Random walking robot group control method, system, equipment and medium

Abstract

The method is mainly applied to the technical field of intelligent robots. The invention discloses a robot group control method, system and device capable of walking randomly and a medium, and the method comprises the steps that a robot group is divided into a plurality of robot groups, and each robot group comprises a plurality of robots; determining a virtual leader in each robot group; aiming at each robot group, each robot moves by taking the virtual leader as a target; each robot group performs search action in a random walk mode; and when the robot group finds the target object in the search action, each robot in the robot group moves by taking the target object as a target so as to execute a hunting task aiming at the target object. According to the method and the device, the task of exploring and hunting multiple targets can be executed, and the task execution efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent robots, and in particular to a method, system, equipment and medium for controlling a group of random walking robots. Background Art

[0002] Multi-Robot Systems (MRS) consist of multiple autonomous robots that work together to complete tasks, demonstrating collective intelligence, scalability, and robustness that a single robot cannot achieve. They are widely used in exploration, rescue, target capture, and other application scenarios. However, when a multi-robot system is used to control a group of robots to capture multiple targets, there are problems such as low search efficiency and long search time. Therefore, there is still a need to further improve the ability of robot groups to collaboratively solve problems in complex environments. Summary of the invention

[0003] The present invention provides a random walking robot group control method, system, equipment and medium, which can perform tasks of exploring and encircling multiple targets while improving the efficiency of executing the tasks.

[0004] The present invention provides a random walking robot group control method, the method comprising: Dividing the robot group into a plurality of robot groups, each of the robot groups comprising a plurality of robots; determining a virtual leader within each of said robot groups; For each of the robot groups, each of the robots moves with the virtual leader as a target; Each of the robot groups adopts a random walk method to perform search actions; When the robot team finds a target object during a search operation, each robot in the robot team moves with the target object as a target to perform a capture mission for the target object.

[0005] Furthermore, for each robot group, each robot moves with the virtual leader as a target, including: Each of the robots moves in the direction of a preset synthetic potential field, wherein the synthetic potential field includes a first potential field and a second potential field; When the robot detects the virtual leader, generating gravitational force according to the first potential field, so that the robot moves closer to the virtual leader under the action of the gravitational force; When the robot detects a target object, a repulsive force is generated according to the second potential field, so that the robot moves away from the target object under the action of the repulsive force, wherein the target object is the virtual leader, a robot or an obstacle.

[0006] Furthermore, the random walking robot group control method also includes: When the robot group performs a search operation in the current area, the density of the robot groups in the current area is determined based on the time interval between interference events among the multiple robot groups, wherein when two robot groups performing a search operation interfere with each other, it is recorded as an interference event, and the time interval is the length of time between the occurrence time points of the two interference events.

[0007] Furthermore, the random walking robot group control method also includes: determining a total average time interval based on a plurality of said time intervals; If the current time interval is greater than the total average time interval, reducing the step length of each robot in the robot group when moving, so as to narrow the search range of the robot group; If the current time interval is smaller than the total average time interval, the step length of each robot in the robot group when moving is increased to expand the search range of the robot group.

[0008] Furthermore, when the robot group finds a target object during the search operation, each robot in the robot group moves with the target object as a target to perform a capture mission for the target object, including: Acquire local position information of a robot group that discovers a target object, and use each robot in the robot group that discovers the target object as a capture robot to perform a capture task for the target object; Based on the local position information, a concentration field corresponding to the area where the target object is located is generated, wherein each position in the concentration field has a corresponding concentration value; Based on a preset safety distance and position information of a target object located in the concentration field, generating concentration value equipotential lines around the target object; Controlling each of the capturing robots to move closer to the target object, and obtaining a concentration value corresponding to the position of each of the capturing robots; When the concentration value corresponding to the position of the capture robot is equal to the concentration value of the concentration value equipotential line, the capture robot is controlled to stop moving.

[0009] Furthermore, the generating of a concentration field corresponding to the area where the target object is located based on the local position information, wherein each position in the concentration field has a corresponding concentration value, includes: Based on the local position information, dividing the area where the target object is located into a plurality of grids; Marking the grid corresponding to the position of the target object as a first grid, and marking the grid corresponding to the position of the obstacle as a second grid; Based on the position information and the first distance of the first grid, determining the first concentration value of each of the grids and generating a first concentration field, wherein the first distance is the distance between the grids; Based on the position information and the second distance of the second grid, determining the second concentration value of each of the grids and generating a second concentration field, wherein the second distance is the distance between the grids and the second grids; The first concentration field and the second concentration field are fused to obtain a fused third concentration field, and a third concentration value of each of the grids in the third concentration field is determined.

[0010] Furthermore, the random walking robot group control method comprises: When the capturing robot moves toward the target object, obtaining coordinate information of the capturing robot; Based on the coordinate information of the encirclement and capture robot, determine a first offset of the encirclement and capture robot, where the first offset is a coordinate offset between the encirclement and capture robot and another encirclement and capture robot; Based on the coordinate information of the target object and the coordinate information of the capture robot, determine a second offset of the capture robot, where the second offset is a coordinate offset between the capture robot and the target object; Determining a third offset of the encirclement robot based on the first offset and the second offset; Based on the third offset, the capture robot is controlled to move.

[0011] The present invention provides a random walking robot group control system, the system comprising a control device and a robot group consisting of a plurality of robots; A grouping module, used for dividing the robot group into a plurality of robot groups, each of which includes a plurality of robots; A selection module for determining a virtual leader in each of said robot groups; A first control module is used for each robot in each robot group to move with the virtual leader as a target; A second control module is used for each of the robot groups to perform a search action in a random walk manner; A third control module is used for, when the robot group finds a target object during the search operation, each robot in the robot group moves with the target object as a target to perform a capture mission for the target object; Each of the robots is used to obtain distance information, coordinate information, and identify target objects or obstacles.

[0012] The present invention also provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the random walk robot group control method as described in any one of the above items is implemented.

[0013] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the random walk robot group control method as described in any one of the above items is implemented.

[0014] The present invention has at least the following beneficial effects: The technical solution of this application improves the efficiency of task execution by dividing the robot group into multiple robot groups and setting a virtual leader for each robot group, while realizing the exploration and capture of multiple targets. The robots in each group move with the virtual leader as the target to maintain the coordination and consistency of the group. This structure helps to improve the search efficiency because it allows multiple groups to search in different areas at the same time, increasing the probability of finding the target. In addition, this solution adopts a random walk method for search actions. This strategy does not require prior knowledge of environmental information, is suitable for unknown environments, and can cover a larger area. Random walks make the search action flexible, can adapt to environmental changes, and reduce repeated search areas, thereby improving search efficiency. When any group finds the target, all robots in the group quickly adjust their action strategies and concentrate on capturing the target. This rapid response mechanism improves the capture efficiency. At the same time, other groups continue to perform search tasks, and the overall search efficiency will not be affected by the capture action of one group. Through this combination of distributed control and rapid response, efficient exploration and capture of multiple targets are achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings are used to provide a further understanding of the technical solution of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the technical solution of the present invention and do not constitute a limitation on the technical solution of the present invention.

[0016] Figure 1is a flowchart of the steps of the random walk robot group control method of this embodiment; Figure 2 is a flowchart of step S103 in the random walking robot group control method of this embodiment; Figure 3 is another step flow chart of the random walking robot group control method of this embodiment; Figure 4 is a flowchart of step S105 in the random walking robot group control method of this embodiment; Figure 5 is a flowchart of step S402 in the random walking robot group control method of this embodiment; Figure 6 It is an algorithm architecture diagram of a random walking robot group control method in an application scenario; Figure 7 It is a schematic diagram of the effect of a random walking robot group control method in an application scenario when performing a roundup task; Figure 8 It is a schematic diagram of the structure of a random walking robot group control system; Fig. 9 is a flowchart of the process steps when the control device and the robots work together in the random walk robot group control system of this embodiment; Fig.10 It is a structural diagram of an electronic device. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0018] Before explaining the technical solution of this application, the technical terms are explained first. For example: Multi-Robot Systems (MRS) are composed of multiple autonomous robots that work together to complete tasks, demonstrating collective intelligence, scalability, and robustness that cannot be achieved by a single robot. They are widely used in exploration, rescue, target capture and other fields.

[0019] In the relevant technical field, the exploration method closest to the technical solution of the present application is the one proposed in "Bioinspired Environment Exploration Algorithm in Swarm Based on Lévy Flight and Improved Artificial Potential Field" [1], which combines the traditional Lévy flight random walk algorithm and the improved artificial potential field algorithm. This solution uses a virtual leader to guide the swarm robots to use Lévy flight to explore the environment, and uses the improved artificial potential field (APF) method to achieve formation maintenance, obstacle avoidance and flexible response to environmental changes among the swarm robots. In this method, the movement of the virtual leader generates steps through the Lévy flight mechanism, guiding the swarm robots to search elegantly and efficiently in an unknown environment, while ensuring that the robots maintain an appropriate distance to avoid collisions. The artificial potential field law is used to achieve smooth obstacle avoidance behavior during the exploration process, allowing the robot swarm to flexibly adapt to different terrains and obstacles like a natural swarm. A significant advantage of this method is that it does not rely on complex sensors and computing equipment. The robot swarm can achieve elegant search behavior through a simple random step generation mechanism, just like natural organisms freely moving and changing formations in the environment. This method is particularly suitable for biomimetic robots that mimic the behavior of natural biological groups.

[0020] However, the researchers of this application found that in the above technical solution, when the scale of the environment expands or the number of targets increases, a single team needs to spend a lot of time, which is inefficient, and it is impossible to take further capture tasks for multiple targets. For a multi-robot system, after finding the target, it should be able to further process the target. Although the Levy flight used in the above technical solution combines short-distance search with occasional large-step movement, it is suitable for preliminary exploration of large areas, but it is prone to frequent repeated coverage and physical interference in multi-robot tasks, and the exploration efficiency is low, especially in complex and dense environments, which is not conducive to expansion to multiple teams of robots to complete tasks collaboratively.

[0021] The reason is that the random nature of Levy flight means that the step length and direction are largely unaffected by environmental factors, which means that even if the density of robots changes, its step length cannot be adjusted accordingly. Therefore, in an environment with a high density of robots, maintaining large step lengths will lead to increased physical interference and path duplication, thereby reducing the overall exploration efficiency. In a complex and frequently changing environment, the lack of this adaptive adjustment capability makes the exploration strategy inflexible and prone to inefficient repeated exploration, affecting the overall performance of the swarm robot.

[0022] In related fields, even if attempts are made to introduce multiple robot teams, there is a lack of systematic task allocation and collaboration mechanisms, and each robot or team still works independently, using independent Levy random exploration strategies. This approach does not take into account how to achieve balanced task allocation among multiple teams, resulting in repeated coverage of some areas while other areas are missed. At the same time, the lack of effective communication and collaboration makes it difficult for each team to form a unified capture action after the target is discovered, and it is impossible to fully utilize the potential advantages of the swarm robot system in a dynamic environment. Therefore, when faced with multiple targets or tasks that require complex collaboration, the existing technical solutions seem to be unable to cope with the situation.

[0023] In view of the fact that most traditional multi-robot systems focus on any independent aspect of exploring targets, clustering movements or capturing targets, and the exploration methods are either limited to a single regular motion pattern and cannot effectively deal with dynamic targets, or are limited by the limitations of traditional random walk algorithms and cannot take into account both exploration efficiency and collaborative operations, the present application proposes the following embodiments, which aim to solve the problem of efficient exploration and capture and rescue of multiple static or dynamic targets, and can perform the tasks of exploring and capturing multiple targets while improving the efficiency of executing the tasks.

[0024] Please refer to Figure 1 , Figure 1 It is a flowchart of the steps of the random walking robot group control method of this embodiment.

[0025] This embodiment provides a random walking robot group control method comprising: S101. Divide a robot group into a plurality of robot groups, each of which includes a plurality of robots.

[0026] S102. Determine a virtual leader in each robot group.

[0027] S103. For each robot group, each robot moves with the virtual leader as the target.

[0028] S104. Each robot group uses a random walk method to perform search operations.

[0029] S105. When the robot team finds the target object during the search operation, each robot in the robot team moves with the target object as the target to perform a capture mission for the target object.

[0030] In step S101 of some embodiments, the robots are divided into groups according to their positions in the environment, for example, each group is responsible for a specific area or grid.

[0031] In step S102 of some embodiments, a leader is elected through an algorithm, for example, the robot with the strongest communication capability or the best position is selected as the leader, or the leader is rotated within the group to balance energy consumption and task allocation, or the leader is determined according to preset rules, such as ID number or startup order.

[0032] In step S103 of some embodiments, a following algorithm such as a PID controller is used to enable the robot to dynamically adjust its position to maintain its relative position with the leader. In addition, behavioral rules are set, such as maintaining a certain distance, avoiding collisions, etc., to maintain the coordinated movement of the group. For example, the distance from the leader is monitored by sensors, and the speed and direction are automatically adjusted to maintain the formation.

[0033] It can be understood that this embodiment uses multiple teams to perform distributed collaborative exploration, which improves the exploration efficiency of the system in dealing with large environments and multiple targets.

[0034] Please refer to Figure 2 , Figure 2 It is a flow chart of step S103 in the random walking robot group control method of this embodiment.

[0035] In some embodiments, step S103 includes: S201. Each robot moves in the direction of a preset synthetic potential field, wherein the synthetic potential field includes a first potential field and a second potential field.

[0036] S202: When the robot detects the virtual leader, it generates gravity according to the first potential field, so that the robot moves toward the virtual leader under the action of the gravity.

[0037] S203. When the robot detects a target object, a repulsive force is generated according to the second potential field, so that the robot moves away from the target object under the action of the repulsive force, wherein the target object is the virtual leader, the robot or an obstacle.

[0038] It is understandable that there is repulsion between robots in a robot group and between robots and the virtual leader to maintain a safe distance. The robot takes the virtual leader as a target and approaches it through gravity, but at the same time, when it approaches to a certain extent, it needs to be restricted by repulsion to maintain a certain safe distance. The robots in the group also maintain a safe distance through repulsion.

[0039] In some embodiments, an artificial potential field algorithm is used to realize group robot cluster motion. In the artificial potential field method, the potential field in which the robot is located is artificially defined as a gravitational potential field (first potential field) and a repulsive potential field (second potential field). The gravitational potential field is provided by the target object, and the repulsive potential field is provided by the obstacle.

[0040] In the present invention, the artificial potential field method is used to make each group of robots move in groups. The function of this gravitational field is to attract individual robots in the group to move towards the virtual leader. The strength of the gravitational field is inversely proportional to the distance from the virtual leader, that is, the closer to the virtual leader, the smaller the gravitational force; the farther from the virtual leader, the greater the gravitational force. The mathematical expression of the gravitational field is:

[0041] in, represents the gravitational field coefficient, Indicates the positions of other robots in the group Position with the virtual leader of the group The distance between Represents the magnitude of the gravitational field.

[0042] The function of the repulsive field is to avoid collisions between members of each team while avoiding obstacles. That is, for other robots in the group, except for the virtual leader, all other robots are obstacles. The strength of the repulsive field is inversely proportional to the distance to the obstacle. That is, the closer the distance to the obstacle, the greater the repulsive force; the farther the distance to the obstacle, the smaller the repulsive force. The mathematical expression of the repulsive field is:

[0043] in, represents the repulsive field coefficient, Indicates the positions of other robots in the group Position with obstacles The distance between Indicates the maximum distance affected by obstacles. Indicates the size of the repulsive field.

[0044] The combined field is obtained by superimposing the effects of the gravitational field and the repulsive field mentioned above. The individual robots in each group move in the direction of the combined field, which can achieve a certain distance within the cluster and follow the virtual leader of the group to avoid obstacles when encountering them. In order to avoid falling into the local optimum, an additional random perturbation term is added to the combined field. The mathematical expression of the combined field is:

[0045] in, represents the random disturbance term.

[0046] It can be understood that in this embodiment, the team is composed of robots with different functions. The leader robot uses the artificial potential field method to lead the members to achieve cluster coordinated movement to maintain the consistency and flexibility of the team, and can adaptively avoid obstacles after discovering them. At the same time, it also provides a disturbance term for the artificial potential field method to avoid falling into local minimum points during the obstacle avoidance process.

[0047] In some embodiments, each robot divides the space into several areas according to its sensing range and identifies the number of robots in these areas. The density of the robots can be estimated by calculating the ratio of the number of robots in the sensing area to the total area. The advantage of this method is that the size of the sensing area can be dynamically adjusted to adapt to different environments and task requirements, thereby more accurately determining the density of the robot group.

[0048] In some embodiments, the step of determining the density of the robot group in the current area includes: When a robot team performs a search operation in the current area, the density of the robot teams in the current area is determined based on the time interval between interference events among multiple robot teams. When two robot teams performing search operations interfere with each other, it is recorded as an interference event, and the time interval is the length of time between the occurrence time points of the two interference events.

[0049] Please refer to Figure 3 , Figure 3 This is another step flow chart of the random walking robot group control method of this embodiment.

[0050] In some embodiments, the random walking robot group control method further includes: S301. Determine a total average time interval according to multiple time intervals.

[0051] S302: If the current time interval is greater than the total average time interval, the step length of each robot in the robot group when moving is reduced to narrow the search range of the robot group.

[0052] S303: If the current time interval is smaller than the total average time interval, the step length of each robot in the robot group when moving is increased to expand the search range of the robot group.

[0053] It can be understood that random walk refers to moving in random directions and with random step sizes. Random walk does not require prior knowledge of the map of the environment or the specific location of the target, which makes it very suitable for dynamic and unknown environments. The target can also move randomly instead of stationary or regular motion.

[0054] Traditional random walk algorithms mainly include Brownian motion and Levy flight. Brownian motion is suitable for local search, but not for efficient exploration of large areas. Although Levy flight combines short-distance search with occasional large-step movement, it is suitable for preliminary exploration of large areas. However, if it is expanded to multiple leaders and multiple teams for distributed movement, it is prone to frequent repeated coverage and physical interference, and the exploration efficiency is not high. Therefore, this embodiment adopts an improved random walk algorithm.

[0055] In the random walk algorithm adopted in this embodiment, each robot group can estimate the density of the robot group based on the time interval of interference with other groups, that is, estimate whether there are multiple robot groups exploring near the same local area, and adaptively adjust the step size according to the density of other robot groups in the environment. When the density of the robot group is high, the step size is reduced to search the local area; when the density is low, the step size is increased to search a wider area. This method reduces repeated searches and improves search efficiency. The mathematical expression of this improved random walk algorithm is:

[0056] in, represents the new step size, represents the last step length, represents the speed of the robot, It represents the total average time interval of physical interference between robot groups, which is used to estimate the density of robot groups. k represents the adjustment factor, which controls the influence of the previous step on the current step. Indicates the time interval between the current interference event and the previous interference event.

[0057] when When , it means that the robot group density in the local area is low, and the robot should increase the step length to cover a larger area; when When , it means that the density of robot groups in the local area is high, and the robot should reduce the step size to avoid repeated search with other groups.

[0058] It can be understood that in distributed adaptive exploration in an unknown environment, this embodiment reduces repeated searches between robot groups by adaptively adjusting the step size, thereby improving the exploration efficiency.

[0059] In some embodiments, a random walking robot group control method method further includes: When the robot group is performing a search operation in the current area, if the obstacle detected by the target robot in the robot group is a robot of the same robot group, the search operation is performed according to the preset distance between the target robot and the robots of the same robot group; if the obstacle detected by the target robot is a robot of a different robot group, the target robot is controlled to move according to a preset direction.

[0060] It is understandable that in order to further let each robot team finally converge to explore in its own independent area, when a robot of one team encounters a robot of another team that performs obstacle avoidance behavior, it turns to the opposite direction. In this way, the above-mentioned groups that perform cluster movement using the artificial potential field method can evenly distribute the teams in the environment, can more effectively explore the environment, reduce repeated searches, and improve search efficiency.

[0061] Please refer to Figure 4 , Figure 4 It is a flow chart of step S105 in the random walking robot group control method of this embodiment.

[0062] In some embodiments, step S105 includes: S401, obtaining local position information of a robot team that has discovered a target object, and using each robot in the robot team that has discovered the target object as a capturing robot to perform a capturing task for the target object.

[0063] S402: Generate a concentration field corresponding to the area where the target object is located based on the local position information, wherein each position in the concentration field has a corresponding concentration value.

[0064] S403: Generate concentration value equipotential lines around the target object based on the preset safety distance and the position information of the target object located in the concentration field.

[0065] S404, controlling each of the capturing robots to move toward the target object, and obtaining a concentration value corresponding to the position of each of the capturing robots.

[0066] S405. When the concentration value corresponding to the position of the capture robot is equal to the concentration value of the concentration equipotential line, control the capture robot to stop moving.

[0067] In this embodiment, after each group finds the target, it switches the original cluster motion exploration state to the capture state through the state machine, removes the constraints of the artificial potential field method and the improved random walk method, and adopts other control strategies to complete the capture task.

[0068] In some embodiments, a gene regulatory network algorithm is used to search for a target and then capture the target. The gene regulatory network algorithm is a mechanism that simulates how genes in an organism control protein production and cell behavior. It is used in a multi-robot system. The swarm robot system uses local information to obtain the positions of targets and obstacles in the working area, thereby generating a concentration field about the target and obstacles. In this concentration field, the intelligent agent can calculate the concentration value of the current intelligent agent's position through its own distance from the target and the obstacle. This process can not only help the intelligent agent move toward the target in a decreasing concentration gradient, but also help the intelligent agent avoid obstacles. Based on the minimum safe distance position from the intelligent agent to the target, an equipotential line is selected. This equipotential line just surrounds the target (ignoring obstacles) and acts as an encirclement. During the movement of the intelligent agent toward the target, once the concentration value at the location of the intelligent agent is the same as the concentration value at the encirclement, the intelligent agent stops moving.

[0069] Please refer to Figure 5 , Figure 5 It is a flow chart of step S402 in the random walking robot group control method of this embodiment.

[0070] In some embodiments, step S402 includes: S501: Divide the area where the target object is located into multiple grids based on local position information.

[0071] S502: Mark the grid corresponding to the position of the target object as the first grid, and mark the grid corresponding to the position of the obstacle as the second grid.

[0072] S503 . Determine a first concentration value of each grid and generate a first concentration field based on the position information of the first grid and the first distance, wherein the first distance is the distance between the grids.

[0073] S504 , based on the position information of the second grid and the second distance, determine the second concentration value of each grid and generate a second concentration field, wherein the second distance is the distance between the grids.

[0074] S505 , fusing the first concentration field with the second concentration field to obtain a fused third concentration field and determining a third concentration value of each grid in the third concentration field.

[0075] In this embodiment, the local environment detected by the group robot that finds the target is first gridded, and the grids where the target and obstacles are located are marked as "1", and the remaining grids are marked as "0". Using the target position information, the original concentration field of the target is generated. The specific formula is as follows:

[0076]

[0077] Among them, the distance from the i-th grid to the target is calculated as . , are the x-coordinate and y-coordinate of the ith grid respectively. , are the x-coordinate and y-coordinate of the target respectively. Calculate the Concentration value of each grid After that, we can know that the concentration value at the target is the largest, and the farther away from the target, the smaller the concentration value of the corresponding grid.

[0078] Similarly, the original concentration field of the obstacle is generated by using the obstacle position information. The specific formula is as follows:

[0079]

[0080] Among them, the distance from the i-th grid to the obstacle is calculated as . , are the x-coordinate and y-coordinate of the ith grid respectively. , are the x-coordinate and y-coordinate of the obstacle respectively. Calculate the concentration value of the i-th grid under the influence of the obstacle .

[0081] The obtained original concentration field is further processed to merge the concentration field formed by the target position information and the concentration field formed by the obstacle position information, and to make the concentration field about the target and the concentration field about the obstacle "opposite", so as to ensure that the selected equipotential line (encirclement) just surrounds the target. The specific formula is as follows:

[0082]

[0083]

[0084]

[0085] in, Is a Sigmoid function. In robot control systems, this function is usually used to smoothly control the switching of certain behaviors. In the formula, x represents the current input value, and k and z represent the adjustment parameters.

[0086] When the original obstacle concentration field is further processed, The meaning is that at time t Concentration field formed by obstacles obtained after preliminary processing by the module , Contains the obstacle concentration values ​​corresponding to all grids, that is, the original obstacle concentration field. and k represent tuning parameters.

[0087] When processing the obtained target original concentration field and obstacle original concentration field, The meaning is that at time t The concentration field formed by the target and obstacles obtained after the module's preliminary processing , Contains the target concentration values ​​corresponding to all grids, that is, the target original concentration field. Contains the obstacle concentration values ​​corresponding to all grids, that is, the original obstacle concentration field. and k represent tuning parameters.

[0088] Will get and The final concentration field is obtained by fusion, and the equipotential line information of the encirclement mode used to surround the target is extracted (that is, the concentration value corresponding to the equipotential line, which is determined by the safe distance that the robot needs to maintain with the target). The meaning is that at time t The concentration field formed by the target and obstacles obtained after the module's preliminary processing , and k represent tuning parameters.

[0089] In some embodiments, a random walking robot group control method further includes: When the capturing robot moves toward the target object, the coordinate information of the capturing robot is obtained; based on the coordinate information of the capturing robot, a first offset of the capturing robot is determined, and the first offset is the coordinate offset between the capturing robot and another capturing robot; based on the coordinate information of the target object and the coordinate information of the capturing robot, a second offset of the capturing robot is determined, and the second offset is the coordinate offset between the capturing robot and the target object; based on the first offset and the second offset, a third offset of the capturing robot is determined; based on the third offset, the movement of the capturing robot is controlled.

[0090] It is understandable that the coordinate system of the robot is set as a two-dimensional x-axis and y-axis coordinate system, and a three-dimensional coordinate system may also be set. In this embodiment, only a two-dimensional coordinate system is used as an example for description.

[0091] Specifically, when calculating the first offset, it is calculated by the following formula:

[0092]

[0093] in, , are the x-coordinate and y-coordinate of the i-th robot in the team, , are the x-coordinate and y-coordinate of the j-th robot in the team respectively. , are the first offsets of the ith robot in the x and y axes under the influence of the jth robot, respectively.

[0094] Specifically, when calculating the second offset, it is calculated by the following formula:

[0095]

[0096] in, , are the x and y coordinates of the target respectively. , are the second offsets of the i-th robot in the x and y axes under the influence of the target, respectively.

[0097] Combining the influence of the two effects, we can get the offset of the robot on the x and y axes. The specific formula is as follows:

[0098]

[0099] in, , They are the offsets of the robot target on the x and y axes respectively due to the combined effects of the two effects.

[0100] It can be understood that the comprehensive concentration field between the team robot, target and obstacle is constructed through the gene regulatory network algorithm GRN to complete the task of encircling the target and adapt to the complex and changing task environment.

[0101] Please refer to Figure 6 , Figure 6 It is an algorithm architecture diagram of a random walking robot group control method in an application scenario.

[0102] This embodiment aims to solve the problem of efficient exploration and capture of multiple static or dynamic targets by swarm robots in an unknown complex environment without GPS communication restrictions. Figure 6As shown in the figure, the proposed system mainly consists of three parts: by adopting the artificial potential field algorithm, the virtual leader of the robot can lead other robots to form a small formation for coordinated movement and obstacle avoidance; by adopting the improved random walk method that is better than Levy flight, multiple robot teams can explore static or dynamic targets in the environment in a distributed and efficient manner; by adopting the gene regulatory network GRN algorithm, the targets discovered during exploration are encircled and processed.

[0103] Please refer to Figure 7 , Figure 7 It is a schematic diagram of the effect of a random walking robot group control method in an application scenario when performing a roundup task.

[0104] like Figure 7 As shown in the figure, after the robot team explores the target, the robots in the team switch from the exploration state to the capture state. The virtual leader in the team and other machines with different functions led by it will use the gene regulatory network GRN algorithm to build a comprehensive concentration field between robots, targets and obstacles to capture the target objects discovered in the exploration. When performing the capture task, first, the robots in the team are trained into an encirclement to encircle the target object; then, according to the concentration value in the concentration field, each robot gradually approaches the target object to gradually reduce the encirclement. Finally, each robot stops moving at the concentration value equipotential line to complete the capture task.

[0105] Any of the above embodiments improves the efficiency of executing tasks by dividing the robot group into multiple robot groups and setting a virtual leader for each robot group, while realizing the exploration and capture of multiple targets. The robots in each group move with the virtual leader as the target, maintaining the coordination and consistency of the group. This structure helps to improve the search efficiency because it allows multiple groups to search in different areas at the same time, increasing the probability of finding the target. In addition, the search action is carried out in a random walk manner. This strategy does not require prior knowledge of environmental information, is suitable for unknown environments, and can cover a larger area. Random walk makes the search action flexible, can adapt to environmental changes, and reduces repeated search areas, thereby improving the search efficiency. When any group finds the target, all robots in the group quickly adjust their action strategies and concentrate on capturing the target. This rapid response mechanism improves the capture efficiency. At the same time, other groups continue to perform the search task, and the overall search efficiency will not be affected by the capture action of one group. Through the combination of this distributed control and rapid response, efficient exploration and capture of multiple targets are achieved.

[0106] Please refer to Figure 8 , Figure 8 It is a structural diagram of a random walk robot group control system.

[0107] This embodiment also provides a random walking robot group control system including a control device 610, and a robot group composed of a plurality of robots 620; The grouping module 611 is used to divide the robot group into multiple robot groups, each of which includes multiple robots 620.

[0108] The selection module 612 is used to determine the virtual leader in each robot group.

[0109] The first control module 613 is used for each robot group, and each robot moves with the virtual leader as the target.

[0110] The second control module 614 is used for each robot group to perform a search action in a random walk manner.

[0111] The third control module 615 is used for, when the robot team finds the target object during the search operation, each robot in the robot team moves with the target object as the target, so as to perform the capture mission for the target object.

[0112] Each robot 620 is used to obtain distance information, coordinate information, and identify a target object or obstacle.

[0113] Please refer to Fig. 9 , Fig. 9 It is a flowchart of the process steps when the control device and the robots work together in the random walk robot group control system of this embodiment.

[0114] For each robot controlled in the random walking robot group control system, first, the robot group to which each robot belongs initializes and constructs a unified local coordinate system within the group, and determines the initialization direction of the group as a whole according to the task objectives and environmental information; secondly, the movement is coordinated within the group according to the artificial potential field method; when exploring the environment according to the improved random method, the robot's built-in sensors detect and avoid collisions with other robots, and adjust the movement step size accordingly according to the density of robots in the exploration area. Then, the presence of the target is continuously monitored and detected. Once the target is found, the gene regulatory network algorithm is used to participate in the formation of the roundup mode until the task is completed and the operation stops.

[0115] It will be appreciated by those skilled in the art that all or some of the steps and devices in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or transient medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. As is well known to those skilled in the art, communication media typically embodies computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0116] It can be understood that the contents of the above method embodiments are all applicable to the present system embodiments, the functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0117] An embodiment of the present application also provides an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, any of the above random walk robot group control methods is implemented.

[0118] refer to Fig.10 , Fig.10 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes: The processor 701 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application; The memory 702 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 702 can store operating devices and other applications. When the technical solution provided in the embodiments of this specification is implemented by software or firmware, the relevant program code is stored in the memory 702, and the processor 701 calls and executes the random walk robot group control method of the embodiment of the present application; Input / output interface 703, used to implement information input and output; Communication interface 704, used to realize communication interaction between the device and other devices, which can be realized through wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.); A bus 705 that transmits information between various components of the device (e.g., the processor 701, the memory 702, the input / output interface 703, and the communication interface 704); The processor 701 , the memory 702 , the input / output interface 703 and the communication interface 704 are connected to each other in communication within the device via a bus 705 .

[0119] It can be understood that the contents of the above method embodiments are all applicable to the electronic device embodiment, the functions specifically implemented by the electronic device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0120] An embodiment of the present application also provides a computer-readable storage medium, which stores a program executable by a processor. When the program executable by the processor is executed by the processor, it is used to implement the random walking robot group control method as described in any one of the above specific embodiments.

[0121] An embodiment of the present application also discloses a computer program product, including a computer program or computer instructions, wherein the computer program or computer instructions are stored in a computer-readable storage medium, a processor of a computer device reads the computer program or computer instructions from the computer-readable storage medium, and the processor executes the computer program or computer instructions, so that the computer device executes the random walk robot group control method as described in any of the previous embodiments.

[0122] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0123] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can, for example, be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, device, product or system comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. It should be understood that in the present application, "at least one (item)" refers to one or more, and "a plurality" refers to two or more.

[0124] In the several embodiments provided in the present application, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0125] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0126] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0127] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store program codes.

[0128] Although the description of the present application has been quite detailed and specifically describes several described embodiments, it is not intended to be limited to any of these details or embodiments or any particular embodiment, but should be regarded as providing a broad possible interpretation of these claims by reference to the attached claims, taking into account the prior art, so as to effectively cover the intended scope of the present application. In addition, the above description of the present application is based on the embodiments foreseeable by the inventor, and its purpose is to provide a useful description, and those non-substantial changes to the present application that have not yet been foreseen may still represent equivalent changes to the present application.

Claims

1. A random walking robot group control method, characterized in that: The method comprises: Dividing the robot group into a plurality of robot groups, each of the robot groups comprising a plurality of robots; determining a virtual leader within each of said robot groups; For each of the robot groups, each of the robots moves with the virtual leader as a target; Each of the robot groups adopts a random walk method to perform search actions; When the robot team finds a target object during a search operation, each robot in the robot team moves with the target object as a target to perform a capture mission for the target object.

2. A random walking robot group control method according to claim 1, characterized in that: For each robot group, each robot moves with the virtual leader as the target, including: Each of the robots moves in the direction of a preset synthetic potential field, wherein the synthetic potential field includes a first potential field and a second potential field; When the robot detects the virtual leader, generating gravitational force according to the first potential field, so that the robot moves closer to the virtual leader under the action of the gravitational force; When the robot detects a target object, a repulsive force is generated according to the second potential field, so that the robot moves away from the target object under the action of the repulsive force, wherein the target object is the virtual leader, a robot or an obstacle.

3. The random walking robot group control method according to claim 1 is characterized in that: The method comprises: When the robot group performs a search operation in the current area, the density of the robot groups in the current area is determined based on the time interval between interference events among the multiple robot groups, wherein when two robot groups performing a search operation interfere with each other, it is recorded as an interference event, and the time interval is the length of time between the occurrence time points of the two interference events.

4. A random walking robot group control method according to claim 3, characterized in that: The method further comprises: determining a total average time interval based on a plurality of said time intervals; If the current time interval is greater than the total average time interval, reducing the step length of each robot in the robot group when moving, so as to narrow the search range of the robot group; If the current time interval is smaller than the total average time interval, the step length of each robot in the robot group when moving is increased to expand the search range of the robot group.

5. The method for controlling a group of random walking robots according to claim 1, characterized in that: When the robot group finds the target object during the search operation, each robot in the robot group moves with the target object as the target to perform a capture task for the target object, including: Acquire local position information of a robot group that discovers a target object, and use each robot in the robot group that discovers the target object as a capture robot to perform a capture task for the target object; Based on the local position information, a concentration field corresponding to the area where the target object is located is generated, wherein each position in the concentration field has a corresponding concentration value; Based on a preset safety distance and position information of a target object located in the concentration field, generating concentration value equipotential lines around the target object; Controlling each of the capturing robots to move closer to the target object, and obtaining a concentration value corresponding to the position of each of the capturing robots; When the concentration value corresponding to the position of the capture robot is equal to the concentration value of the concentration value equipotential line, the capture robot is controlled to stop moving.

6. A random walking robot group control method according to claim 5, characterized in that: The step of generating a concentration field corresponding to the area where the target object is located based on the local position information, wherein each position in the concentration field has a corresponding concentration value, includes: Based on the local position information, dividing the area where the target object is located into a plurality of grids; Marking the grid corresponding to the position of the target object as a first grid, and marking the grid corresponding to the position of the obstacle as a second grid; Based on the position information and the first distance of the first grid, determining the first concentration value of each of the grids and generating a first concentration field, wherein the first distance is the distance between the grids; Based on the position information and the second distance of the second grid, determining the second concentration value of each of the grids and generating a second concentration field, wherein the second distance is the distance between the grids and the second grids; The first concentration field and the second concentration field are fused to obtain a fused third concentration field, and a third concentration value of each of the grids in the third concentration field is determined.

7. The method for controlling a group of random walking robots according to claim 5, characterized in that: The method comprises: When the capturing robot moves toward the target object, obtaining coordinate information of the capturing robot; Based on the coordinate information of the encirclement and capture robot, determine a first offset of the encirclement and capture robot, where the first offset is a coordinate offset between the encirclement and capture robot and another encirclement and capture robot; Based on the coordinate information of the target object and the coordinate information of the capture robot, determine a second offset of the capture robot, where the second offset is a coordinate offset between the capture robot and the target object; Determining a third offset of the encirclement robot based on the first offset and the second offset; Based on the third offset, the capture robot is controlled to move.

8. A random walk robot group control system, characterized in that: The system includes a control device, and a robot group consisting of a plurality of robots; A grouping module, used for dividing the robot group into a plurality of robot groups, each of which includes a plurality of robots; A selection module for determining a virtual leader in each of said robot groups; A first control module is used for each robot in each robot group to move with the virtual leader as a target; A second control module is used for each of the robot groups to perform a search action in a random walk manner; A third control module is used for, when the robot group finds a target object during the search operation, each robot in the robot group moves with the target object as a target to perform a capture mission for the target object; Each of the robots is used to obtain distance information, coordinate information, and identify target objects or obstacles.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the random walk robot group control method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the random walking robot group control method according to any one of claims 1 to 7 is implemented.

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