance threshold. In addition, the hunter robots are required to be uniformly distributed on the hunting circle centered on the evader, and in the ideal state, they form an inscribed regular polygon to form a closed surrounding circle domain. In this embodiment, for the evader clustering situation, the invention sets the center of the hunting circle as the average position of the evader cluster, and sets the hunting radius as the maximum value of the distance from the cluster center to each evader inside the cluster and The sum of these values ​​indicates that the target location for the encirclement is set as the vertex of the inscribed polygon on the encirclement circle formed by the cluster center and radius.

[0083] The criteria for determining whether the target location needs to be updated for the encirclement are as follows:

[0084] The target position remained stable for an extended period of time.

[0085] S3: Dynamic clustering generates target points.

[0086] Based on the distance matrix of the escapee's location, a clustering algorithm is used to group the escapees, and each group corresponds to a capture circle. The capture circle is described by its capture radius and center. To prevent frequent changes in the target shape of the capture circle due to the escapee's slight movements, this invention records the capture radius and center of any cluster at the previous moment. If the recalculated center offset and radius change are both less than a set threshold, the historical capture circle parameters are used. To avoid overlapping capture circles generated by the clustering algorithm, after clustering is completed, this invention iteratively adjusts the position of the new cluster centers globally. During the iteration process, if an overlap is detected in a newly added cluster center, the new cluster center needs to be translated along the vector direction connecting the centers of conflicting clusters until all cluster circles are non-overlapping. Finally, based on the cluster centers and cluster radii of each group, a set of target points without conflict interference is generated.

[0087] like Figure 6 As shown, when calculating the escapee cluster and the target point set, the distance matrix between escapees is first calculated and the clustering is completed. The center and radius of each cluster are calculated, and it is determined whether the current cluster exists. If it does not exist, the cluster is updated. If it exists, it is further determined whether the changes in the cluster center and radius are both less than the threshold. If so, the cluster center and radius obtained in the previous round of clustering are used. Otherwise, the new cluster center and radius are used.

[0088] Based on the latest clustering, the positions of the cluster centers are iteratively adjusted globally to ensure that all cluster circles do not overlap. Then, the initial allocation of subsequent target points is performed based on the adjusted cluster centers and radii.

[0089] S4: Target assignment within clusters.

[0090] Within each cluster, target assignment from robot to target point is performed. For example... Figure 7 As shown, it is determined whether the current cluster exists in the historical cluster group. If so, the historical target point allocation scheme of the cluster is reused. Otherwise, the distance matrix between the hunter and the target point is calculated and the nearest hunting point is initially assigned to each hunter. For target points with conflict, the hunter farthest from the target point is selected and reserved for allocation, and the rest are released. The released hunters reselect the nearest target point from the remaining target points. The iterative operation continues until the target point allocation of the cluster is completed.

[0091] S5: The robot is updated with the same motion control logic as the cluster formation algorithm.

[0092] Figure 8 to Figure 10 In the figure, the robots are displayed in blue when they have not reached the specified target point position, and in green when they have reached the specified target point position. In the figure, Figure 8 The process of forming a "ZJU" shape by the robot cluster: the robots are initially distributed in the lower left corner, the initial target assignment is performed by using the center assignment method, and finally the target shape "ZJU" is formed through the processes of motion control, target exchange, etc. Figure 9 The process of switching the shape of the robot cluster to "ROBOT": after the formation of the "ZJU" shape, the robot cluster uses the principle of "nearest pairing according to the horizontal / vertical coordinates" to complete grouping, and then performs random target assignment within the group. Through the methods of motion control, target exchange, etc., the shape is successfully switched to "ROBOT". Figure 10 The process of forming a "Hawk" simple polygon by the robot cluster through the processes of "initial target assignment by using the center assignment algorithm", motion control, global target adjustment, target exchange, etc.

[0093] Figure 11 to Figure 14 In the figure, the pursuers are initially displayed in blue, and the escapees are initially displayed in red. When the pursuers reach the specified position of the surrounding circle, they are displayed in green. When the escapees are completely captured into the surrounding circle, they are displayed in purple. In the figure, Figure 11 and Figure 12 The figures respectively show the process of clustering merging and decomposition caused by the position change of the escapees during the pursuit process, wherein the solid circles and the hollow circles respectively represent the pursuers and the escapees. Figure 13 The process of completing the cooperative pursuit task: the escapees and the pursuers are randomly and scattered distributed at the initial moment, the pursuit grouping is determined according to the distance matrix of the escapees / pursuers at the initial moment, the clustering algorithm is used to cluster and group the escapees and generate the target points of the surrounding circles, and the pursuers of the corresponding pursuit group use the above-mentioned cluster formation algorithm to form each target shape (all the surrounding circles), so as to realize the effect of cooperative pursuit. Figure 14 In the figure, part of the escapees change the pursuit clustering due to their own motion, and the pursuers form a new pursuit shape through dynamic adjustment, successfully complete the task of re-pursuing all the escapees, and reflect the flexibility of the adaptive pursuit.

[0094] In the embodiment, a robot cooperative pursuit system for implementing the above-mentioned pursuit method is also provided, which comprises:

[0095] A target assignment module is configured to implement initial target assignment according to the target shape and select an assignment strategy.

[0096] a dynamic motion control module for dynamically controlling the robots based on a differential model until a target formation is achieved;

[0097] a global optimization module for optimizing the control logic in the dynamic motion control module using a global target reassignment mechanism;

[0098] a cooperative hunting module for assigning a balanced number of hunters to each escapee and dynamically clustering the escapees based on a distance matrix of the escapee positions, calculating the center and radius of each cluster, adjusting the cluster center positions to avoid overlapping hunting circles through global iterative optimization, and generating a number of target points equal to the number of hunters on the circumference of the hunting circle;

[0099] a communication network module for optimizing the control logic in the dynamic motion control module using a global information sharing mechanism and a target exchange mechanism within a neighborhood;

[0100] a visual monitoring interface module for displaying the formation and hunting process in real time.

[0101] For the system embodiment, since it basically corresponds to the method embodiment, the relevant parts are described in the method embodiment, and the implementation method of the module is not described here. The system embodiment described above is only illustrative, and the units described as separate components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e., they can be located in one place or distributed on multiple network units. Some or all modules can be selected to achieve the purpose of the present application according to actual needs. Those skilled in the art can understand and implement it without creative labor.

[0102] The system embodiment of the present application can be applied to any device with data processing capability, which can be a device or apparatus such as a computer. The system embodiment can be implemented by software, hardware, or a combination of software and hardware. Taking software implementation as an example, as a logical device, it is formed by reading the corresponding computer program instructions in the non-volatile memory into the memory and running it through the processor of the device with data processing capability.

[0103] In addition, it should be noted that the robot cluster formation method based on target assignment in the above embodiment can be essentially executed by a computer program. Therefore, based on the same inventive concept, another preferred embodiment of the present application also provides a computer electronic device corresponding to the method provided in the above embodiment, which includes a memory and a processor;

[0104] The memory is used to store a computer program;

[0105] The processor is configured to implement the target distribution based robot cluster formation method in the above embodiments when executing the computer program.

[0106] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of a software functional unit and sold or used as an independent product, which can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or part of the technical solutions can be embodied in the form of a software product, which is stored in a storage medium.

[0107] Therefore, based on the same inventive concept, another preferred embodiment of the present application also provides a computer readable storage medium corresponding to the method provided by the above-mentioned embodiments, and the storage medium stores a computer program. When the computer program is executed by a processor, the target distribution based robot cluster formation method in the above-mentioned embodiments can be implemented.

[0108] It can be understood that the computer readable storage medium can be an internal storage unit of any data processing capable device as described in any of the above embodiments, such as a hard disk or a memory. The computer readable storage medium can also be an external storage device of any data processing capable device, such as a plug-in hard disk, a smart media card (SMC), an SD card, a flash card, etc. Further, the computer readable storage medium can include both the internal storage unit and the external storage device of any data processing capable device. The computer readable storage medium is used to store the computer program and other programs and data required by the data processing capable device, and can also be used to temporarily store data that has been output or will be output.

[0109] The above-mentioned embodiments are only preferred solutions of the present application, and are not intended to limit the present application. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present application. Therefore, any technical solution obtained by equivalent replacement or equivalent transformation falls within the scope of the present application.

Claims

1. A method for cooperative hunting of robots based on a robot swarm formation method, characterized in that, The method comprises the following steps: (1) Based on the distance matrix of the trapper and the escapee, the number of trappers is evenly distributed to each escapee, and the corresponding relationship between the trapper and the escapee is unchanged during the whole trapping process; (2) Dynamic target point generation: based on the distance matrix of the escapee position, dynamic clustering grouping is carried out, the center and radius of each cluster are calculated, the cluster center position is adjusted through global iterative optimization to avoid the overlap of trapping circles, and target points equal to the number of trappers are generated in the circumferential direction of the trapping circle; (3) Initial allocation of target points: if the cluster exists in the historical grouping, the historical target point allocation scheme is reused, otherwise, the initial target point is allocated to the trapper in the cluster according to the distance priority principle, and when the target points conflict, the target point is allocated to the trapper farthest from the target point, and the remaining trapper selects the nearest target point from the remaining target points; (4) Motion control: the robot cluster formation method is adopted, the global target re-allocation mechanism, the global information sharing mechanism in the neighborhood and the target exchange mechanism are used to control the trapper corresponding to each trapping circle to move towards the target point direction to form the target formation of the trapping circle; The robot cluster formation method comprises: An initial target index is allocated to each robot according to the target formation of the cluster formation, and the robot is dynamically controlled based on the differential model until the target formation is achieved; In the process of achieving the target formation, the global target re-allocation mechanism, the global information sharing mechanism in the neighborhood and the target exchange mechanism are introduced; The global target re-allocation mechanism refers to when the target formation is a closed polygon with equidistantly distributed adjacent target points, and the proportion of robots that have reached the target point exceeds a first threshold, the robots that have not reached the target point and are more than a second threshold away are recorded as lagging robots, the target point is selected for the lagging robots, the selected target point is taken as a starting point, the nearest unoccupied target point to the starting point is taken as an ending point, the target points on the path from the starting point to the ending point are sequentially re-allocated and locked, the target re-allocation is realized to accelerate the convergence of the formation; The global information sharing mechanism in the neighborhood refers to sharing the global information in the neighborhood through the neighborhood communication of the robot, marking the unallocated target points found by the robot as candidate target points, and updating the gradient value of the robot to dynamically allocate the candidate target points; The target exchange mechanism refers to iteratively optimizing the target points of each robot in the neighborhood to minimize the total time and total path distance of the formation; (5) Real-time judgment of the trapping state, if the trapping is completed, the process is ended, otherwise, step (2) is returned at each updated target position time until the trapping is completed.

2. The robot cooperative hunting method based on the robot swarm formation method according to claim 1, characterized in that, The process of adjusting the cluster center position through global iterative optimization comprises: The center and radius of the new cluster and the existing cluster are detected, it is judged whether the trapping circle of the new cluster overlaps with the trapping circle of the existing cluster, if yes, the new cluster center is translated along the vector direction of the center connecting line of the conflict cluster until the trapping circles of all clusters are not overlapped.

3. The robot cooperative hunting method based on the robot swarm formation method according to claim 1, characterized in that, In the process of dynamic clustering grouping, the following steps are further included: If the current cluster is contained in the cluster group of the previous time, and the change of the cluster center and radius are both less than the threshold, the cluster center and radius of the cluster in the last cluster group are used, otherwise the current calculated cluster center and radius are used.

4. The robot cooperative hunting method based on the robot swarm formation method according to claim 1, characterized in that, In the robot swarm formation method, an initial target index is assigned to each robot according to a target formation of the swarm formation, including: When the target formation is a polygon combination with the center line parallel to the coordinate axis, the robots are clustered and grouped according to the coordinate positions of the robots, and then a center distribution strategy or a random distribution strategy is performed in the group; The rest of the target formation adopts the center distribution strategy; The center distribution strategy specifically includes: calculating the center position of the robot swarm, and sequentially assigning each target point to the nearest idle robot according to the distance between the target point and the center position of the swarm from far to near.

5. The robot cooperative hunting method based on the robot swarm formation method according to claim 1, characterized in that, In the robot swarm formation method, dynamic motion control is performed on the robots based on a differential model until the target formation is achieved, including: The guide speed component and the collision avoidance speed component of the robot are calculated and combined, and then the combined speed is calculated by weighting the speed at the previous time; The combined speed is converted into left and right wheel speeds, the amplitude constraint of the wheel speed is introduced, the linear speed and angular speed of the robot are solved through the forward kinematics model, and the position and attitude of the robot are updated.

6. The robot cooperative hunting method based on the robot swarm formation method according to claim 1, characterized in that, In the global target redistribution mechanism, the robot is unlocked when it reaches the redistributed target point, and the target point in the locked state does not participate in the target exchange mechanism.

7. A robot cooperative hunting system for implementing the robot cooperative hunting method based on the robot swarm formation method of claim 1, characterized in that the system Including: A target distribution module for initial distribution of target points according to the target formation and selection of a distribution strategy; A dynamic motion control module for dynamic motion control of the robots based on a differential model until the target formation is achieved; A global optimization module for optimizing the control logic in the dynamic motion control module by using a global target redistribution mechanism; A cooperative hunting module for assigning a number of hunters to each escapee and performing dynamic clustering grouping based on the distance matrix of the escapee positions, calculating the center and radius of each cluster, adjusting the cluster center position through global iterative optimization to avoid overlapping of the hunting circles, and generating target points on the circumference of the hunting circle equal to the number of hunters; A communication network module for optimizing the control logic in the dynamic motion control module by using a global information sharing mechanism and a target exchange mechanism within the neighborhood; A visual monitoring interface module for real-time display of the formation and hunting process.

8. A computer-readable storage medium, characterized in that, The storage medium has a computer program stored thereon, and when the computer program is executed by a processor, a robot cooperative hunting method based on the robot swarm formation method of any one of claims 1 to 6 is realized.

9. A computer electronic device, comprising: Including a memory and a processor; The memory is used to store a computer program; The processor is used to realize the robot cooperative hunting method based on the robot swarm formation method of any one of claims 1 to 6 when the computer program is executed.

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