A method, system and device for planning charging paths for multiple mobile robots

Abstract

The present invention discloses a multi-mobile robot charging path planning method, system and device, and relates to the field of robot path planning. The method comprises obtaining map data of a parking lot, obtaining map data of a parking lot, obtaining task points and starting points of multiple mobile robots; according to the map data and the positions of the task points, using an improved K-Means algorithm, clustering the task points according to the triple evaluation criteria, i.e., the distance between task points in each class, the amount of electricity required to be charged by the task points in each class, and the distance between different classes, according to the minimization optimization goal of the difference value of the charging electricity and the distance between different classes; according to the clustering results, using an improved gray wolf optimization algorithm and a joint optimization algorithm of the improved algorithm, generating a mobile robot charging path that keeps communication interconnection at all times. The present invention can keep the communication between robots fully connected.

Description

Technical Field

[0001] The present invention relates to the field of robot control, and in particular to the field of charging path planning for multiple mobile robots. Background Art

[0002] In recent years, the automobile industry has undergone changes, and the coordinated promotion of technological innovation, promotion and application, and infrastructure construction have achieved positive results in the development of the new energy automobile industry. However, in real life, the construction speed of charging piles is slower than the increase in electric vehicles, and problems such as "zombie piles", "waste piles", and low utilization of charging piles have long existed. With the rapid development of technology, mobile charging robots have begun to take up their posts, effectively alleviating the mileage anxiety of car owners and providing innovative solutions for charging new energy vehicles. With the continuous increase in the number of new energy vehicles, the demand for the construction of supporting charging facilities has been further stimulated. Therefore, battery swap stations and mobile charging robots have become a way to disperse pressure. On the one hand, compared with battery swap stations, mobile charging robots can further solve charging problems such as old communities and no fixed parking spaces. On the other hand, mobile charging robots do not require too much transformation of the power grid, and do not even require high land use costs. The use of their automatic driving characteristics can maximize the value of the site. In practical applications, a single robot cannot guarantee high efficiency and real-time performance, so multiple mobile robots are needed to collaborate to complete charging tasks.

[0003] In the charging task of multiple mobile robots, due to the limitation of road network, path planning is divided into task allocation and specific path planning, which are coupled with each other. Traditional path planning algorithms include genetic algorithms, Algorithms, fast expansion random number algorithms, etc. Among them, genetic algorithms and fast expansion random number algorithms have the problems of slow speed and low efficiency. The algorithm has difficulties in solving the task allocation problem. Current swarm intelligence optimization algorithms including particle swarm optimization, ant colony optimization, gray wolf optimization algorithm, white whale algorithm, etc. can also solve the path planning problem, and are better than traditional path planning methods. Among them, the gray wolf optimization algorithm has the advantage of high search efficiency, and the discrete gray wolf algorithm has advantages in solving the traveling salesman problem. However, under the limitation of the road network, there is a variable coupling between task allocation and specific path formation, and it is difficult to obtain the optimal solution.

[0004] In order to ensure the safe, stable and efficient completion of charging tasks, multiple mobile charging robots need to maintain wireless network connections with each other to achieve collaborative communication among multiple robots. However, the existing joint optimization algorithms for path planning and task allocation have not yet involved the maintenance of full communication connectivity; therefore, it is urgent to propose a method for task allocation and path planning for multiple mobile robots under the condition of maintaining full communication connectivity. Summary of the invention

[0005] The purpose of the present invention is to provide a method, system and device for planning charging paths for multiple mobile robots while maintaining full communication connectivity between the robots.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] A method for planning charging paths for multiple mobile robots, comprising:

[0008] Obtain the map data of the parking lot, obtain the mission points and the starting points of multiple mobile robots;

[0009] According to the map data and the location of the task points, the improved K-Means algorithm is used to cluster the task points according to the triple evaluation criteria, based on the optimization objectives of minimizing the difference in charging power and the distance between different classes; the triple evaluation criteria are the distance between the task points in each class, the power required to be charged by the task points in each class, and the distance between different classes;

[0010] According to the clustering results, the improved gray wolf optimization algorithm and improved A joint optimization algorithm of the algorithms is used to generate a charging path for the mobile robot that maintains full communication connectivity.

[0011] Optionally, the improved K-Means algorithm is used according to the map data and the location of the task points, and the task points are clustered according to the triple evaluation criteria and the minimization optimization objectives of the difference value of the charging power and the distance between different classes, specifically including:

[0012] According to the formula Determine the function value that minimizes the optimization objective;

[0013] Using the formula Determine the constraints so that the task points in the same class meet the communication range restrictions;

[0014] Using the formula Determine the constraints so that the number of task points in each class is less than or equal to the number of robots;

[0015] in, is the charging time difference, For the Class and The distance between the classes, For the Within a class Mission points and The distance between task points, is the robot communication distance, For the The number of task points in a class, is the number of robots, C is the set of classes, and min is the minimum value.

[0016] Optionally, based on the clustering results, the distribution information of the task points is initialized and iteratively optimized in the Grey Wolf algorithm, and the access order of the classes and the distribution order of the task points within each class are matched with the task point numbers of the clustering results to obtain the task point numbers and charging order of each robot.

[0017] Optionally, the improved grey wolf optimization algorithm and the improved The joint optimization algorithm of the algorithm generates a charging path for the mobile robot that maintains full communication connection, including:

[0018] Initialize task allocation based on clustering results and use improved The algorithm plans the specific path sequence between the starting point and the task point and the specific path sequence between each task point;

[0019] According to the specific path sequence, discrete time dynamic topology is used to simulate the time-varying network, and piecewise linear trajectories are used to ensure that the mobile robots can always maintain communication and interconnection;

[0020] Set the maximum number of iterations of the improved gray wolf optimization algorithm. According to the mobile robot's moving track and the maximum number of iterations, use the improved gray wolf optimization algorithm to update and optimize the mobile robot's task allocation. The algorithm generates a charging path for the mobile robot.

[0021] A multi-mobile robot charging path planning system, comprising:

[0022] A map data acquisition module is used to acquire the map data of the parking lot, the mission points and the starting points of multiple mobile robots;

[0023] A clustering module is used to cluster the task points according to the map data and the location of the task points, using an improved K-Means algorithm, and according to the triple evaluation criteria, according to the minimization optimization objectives of the difference value of the charging power and the distance between different classes; the triple evaluation criteria are the distance between the task points in each class, the power required to be charged by the task points in each class, and the distance between different classes;

[0024] The charging path planning module is used to adopt the improved gray wolf optimization algorithm and the improved A joint optimization algorithm of the algorithms is used to generate a charging path for the mobile robot that maintains full communication connectivity.

[0025] A multi-mobile robot charging path planning device comprises: at least one processor, at least one memory and computer program instructions stored in the memory. When the computer program instructions are executed by the processor, a multi-mobile robot charging path planning method is implemented.

[0026] Optionally, the memory is a computer-readable storage medium.

[0027] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0028] The present invention provides a multi-mobile robot charging path planning method, system and device, which uses the K-Means algorithm to cluster task points, and uses the improved gray wolf algorithm and the improved The joint optimization algorithm of the algorithm performs path planning for multiple mobile robots, and the specific path obtained can maintain full communication connection between the robots. The present invention enables multiple mobile robots to start from the starting point and go to various task points to complete the charging task while maintaining full communication connection, and finally return to the starting point, thereby improving the charging efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0030] Figure 1 This is a schematic flow chart of a method for planning charging paths for multiple mobile robots provided by the present invention.

[0031] Figure 2 This is a control flow chart of a multi-mobile robot charging path planning method proposed by the present invention.

[0032] Figure 3 A schematic diagram of an individual in a gray wolf population including task point allocation information according to a preferred embodiment of the present invention.

[0033] Figure 4 This is a schematic diagram of the working status of multiple mobile robots at a certain moment in the results planned by the preferred embodiment of the present invention. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] The purpose of the present invention is to provide a method, system and device for planning charging paths for multiple mobile robots while maintaining full communication connectivity between the robots.

[0036] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0037] like Figure 1 and Figure 2 As shown, the present invention provides a method for planning charging paths for multiple mobile robots, including: S101, obtaining map data of a parking lot, obtaining mission points and starting points of multiple mobile robots.

[0038] First, a topological map of the parking lot is established according to the map data of the parking lot. In the preferred embodiment of the present invention, the upper left corner of the map is set as the starting point of the multi-mobile robot, 4 robots are set, 10 mission points are set, and the charging time of each mission point is 15min, 10min, 10min, 20min, 20min, 20min, 15min, 20min, 15min, 15min. In the preferred embodiment of the present invention, the communication range of the robot is 80m.

[0039] S102, based on the map data and the location of the task points, an improved K-Means algorithm is used to cluster the task points according to the triple evaluation criteria, namely, the distance between the task points in each class, the amount of electricity required to be charged by the task points in each class, and the distance between different classes, according to the optimization goal of minimizing the difference in charging electricity and the distance between different classes.

[0040] The task points are clustered by optimizing the objective function, so that the difference in the charging power demand of the task points in the same class and the distance between different classes are reduced, and the waiting time of multiple mobile robots to maintain full communication connection is reduced.

[0041] The optimization objective function is:

[0042] (1)

[0043] The constraints are that the task points in the same class meet the communication range limit and the number of task points in each class does not exceed the number of robots, so that the task points in the same class meet the communication range limit and increase the probability of maintaining full communication connection when multiple mobile robots are driving, as shown in formula (2) and formula (3):

[0044] (2)

[0045] (3)

[0046] in, is the charging time difference, For the Class and The distance between the classes, For the Within a class Mission points and The distance between task points, is the robot communication distance, For the The number of task points in a class, is the number of robots, C is the set of classes, and min is the minimum value.

[0047] The selection of k value is determined according to the adaptive method. First, the ratio of the number of task points to the number of robots is selected and rounded up as the k value. If the clustering is successful, that is, the task points in the same class meet the communication range limit, then the k value is determined. If the clustering is unsuccessful, that is, the task points in the same class exceed the communication range limit, then the k value is increased by 1 and clustering is performed again.

[0048] The clustering steps are as follows: randomly select k task points as the initial center points of k classes; calculate the evaluation values ​​between the remaining task points and the k center points in turn, and divide the remaining task points into the class where the center point with the lowest evaluation value is located according to the minimum principle; calculate the average value of the coordinates of all task points in each class, and use them as the coordinates of the new center points of the k classes; repeat the first two steps until the maximum number of iterations is met.

[0049] In the preferred embodiment of the present invention, the k value is determined to be 3.

[0050] S103, based on the clustering results, the improved gray wolf optimization algorithm and the improved A joint optimization algorithm of algorithms to generate charging paths for mobile robots.

[0051] S301, using improved gray wolf optimization algorithm and improved The joint optimization algorithm of the algorithm sets the maximum number of iterations, initializes the task point allocation information according to the clustering results, and uses the improved The algorithm generates a specific path that can maintain full communication connectivity, calculates the fitness value of the gray wolf population, that is, the time of the charging task, and selects the individual with the smallest fitness value as the leader of the gray wolf population.

[0052] The task point allocation information initialized according to the clustering results includes two parts, namely the access order of the randomly generated classes and the task point allocation order within each class. Corresponding to the task point serial number of the clustering result, the task point allocation information of each robot can be obtained, including the task point serial number and the working order.

[0053] like Figure 3 This is the task point allocation information of an individual in the preferred embodiment of the present invention. The first three digits from 1 to 3 represent the access order of the three classes, that is, the charging task of the task point in the third class is performed first, then the charging task of the task point in the first class is performed, and finally the charging task of the task point in the second class is performed; the part after the individual is the allocation information of the task points within the three classes. Taking the part from the fourth to the seventh digit of the individual as an example, this part represents the task point allocation information within the first class, that is, the third task point is allocated to the first robot, the fourth task point is allocated to the second robot, the first task point is allocated to the third robot, and the second task point is allocated to the fourth robot.

[0054] improve The algorithm adds the direction probability judgment of each path point. The probability of selecting the path point closest to the next task point is higher, and the probability of selecting other path points is lower. This makes the generated path more variable and easier to generate a path that can maintain full communication connection. The algorithm plans the specific path sequence between the starting point and the task point and the specific path sequence between each task point. Several paths are connected to form a charging path for a robot. For the judgment of full communication connection, the present invention introduces a discrete-time dynamic topology to simulate a time-varying network, and approximates the continuous robot movement trajectory through a piecewise linear trajectory. The completion time of the charging task is the maximum value of the sum of the travel time of multiple mobile robots and the charging time of the task point. Divide into Equal-length time slots , , , in any time slot, the position of each node can be considered to be approximately unchanged, and the average channel gain is maintained approximately constant. To simplify the expression, the time slot set is defined as According to this time slot division method, the robot Time slot The coordinate position can be expressed as , , judge whether the multi-mobile robots can be connected to each other in each time slot, that is, each robot is within the communication range of other robots. If a robot in a certain time slot cannot maintain full connection at the position of the next time slot, a waiting strategy is adopted. The robot that exceeds the communication range of the multi-mobile robot cluster waits in place, adds a time slot, and copies its current position coordinates until the multi-mobile robot communication can be fully connected in the next time slot. If the number of time slots exceeds the preset value, this time through the improvement If the path generated by the algorithm does not meet the conditions for maintaining full communication connectivity, the path is regenerated.

[0055] S302, according to the hunting and predation behavior of the gray wolf population, the position of the gray wolf population is updated, that is, the allocation information of the task points is updated, the parameters of the hunting and predation behavior are updated, the number of iterations is increased by 1, a specific path is generated according to the new task point allocation information, and the fitness value of the gray wolf population is calculated, and the leader wolf is updated;

[0056] The position of the gray wolf population is updated, that is, the distribution information of the task points is updated, including decoding the gray wolf population after the updated position. Each individual in the gray wolf population carries two kinds of information, namely, the class access order and the task allocation within each class. Through these two kinds of information, the task point number and charging order of each robot can be obtained.

[0057] The fitness value of the gray wolf population in the present invention is the time consumption of the charging task. The above steps obtain the specific path of the multi-mobile robot, that is, its coordinates in each time slot, and the time consumption of the charging task. is the number of time slots The time of each time slot The product of .

[0058] Calculate the fitness value of the gray wolf population, sort them, and select the three individuals with the smallest fitness value as the alpha wolf , , , record the wolf Contains task assignment and specific path information.

[0059] According to formulas (4) and (5), the hunting behavior of the gray wolf population is implemented:

[0060] (4)

[0061] (5)

[0062] in, represents the distance vector between the individual and the prey, Indicates the current number of iterations. and Represent the position vector of the individual gray wolf, represents the position vector of the prey, A and C are coefficient vectors, calculated according to formula (6), formula (7), and formula (8):

[0063] (6)

[0064] (7)

[0065] (8)

[0066] Among them, the attenuation factor According to formula (8), as the number of iterations increases, it decreases from 2 to 0. represents the maximum number of iterations, and Pick A random vector between .

[0067] The predation behavior of the gray wolf population is implemented according to the following formula:

[0068] (9)

[0069] (10)

[0070] (11)

[0071] (12)

[0072] (13)

[0073] (14)

[0074] (15)

[0075] in, , , Indicates the wolf , , Current location; is the current position of the Gray Wolf; , , Indicates the wolf , , Distance from other individuals; , , Indicates the direction and step length of the gray wolf's current progress towards the three alpha wolves. , , represents the coefficient vector.

[0076] According to the hunting and predation behavior of the gray wolf population, the position of the gray wolf population is updated, and the gray wolf population after the updated position is decoded. Each individual in the gray wolf population carries two kinds of information, namely the class access order and the task allocation within each class. The first k numbers of each individual are sorted by size to obtain the class access order; the last part of the numbers is sorted according to the number of classes to obtain the task allocation within each class. Through these two kinds of information, the task point sequence number and charging order of each robot can be obtained. According to the task point allocation information, the improved The algorithm generates a specific path for multiple mobile robots, calculates the fitness values ​​of the gray wolf population, sorts them, and selects the three individuals with the smallest fitness values ​​as the alpha wolves. , , , record the wolf Contains task assignment and specific path information.

[0077] S303, calculate whether the current number of iterations is equal to the maximum number of iterations. If they are equal, terminate the iterative update and output the information contained in the leader with the highest fitness value. Otherwise, return to S302.

[0078] After completing the maximum number of iterations, output the wolf with the smallest fitness value The task allocation information and specific path included, and its fitness value is the duration of this charging task.

[0079] like Figure 4 This is the state of the path planning at a certain moment of the preferred embodiment of the present invention. It can be seen that the communication can be fully connected during the charging task. The iteration result of the preferred embodiment of the present invention is a charging time of 2841s. This algorithm is used to perform charging path planning without determining the communication full connection through time slots when generating specific paths, that is, when it is not necessary to maintain full communication connection at all times. The result is a charging time of 2838s. It can be seen that the method of the present invention can effectively reduce the time cost of maintaining full communication connection at all times.

[0080] Corresponding to the above method, the present invention also provides a multi-mobile robot charging path planning system, comprising:

[0081] A map data acquisition module is used to acquire the map data of the parking lot, the mission points and the starting points of multiple mobile robots;

[0082] A clustering module is used to cluster the task points according to the map data and the location of the task points, using an improved K-Means algorithm, and according to the triple evaluation criteria, according to the minimization optimization objectives of the difference value of the charging power and the distance between different classes; the triple evaluation criteria are the distance between the task points in each class, the power required to be charged by the task points in each class, and the distance between different classes;

[0083] The charging path planning module is used to adopt the improved gray wolf optimization algorithm and the improved A joint optimization algorithm of the algorithms is used to generate a charging path for the mobile robot that maintains full communication connectivity.

[0084] In order to execute the methods corresponding to the above embodiments to achieve the corresponding functions and technical effects, the present invention also provides a multi-mobile robot charging path planning device, including: at least one processor, at least one memory and computer program instructions stored in the memory. When the computer program instructions are executed by the processor, a multi-mobile robot charging path planning method is implemented.

[0085] The memory is a computer-readable storage medium.

[0086] Based on the above description, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present invention. The aforementioned computer storage medium includes: U disk, mobile hard disk, read-only memory, random access memory, disk or optical disk, etc., various media that can store program codes.

[0087] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0088] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for planning charging paths for multiple mobile robots, characterized in that: include: Obtain the map data of the parking lot, obtain the mission points and the starting points of multiple mobile robots; According to the map data and the location of the task points, the improved K-Means algorithm is used to cluster the task points according to the triple evaluation criteria, based on the optimization objectives of minimizing the difference in charging power and the distance between different classes; the triple evaluation criteria are the distance between the task points in each class, the power required to be charged by the task points in each class, and the distance between different classes; According to the clustering results, the improved gray wolf optimization algorithm and improved A joint optimization algorithm of algorithms to generate charging paths for mobile robots that maintain full communication connectivity; According to the map data and the location of the task points, the improved K-Means algorithm is used to cluster the task points according to the triple evaluation criteria and the minimization optimization objectives of the difference in charging power and the distance between different classes, specifically including: According to the formula Determine the function value that minimizes the optimization objective; Using the formula Determine the constraints so that the task points in the same class meet the communication range restrictions; Using the formula Determine the constraints so that the number of task points in each class is less than or equal to the number of robots; in, is the difference in charging capacity, For the Class and The distance between the classes, For the Within a class Mission points and The distance between task points, is the robot communication distance, For the The number of task points in a class, is the number of robots, C is the set of classes, and min is the minimum value; According to the clustering results, the task point allocation information is initialized and iteratively optimized in the Grey Wolf Algorithm. The class access order and the task point allocation order within each class are matched with the task point sequence number of the clustering results to obtain the task point sequence number and charging order of each robot. According to the clustering results, the improved gray wolf optimization algorithm and the improved The joint optimization algorithm of the algorithm generates a charging path for the mobile robot that maintains full communication connection, including: Set the maximum number of iterations of the improved gray wolf optimization algorithm. According to the mobile robot's moving track and the maximum number of iterations, use the improved gray wolf optimization algorithm to update and optimize the mobile robot's task allocation. The algorithm generates a charging path for the mobile robot; Using improved gray wolf optimization algorithm and improved The joint optimization algorithm of the algorithm sets the maximum number of iterations, initializes the task point allocation information according to the clustering results, and uses the improved The algorithm generates a specific path that can maintain full communication connectivity, calculates the fitness value of the gray wolf population, and selects the individual with the highest fitness value as the leader of the gray wolf population; the fitness value of the gray wolf population includes: the time of the charging task; improve The algorithm adds the direction probability judgment of each path point. The probability of selecting the path point closest to the next task point is higher, and the probability of selecting other path points is lower. This makes the generated path more variable and easier to generate a path that can maintain full communication connection. The algorithm plans the specific path sequence between the starting point and the task point and the specific path sequence between each task point. Several paths are connected to form a robot charging path, and the judgment of full communication connection is introduced; discrete time dynamic topology is introduced to simulate time-varying networks, and the continuous robot movement trajectory is approximated by piecewise linear trajectory. The completion time of the charging task is the maximum value of the sum of the multi-mobile robot path driving time and the task point charging time. Divide into Equal-length time slots , , , in any time slot, the position of each node can be considered to be approximately unchanged, and the average channel gain is maintained approximately constant. The time slot set is defined as According to this time slot division method, the robot Time slot The coordinate position can be expressed as , , judge whether the multi-mobile robots can be connected to each other in each time slot, that is, each robot is within the communication range of other robots. If a robot in a certain time slot cannot maintain full connection at the position of the next time slot, a waiting strategy is adopted. The robot that exceeds the communication range of the multi-mobile robot cluster waits in place, adds a time slot, and copies its current position coordinates until the multi-mobile robot communication can be fully connected in the next time slot. If the number of time slots exceeds the preset value, this time through the improvement If the path generated by the algorithm does not meet the conditions for maintaining full communication connectivity, a new path will be generated; According to the hunting and predation behavior of the gray wolf population, the location of the gray wolf population is updated, the allocation information of the task points is updated, the parameters of the hunting and predation behavior are updated, the number of iterations is increased by 1, a specific path is generated according to the new task point allocation information, and the fitness value of the gray wolf population is calculated, and the alpha wolf is updated; Calculate whether the current number of iterations is equal to the maximum number of iterations. If they are equal, terminate the iterative update and output the information contained in the leader with the highest fitness value.

2. A multi-mobile robot charging path planning system, implementing a multi-mobile robot charging path planning method as claimed in any one of claims 1, characterized in that: The multi-mobile robot charging path planning system comprises: A map data acquisition module is used to acquire the map data of the parking lot, the mission points and the starting points of multiple mobile robots; A clustering module is used to cluster the task points according to the map data and the location of the task points, using an improved K-Means algorithm, and according to the triple evaluation criteria, according to the minimization optimization objectives of the difference value of the charging power and the distance between different classes; the triple evaluation criteria are the distance between the task points in each class, the power required to be charged by the task points in each class, and the distance between different classes; The charging path planning module is used to adopt the improved gray wolf optimization algorithm and the improved A joint optimization algorithm of the algorithms is used to generate a charging path for the mobile robot that maintains full communication connectivity.

3. A multi-mobile robot charging path planning device, characterized in that: include: At least one processor, at least one memory and computer program instructions stored in the memory, when the computer program instructions are executed by the processor, a multi-mobile robot charging path planning method as described in any one of claim 1 is implemented.

4. A multi-mobile robot charging path planning device according to claim 3, characterized in that: The memory is a computer-readable storage medium.

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