Multi-robot collaborative control method and device

Abstract

The present application relates to the field of intelligent control technology, and discloses a multi-robot collaborative control method and device, the method comprising: constructing a plurality of robots into a multi-robot system, using a graph theory algorithm to describe a directed communication topology diagram between each robot according to the task requirements of the multi-robot system, and using a hierarchical control framework to design a multi-robot collaborative control algorithm based on the multi-robot system and the directed communication topology diagram, the multi-robot collaborative control algorithm comprising a decision layer, a distributed estimation layer, and a local control layer. Since the present application constructs a multi-robot system, uses a graph theory algorithm to describe a directed communication topology diagram between each robot according to the task requirements of the multi-robot system, and uses a hierarchical control framework to design a multi-robot collaborative control algorithm, efficient collaborative control and intelligent management of the multi-robot system are achieved.

Description

Technical Field

[0001] The present application relates to the field of intelligent control technology, and in particular to a multi-robot collaborative control method and device. Background Art

[0002] At present, multi-robot systems are increasingly used in logistics, warehousing and other fields. Traditional multi-robot system path planning methods mostly rely on preset paths and assume that the operating environment is relatively simple, and have limited ability to cope with obstacles in complex environments. At the same time, existing multi-robot systems usually operate independently and lack effective collaborative control mechanisms, making it difficult to give full play to the overall advantages of the system. These limitations result in low operating efficiency and reliability of existing systems in complex environments, which cannot meet the needs of modern logistics for efficiency and flexibility. Summary of the invention

[0003] The main purpose of this application is to provide a multi-robot collaborative control method and device, aiming to solve the technical problem that the existing multi-robot system lacks an effective collaborative control mechanism.

[0004] To achieve the above objectives, the present application proposes a multi-robot collaborative control method, the method comprising:

[0005] Build multiple robots into a multi-robot system;

[0006] According to the task requirements of the multi-robot system, a graph theory algorithm is used to describe a directed communication topology graph between the robots;

[0007] Based on the multi-robot system and the directed communication topology graph, a multi-robot collaborative control algorithm is designed using a hierarchical control framework. The multi-robot collaborative control algorithm includes a decision layer, a distributed estimation layer and a local control layer.

[0008] In one embodiment, based on the multi-robot system and the directed communication topology graph, a multi-robot collaborative control algorithm is designed using a hierarchical control framework, and the multi-robot collaborative control algorithm includes the steps of a decision layer, a distributed estimation layer, and a local control layer, including:

[0009] In the decision layer, based on the multi-robot system and the directed communication topology graph, the original activity boundary is expanded by combining the robot size and the safety distance to generate a safe operation space;

[0010] constructing a node graph using the starting point, the end point and the expansion point, and finding the shortest path from the starting point to the end point in the node graph using a shortest path algorithm;

[0011] Using cubic spline interpolation method to interpolate the discrete path points of the shortest path to generate a continuous motion trajectory;

[0012] In the distributed estimation layer, the robot position and velocity information is updated and estimated based on the distributed estimation algorithm;

[0013] In the local control layer, based on the cooperative tracking control algorithm, the robot position and the speed information are adjusted;

[0014] The decision layer, the distributed estimation layer and the local control layer are integrated by adopting a hierarchical control framework to obtain a multi-robot collaborative control algorithm.

[0015] In one embodiment, the step of generating a safe operation space in the decision layer by dilating the original activity boundary based on the multi-robot system and the directed communication topology graph by combining the robot size and the safety distance includes:

[0016] In the decision layer, based on the multi-robot system and the directed communication topology graph, an original activity boundary is set by connecting each point in the point set;

[0017] Calculate the expansion distance according to the robot size and the safety distance, and expand the original boundary outward by the expansion distance to obtain an expansion boundary;

[0018] Calculating the center and perpendicular bisector of the expansion boundary based on adjacent points of the expansion boundary, and offsetting the center by a preset distance in the direction of the perpendicular bisector to obtain an expansion point;

[0019] If the expansion point is within the expansion boundary and does not cross an obstacle, the expansion point is added to the boundary point set, and a safe operation space is generated according to the boundary point set.

[0020] In one embodiment, the step of constructing a node graph using the starting point, the end point, and the expansion point, and finding the shortest path from the starting point to the end point in the node graph using the shortest path algorithm includes:

[0021] Acquire a starting point, an end point, and the expansion point from the boundary point set, and construct a node graph using the starting point, the end point, and the expansion point;

[0022] Selecting a connected node pair from the node pairs in the node graph, and selecting a node pair with the smallest distance from the connected node pairs using a shortest path algorithm;

[0023] The starting point and the end point of the node pair with the smallest distance are connected to obtain the shortest path.

[0024] In one embodiment, the step of using cubic spline interpolation to interpolate discrete path points of the shortest path to generate a continuous motion trajectory includes:

[0025] Based on the cubic spline interpolation method, the cubic spline function coefficients are solved according to the preset boundary conditions;

[0026] Constructing a cubic polynomial of the shortest path using the cubic spline function coefficients;

[0027] The cubic polynomials connecting all path segments of the shortest path are concatenated to generate a continuous motion trajectory.

[0028] In one embodiment, the step of constructing a plurality of robots into a multi-robot system comprises:

[0029] According to the model parameter uncertainties and external disturbance terms of multiple robots with Euler-Lagrangian dynamics models, the dynamic equations of the three-degree-of-freedom robot are established;

[0030] After redefining the variables of the three-degree-of-freedom robot dynamics equation, transforming the three-degree-of-freedom robot dynamics equation;

[0031] Integrate the dynamic equations of multiple three-degree-of-freedom robots to obtain a multi-robot system.

[0032] In one embodiment, the step of using a graph theory algorithm to describe a directed communication topology graph between the robots according to the task requirements of the multi-robot system includes:

[0033] According to the task requirements of the multi-robot system, obtaining vertex sets, edge sets and weight matrices of the multiple robots;

[0034] Based on the vertex set, the edge set and the weight matrix, define a directed communication topology graph;

[0035] Defining a Laplacian matrix according to the weight matrix;

[0036] Based on the information status between each robot and the corresponding central node, a diagonal weight matrix is ​​defined;

[0037] Based on a graph theory algorithm, the directed communication topology graph, the Laplace matrix and the diagonal weight matrix are analyzed to describe the directed communication topology graph between the robots.

[0038] In one embodiment, based on the multi-robot system and the directed communication topology graph, a multi-robot collaborative control algorithm is designed using a hierarchical control framework. After the multi-robot collaborative control algorithm includes the steps of a decision layer, a distributed estimation layer, and a local control layer, it also includes:

[0039] According to the robot position and velocity information of the distributed estimation algorithm, the relevant error variables are set;

[0040] Adding the relevant error variables to the distributed estimation algorithm, and combining and simplifying the distributed estimation algorithm to obtain a compact form of the distributed estimation algorithm;

[0041] Combining the robot position and the velocity information to estimate the error form, obtaining a Lyapunov function;

[0042] According to the compact form of the distributed estimation algorithm and the Lyapunov function, a stability analysis is performed on the multi-robot collaborative control algorithm to obtain a stability analysis result;

[0043] Based on the stability analysis results, simulation experiment conditions are set, and a simulation experiment is performed to verify the multi-robot collaborative control algorithm according to the simulation experiment conditions.

[0044] In one embodiment, the step of setting simulation experiment conditions based on the stability analysis results, and performing a simulation experiment verification on the multi-robot collaborative control algorithm according to the simulation experiment conditions includes:

[0045] Based on the design requirements and the stability analysis results, the simulation experiment conditions are determined by setting the control parameters of the multi-robot collaborative control algorithm, wherein the control parameters include the gain coefficient, the weight in the control law, and the path planning related parameters;

[0046] Based on the task requirements, a simulation experiment is performed to verify the multi-robot collaborative control algorithm according to the simulation experiment conditions.

[0047] In addition, to achieve the above objectives, the present application also proposes a multi-robot collaborative control device, the multi-robot collaborative control device comprising:

[0048] System building blocks for building multiple robots into a multi-robot system;

[0049] A topology description module, used to describe a directed communication topology graph between the robots using a graph theory algorithm according to the task requirements of the multi-robot system;

[0050] An algorithm integration module is used to design a multi-robot collaborative control algorithm based on the multi-robot system and the directed communication topology graph using a hierarchical control framework, wherein the multi-robot collaborative control algorithm includes a decision layer, a distributed estimation layer, and a local control layer.

[0051] In addition, to achieve the above-mentioned objectives, the present application also proposes a multi-robot collaborative control device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the multi-robot collaborative control method as described above.

[0052] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the multi-robot collaborative control method described above are implemented.

[0053] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the multi-robot collaborative control method described above.

[0054] The technical solution proposed in this application constructs multiple robots into a multi-robot system. According to the task requirements of the multi-robot system, a graph theory algorithm is used to describe the directed communication topology between each robot. Based on the multi-robot system and the directed communication topology, a hierarchical control framework is used to design a multi-robot collaborative control algorithm. The multi-robot collaborative control algorithm includes a decision layer, a distributed estimation layer, and a local control layer. Since this application constructs a multi-robot system, according to the task requirements of the multi-robot system, a graph theory algorithm is used to describe the directed communication topology between each robot, and a hierarchical control framework is used to design a multi-robot collaborative control algorithm, efficient collaborative control and intelligent management of the multi-robot system are achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0056] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0057] Figure 1 A flowchart of the first embodiment of the multi-robot collaborative control method of the present application is provided;

[0058] Figure 2 A schematic diagram of the physical structure of a single robot for this application;

[0059] Figure 3 The communication topology diagram of the multi-robot system of this application;

[0060] Figure 4 A schematic diagram of a flow chart provided for the second embodiment of the multi-robot collaborative control method of the present application;

[0061] Figure 5 This is a framework diagram of the multi-robot collaborative control method of this application;

[0062] Figure 6 A flowchart of the third embodiment of the multi-robot collaborative control method of the present application is provided;

[0063] Figure 7 A path tracking diagram for multi-robot collaborative freight transport in this application;

[0064] Figure 8 This is a schematic diagram of the module structure of a multi-robot collaborative control device according to an embodiment of the present application;

[0065] Fig. 9 Schematic diagram of the equipment structure of the hardware operating environment involved in the multi-robot collaborative control method in the embodiment of the present application.

[0066] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0067] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0068] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0069] At present, multi-robot systems have been gradually integrated into intelligent logistics systems. However, existing multi-robot systems still face many challenges in path planning, obstacle avoidance and collaborative control.

[0070] Therefore, in order to overcome the above-mentioned defects, the present application provides a solution by constructing a multi-robot system. According to the task requirements of the multi-robot system, a graph theory algorithm is used to describe the directed communication topology between the robots, and a hierarchical control framework is used to design a multi-robot collaborative control algorithm, thereby realizing efficient collaborative control and intelligent management of the multi-robot system.

[0071] It should be noted that the execution subject of each embodiment of the present application may be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, a multi-robot collaborative control device, etc. The following embodiments are described below taking a multi-robot collaborative control device as an example.

[0072] Based on this, the present application embodiment provides a multi-robot collaborative control method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the multi-robot collaborative control method of the present application.

[0073] In this embodiment, the multi-robot collaborative control method includes steps S10 to S30:

[0074] Step S10, constructing multiple robots into a multi-robot system.

[0075] It should be noted that the multi-robot system integrates multiple robot units with autonomous or collaborative capabilities and can be applied in the fields of logistics, warehousing, etc., which are not limited in this embodiment. For example, in a campus environment, the multi-robot system can collaboratively complete the handling, sorting, packaging and delivery of goods, and realize the collaborative delivery of boxes and packages through multiple robot manipulators installed on a mobile platform.

[0076] Furthermore, the step S10 includes:

[0077] According to the model parameter uncertainties and external disturbance terms of multiple robots with Euler-Lagrangian dynamics models, the dynamic equations of the three-degree-of-freedom robot are established;

[0078] After redefining the variables of the three-degree-of-freedom robot dynamics equation, transforming the three-degree-of-freedom robot dynamics equation;

[0079] Integrate the dynamic equations of multiple three-degree-of-freedom robots to obtain a multi-robot system.

[0080] It should be noted that the Euler-Lagrangian dynamics model is a mathematical framework that describes the dynamics of a physical system. Based on the energy point of view, it calculates the kinetic energy and potential energy of the system and uses the Lagrangian equation to derive the system's equation of motion. Model parameter uncertainty refers to the uncertainty or error that may exist in the system parameters (such as mass, moment of inertia, etc.) in the dynamics model. External disturbance terms refer to factors from outside the system that affect the system's motion.

[0081] It should be understood that a three-degree-of-freedom robot refers to a robot with three independent directions of motion, which can be translation or rotation, and the dynamic equations of the three-degree-of-freedom robot describe the dynamic behavior in three directions.

[0082] Exemplarily, four robots are selected, and different physical parameters of the three-DOF robots can be shown in Table 1:

[0083] Table 1 Physical parameters in multi-robot system

[0084] The i-th robot <![CDATA[m i (kg)]]> <![CDATA[l i (m)]]> 1 col(1.16,1.08,0.90) col(2.24,2.20,1.42) 2 col(1.18,1.10,0.93) col(2.43,2.22,1.44) 3 col(1.20,1.12,0.96) col(2.46,2.24,1.46) 4 col(1.22,1.14,0.99) col(2.49,2.26,1.48)

[0085] It can be understood that based on the Euler-Lagrangian dynamics model, combined with the model parameter uncertainty and external disturbance terms of the robot, the three-degree-of-freedom dynamics equations describing the dynamic behavior of the robot are derived. Furthermore, in order to simplify the equations or facilitate subsequent processing, it is necessary to redefine the variables in the equations and transform the equations accordingly. When considering a system composed of multiple three-degree-of-freedom robots, it is necessary to integrate the dynamics equations of each robot to form a set of equations that describe the dynamic behavior of the entire multi-robot system.

[0086] For ease of understanding, refer to Figure 2 This invention is provided for illustration, but is not intended to limit the present application. Figure 2 The physical structure diagram of a single robot in this application is as follows:

[0087] 1. Establish the dynamic equation of the three-degree-of-freedom robot

[0088]

[0089] In the formula, q i ∈R d ; as well as Represent the position, velocity and acceleration of the robot joints respectively;

[0090] D i (q i )=D 0i (q i )+ΔD i (q i )∈R d×d is the positive definite inertia matrix;

[0091] is the Coriolis centrifugal matrix;

[0092] G i (q i )=G 0i (q i )+ΔG i (q i )∈R d×d is the gravity matrix;

[0093] τ i ∈R d represents the input torque; f i (t) is the external disturbance; ΔD i (q i ), ΔG i (q i ) is the kinetic uncertainty.

[0094] 2. Transform the dynamic equations of the three-degree-of-freedom robot

[0095] Set x i =q i ∈R d , The dynamic equation of the three-degree-of-freedom robot is converted to:

[0096]

[0097] In the formula, is the aggregate uncertainty.

[0098] 3. Integrate the three-degree-of-freedom robot dynamics equations of multiple robots to obtain a multi-robot system.

[0099] Step S20, based on the task requirements of the multi-robot system, using a graph theory algorithm to describe a directed communication topology graph between the robots.

[0100] It should be noted that graph theory is a branch of mathematics that studies graphs (structures consisting of nodes and edges connecting nodes), and graph theory algorithms are computational methods used to solve graph-related problems, such as path search, network flow, shortest path, etc.

[0101] In a multi-robot system, a directed communication topology graph is used to represent the communication relationship between robots, where nodes represent robots and directed edges represent the flow direction of information (i.e., from which robot is the information sent to which robot).

[0102] The step S20 comprises:

[0103] According to the task requirements of the multi-robot system, obtaining vertex sets, edge sets and weight matrices of the multiple robots;

[0104] Based on the vertex set, the edge set and the weight matrix, define a directed communication topology graph;

[0105] Defining a Laplacian matrix according to the weight matrix;

[0106] Based on the information status between each robot and the corresponding central node, a diagonal weight matrix is ​​defined;

[0107] Based on a graph theory algorithm, the directed communication topology graph, the Laplace matrix and the diagonal weight matrix are analyzed to describe the directed communication topology graph between the robots.

[0108] To facilitate understanding, combine the following examples and Figure 3 This invention is provided for illustration, but is not intended to limit the present application. Figure 3It is the communication topology diagram of the multi-robot system of the present application. According to the task requirements of the multi-robot system, the specific steps of using the graph theory algorithm to describe the directed communication topology diagram between the robots are as follows:

[0109] 1. Define a directed communication topology graph

[0110] Suppose the directed topological graph is G = {V, E, A}, where V = {1, 2, ..., n} is the vertex set of n robots; E∈ = V × V represents the set of edges between robots; A = [= a ij ]∈R n×n is the weight matrix, where a ij represents the element in the i-th row and j-th column. If there is a connection between the j-th robot and the i-th robot, then a ij ≠0, a ij =0, and a ii =0 (ie no self-loop connection).

[0111] 2. Define the Laplacian matrix

[0112] The adjacent set of the i-th robot is denoted as N i ={j∈V|(j,i)∈E}, the size of the adjacent elements represents the size of the energy transfer between robots; according to the defined weight matrix A, the Laplace matrix L of the topological graph = [l ij ]∈R n×n The definition is as follows: When i = j, Otherwise ij =-a ij .

[0113] 3. Define the diagonal weight matrix

[0114] According to the information status between each robot and its corresponding virtual pilot robot (central node), the diagonal weight matrix Ξ=diag(ε1,ε2,…,ε n ), where ε i represents the diagonal elements in the matrix Ξ. If the i-th robot can directly receive information from its virtual pilot robot, then ε i > 0, otherwise ε i =0.

[0115] 4. Based on graph theory algorithms, analyze the directed communication topology graph, Laplace matrix and diagonal weight matrix to describe the communication relationship between robots.

[0116] It can be understood that the virtual pilot robot (central node) is a reference point or path guide simulated in the system through an algorithm. The virtual pilot robot can plan an optimal path in advance, and other actual robots (followers) navigate and track according to this path. By adjusting the speed and position of the virtual pilot robot, the movement rhythm and direction of the entire robot system can be controlled.

[0117] Step S30, based on the multi-robot system and the directed communication topology graph, a multi-robot collaborative control algorithm is designed using a hierarchical control framework, wherein the multi-robot collaborative control algorithm includes a decision layer, a distributed estimation layer, and a local control layer.

[0118] It should be noted that the hierarchical control framework is a control structure that decomposes a complex control system into multiple levels. Each level is responsible for different control tasks or functions, and the overall control goal is achieved through interaction between levels. In this framework, controllers at different levels can work in parallel or serially.

[0119] In this embodiment, multiple robots are constructed into a multi-robot system. According to the task requirements of the multi-robot system, a graph theory algorithm is used to describe the directed communication topology between each robot. Based on the multi-robot system and the directed communication topology, a hierarchical control framework is used to design a multi-robot collaborative control algorithm. The multi-robot collaborative control algorithm includes a decision layer, a distributed estimation layer, and a local control layer. Since this application constructs a multi-robot system, according to the task requirements of the multi-robot system, a graph theory algorithm is used to describe the directed communication topology between each robot, and a hierarchical control framework is used to design a multi-robot collaborative control algorithm, efficient collaborative control and intelligent management of the multi-robot system are achieved.

[0120] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 3 , the step S30 may include steps S301 to S306:

[0121] Step S301, in the decision layer, based on the multi-robot system and the directed communication topology graph, the original activity boundary is expanded by combining the robot size and the safety distance to generate a safe operation space.

[0122] It should be understood that in the decision-making layer of the multi-robot collaborative control algorithm, a safe operating space is generated by expanding the original activity boundary (such as the wall boundary of the campus station) and taking into account the size and safety distance of the robot.

[0123] The step S301 includes:

[0124] In the decision layer, based on the multi-robot system and the directed communication topology graph, an original activity boundary is set by connecting each point in the point set;

[0125] Calculate the expansion distance according to the robot size and the safety distance, and expand the original boundary outward by the expansion distance to obtain an expansion boundary;

[0126] Calculating the center and perpendicular bisector of the expansion boundary based on adjacent points of the expansion boundary, and offsetting the center by a preset distance in the direction of the perpendicular bisector to obtain an expansion point;

[0127] If the expansion point is within the expansion boundary and does not cross an obstacle, the expansion point is added to the boundary point set, and a safe operation space is generated according to the boundary point set.

[0128] To facilitate understanding, combine the following examples and Figure 5 This invention is provided for illustration, but is not intended to limit the present application. Figure 5 This is the framework diagram of the multi-robot collaborative control method of this application. The specific steps of generating a safe operating space are:

[0129] Boundary expansion takes into account the physical size and safety distance of the robot, and geometrically expands the obstacle boundary to ensure that the robot can avoid obstacles during path planning and reduce the risk of collision. Specifically, the original boundary is set from the point set {P k =(x k ,y k )}, where k = {1,2,...,m}, these points are connected in sequence to form a polygonal boundary. The goal of expanding the boundary is to expand these boundaries outward by a distance d (expansion distance), which is usually equal to the robot radius r plus the safety buffer distance s, that is:

[0130] d=r+s (3)

[0131] For each edge, there are two adjacent points P k and P k+1 The center of the side is M k for:

[0132]

[0133] The direction vector of the edge ξ=(x k+1 -x k ,y k+1 -y k ), the unit vector in the vertical direction is:

[0134]

[0135] Here |ξ| represents the modulus of the vector ξ. k according to The direction offset d results in two expansion points:

[0136]

[0137] Check whether the two expansion points are in the safe area and do not cross obstacles. If so, add these points to the expanded boundary set. If one side of the expansion points does not meet the conditions, keep the side that meets the conditions. The final expansion boundary is formed by connecting all qualified expansion points to form a new polygon boundary point set {Q k =(x k ,y k )}.

[0138] Step S302: construct a node graph using the starting point, the end point and the expansion point, and use the shortest path algorithm to find the shortest path from the starting point to the end point in the node graph.

[0139] It should be noted that the path from a vertex to another vertex along the edge of the graph with the smallest sum of weights on each edge is called the shortest path. The shortest path algorithm is an algorithm used to solve this type of problem.

[0140] The step S302 includes:

[0141] Acquire a starting point, an end point, and the expansion point from the boundary point set, and construct a node graph using the starting point, the end point, and the expansion point;

[0142] Selecting a connected node pair from the node pairs in the node graph, and selecting a node pair with the smallest distance from the connected node pairs using a shortest path algorithm;

[0143] The starting point and the end point of the node pair with the smallest distance are connected to obtain the shortest path.

[0144] For ease of understanding, the following examples are provided for illustration, but are not intended to limit the present application. The specific steps for obtaining the shortest path are:

[0145] In the new polygon boundary point set {Q k =(x k ,y k )}, the key points (including the starting point, the end point and the boundary point after expansion) are taken as nodes in the graph. The line between each pair of nodes represents a possible path edge. We need to check the connectivity between the node pairs, that is, whether they have a direct line of sight between obstacles. The weight of each edge is usually the straight-line distance between the nodes, and the calculation formula is:

[0146]

[0147] In the formula, (x k -xc ) and (y k -y c ) represent the coordinates of node k and node c respectively. Next, use Dijkstra algorithm to find the shortest path point set from the starting point to the end point as {S k =(x k -y k )}.

[0148] Step S303: Use cubic spline interpolation to interpolate the discrete path points of the shortest path to generate a continuous motion trajectory.

[0149] It should be noted that cubic spline interpolation is a method of approximating a set of discrete data points by constructing a series of cubic polynomials (i.e., cubic curves). Each polynomial segment is between two adjacent data points, and these polynomial segments meet certain smoothness conditions at the connection points, i.e., their first-order and second-order derivatives are continuous.

[0150] Furthermore, the step S303 includes:

[0151] Based on the cubic spline interpolation method, the cubic spline function coefficients are solved according to the preset boundary conditions;

[0152] Constructing a cubic polynomial of the shortest path using the cubic spline function coefficients;

[0153] The cubic polynomials connecting all path segments of the shortest path are concatenated to generate a continuous motion trajectory.

[0154] To facilitate understanding, combine the following examples and Figure 3 The specific steps of generating a continuous motion trajectory are as follows:

[0155] In the discrete shortest path point set {S k =(x k -y k )}, using the cubic spline function F k (x) Interpolation is performed in each path segment. Here, ak, bk, ck, and dk are set as the solution coefficients. The cubic spline function F of each path segment is k (x) can be expressed by a cubic polynomial as:

[0156] F k (x) = a k +b k (xx k )+c k (xx k ) 2 +d k (xx k )3 (9)

[0157] Get the trajectory of the virtual pilot robot The smoothed trajectory is then expressed in parameterized vector form: l (t) = Tra(t) = (x(t), y(t)). At this time, the state of the virtual pilot robot is expressed as Among them, χ l , l and a l Indicates the position state, velocity and acceleration of the virtual pilot robot.

[0158] Step S304, in the distributed estimation layer, based on the distributed estimation algorithm, the robot position and velocity information is updated and estimated.

[0159] It should be noted that a distributed estimation algorithm is a type of algorithm that works collaboratively between multiple robots to jointly process and fuse data from different sources to obtain the optimal estimate of one or more variables.

[0160] Exemplarily, the distributed estimation algorithm can be expressed as:

[0161]

[0162] In the formula, and They are respectively i and ψ i The observed value; η1 and η2 are constant gains; χ l and ψ l Indicates the position status and speed of the virtual pilot robot.

[0163] Step S305: In the local control layer, based on the collaborative tracking control algorithm, adjust the robot position and the speed information.

[0164] It should be noted that cooperative tracking control algorithms are a type of control algorithm specifically designed for multi-robot systems that can handle the interactions and constraints between multiple robots to achieve cooperative tracking and positioning. These algorithms usually take into account factors such as the robot's dynamic characteristics, communication delays, and environmental noise to ensure control accuracy and robustness.

[0165] Exemplarily, the cooperative tracking control algorithm can be designed as follows:

[0166]

[0167] In the formula, Λ=diag{Λ1,Λ2,...,Λ d} is a positive diagonal matrix, and Φ1 and Φ2 are positive definite symmetric matrices; is the observed value of the aggregate uncertainty.

[0168] Step S306, using a hierarchical control framework to integrate the decision layer, the distributed estimation layer and the local control layer to obtain a multi-robot collaborative control algorithm.

[0169] In the decision layer of this embodiment, based on the multi-robot system and the directed communication topology graph, the original activity boundary is expanded by combining the robot size and the safety distance to generate a safe operation space, the starting point, the end point and the expansion point are used to construct a node graph, and the shortest path algorithm is used to find the shortest path from the starting point to the end point in the node graph, and the discrete path points of the shortest path are interpolated using the cubic spline interpolation method to generate a continuous motion trajectory. In the distributed estimation layer, based on the distributed estimation algorithm, the robot position and speed information are updated and estimated. In the local control layer, based on the collaborative tracking control algorithm, the robot position and speed information are adjusted. The hierarchical control framework is used to integrate the decision layer, the distributed estimation layer and the local control layer to obtain a multi-robot collaborative control algorithm, thereby realizing path planning and external obstacle avoidance, and improving the safety and adaptability of the multi-robot system in complex environments.

[0170] Based on the first embodiment of the present application, in the third embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 6 , the step S30 may include steps S311 to S315:

[0171] Step S311, setting relevant error variables according to the robot position and speed information of the distributed estimation algorithm.

[0172] It should be understood that in a control system, an error variable is a variable used to represent the difference between the actual state and the desired state. In a distributed estimation algorithm, the error variable may represent the difference between the estimated position and velocity and the true position and velocity. Based on the robot position and velocity information obtained by the distributed estimation algorithm, the difference between the true position and velocity, i.e., the error variable, is defined and calculated.

[0173] Step S312: adding the relevant error variables to the distributed estimation algorithm, and merging and simplifying the distributed estimation algorithm to obtain a compact form of the distributed estimation algorithm.

[0174] It can be understood that the error variables are added to the distributed estimation algorithm, and the algorithm is merged and simplified to obtain a more compact and easier to implement form.

[0175] Step S313, combining the robot position and the velocity information to estimate the error form, and obtaining the Lyapunov function.

[0176] It should be noted that the Lyapunov function is a function used to judge the stability of a system in control theory and stability analysis. If the Lyapunov function is positive near a certain equilibrium point and its derivative is negative near this point, then the system is stable at this point.

[0177] Combining the estimated error form of the robot position and velocity information, a Lyapunov function is constructed for subsequent stability analysis.

[0178] Step S314: performing stability analysis on the multi-robot collaborative control algorithm according to the compact form of the distributed estimation algorithm and the Lyapunov function to obtain a stability analysis result.

[0179] It can be understood that the compact form of the distributed estimation algorithm and the Lyapunov function are used to conduct stability analysis on the multi-robot cooperative control algorithm to determine whether the system can remain stable after being subjected to external disturbances.

[0180] Step S315, setting simulation experiment conditions based on the stability analysis results, and performing a simulation experiment to verify the multi-robot collaborative control algorithm according to the simulation experiment conditions.

[0181] It should be noted that simulation experiment conditions refer to the conditions set when conducting simulation experiments in order to simulate the real environment or specific scenarios, including control parameters, task requirements, etc.

[0182] The step S315 comprises:

[0183] Based on the design requirements and the stability analysis results, the simulation experiment conditions are determined by setting the control parameters of the multi-robot collaborative control algorithm, wherein the control parameters include the gain coefficient, the weight in the control law, and the path planning related parameters;

[0184] Based on the task requirements, a simulation experiment is performed to verify the multi-robot collaborative control algorithm according to the simulation experiment conditions.

[0185] It should be noted that control parameters refer to parameters used to adjust the behavior of the system in the control system. In the multi-robot collaborative control algorithm, control parameters can include gain coefficients, weights in the control law, path planning related parameters, etc.

[0186] It can be understood that based on the design requirements and stability analysis results, the control parameters of the multi-robot collaborative control algorithm are set to simulate the real environment or specific scenarios. According to the set simulation experiment conditions, the multi-robot collaborative control algorithm is simulated to verify the effectiveness and stability of the algorithm.

[0187] To facilitate understanding, combine the following examples and Figure 7 This invention is provided for illustration, but is not intended to limit the present application. Figure 7 The specific steps of the path tracking diagram, stability analysis and simulation experiment verification of the multi-robot collaborative freight transport in this application are as follows:

[0188] 1. Set relevant error variables

[0189] Combined with the position and velocity estimation error form in the distributed estimation layer algorithm, set and Then, the mathematical form of the distributed estimation layer algorithm can be transformed into:

[0190]

[0191] 2. Obtaining a compact form of the distributed estimation algorithm

[0192] set up The compact form of the distributed estimation algorithm can be expressed as:

[0193]

[0194] Then, set X = col(χ,ψ)∈R 2nd , the compact form of the above distributed estimation algorithm is transformed into:

[0195]

[0196] Then, it is calculated that the characteristic equation of θ is zero, that is, det(λI2nd-θ)=0, and it is deduced that the distributed estimation layer is Hurwitz stable.

[0197] 3. Analysis of Lyapunov function

[0198] Combining the position and velocity estimation error forms in the local layer algorithm, the Lyapunov function is constructed:

[0199]

[0200] Finally, according to the compact form of the distributed estimation algorithm and the Lyapunov function, the stability analysis of the multi-robot collaborative control algorithm is performed to obtain the stability analysis result.

[0201] 4. Determine the simulation experiment conditions

[0202] According to the design requirements of the system and the results of the stability analysis, the control parameters of the collaborative freight control algorithm are set. These parameters include gain coefficients, weights in the control law, and parameters related to path planning, such as speed limits, acceleration limits, etc., to ensure that the parameter settings meet the stability requirements of the multi-robot system. The control parameters selected in the control algorithm are detailed in Table 2.

[0203] Table 2 Control parameters in control algorithm

[0204] Control parameters Value Control parameters Value <![CDATA[η1]]> 2.0 <![CDATA[Φ1]]> 200I <![CDATA[η2]]> 4.0 <![CDATA[Φ2]]> 300I Λ diag{100,100} s 2

[0205] Based on the task requirements, the multi-robot collaborative control algorithm was verified through simulation experiments according to the simulation experimental conditions, and the path tracking simulation effect of multiple robots performing freight tasks in the campus station was obtained.

[0206] This embodiment sets relevant error variables according to the robot position and speed information of the distributed estimation algorithm, adds the relevant error variables to the distributed estimation algorithm, merges and simplifies the distributed estimation algorithm, obtains a compact form of the distributed estimation algorithm, estimates the error form in combination with the robot position and speed information, obtains a Lyapunov function, performs stability analysis on the multi-robot collaborative control algorithm according to the compact form of the distributed estimation algorithm and the Lyapunov function, obtains stability analysis results, sets simulation experiment conditions based on the stability analysis results, and performs simulation experiment verification on the multi-robot collaborative control algorithm according to the simulation experiment conditions, thereby ensuring the theoretical reliability of the algorithm and improving the algorithm practicability and system collaborative efficiency.

[0207] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the multi-robot collaborative control method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0208] This application also provides a multi-robot collaborative control device, please refer to Figure 8 , the multi-robot collaborative control device comprises:

[0209] A system construction module 10, used to construct a plurality of robots into a multi-robot system;

[0210] A topology description module 20, used to describe a directed communication topology graph between the robots using a graph theory algorithm according to the task requirements of the multi-robot system;

[0211] The algorithm integration module 30 is used to design a multi-robot collaborative control algorithm based on the multi-robot system and the directed communication topology graph using a hierarchical control framework, wherein the multi-robot collaborative control algorithm includes a decision layer, a distributed estimation layer and a local control layer.

[0212] The multi-robot collaborative control device provided by the present application adopts the multi-robot collaborative control method in the above embodiment, which can solve the technical problem that the existing multi-robot system lacks an effective collaborative control mechanism. Compared with the prior art, the beneficial effects of the multi-robot collaborative control device provided by the present application are the same as the beneficial effects of the multi-robot collaborative control method provided by the above embodiment, and the other technical features in the multi-robot collaborative control device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0213] The present application provides a multi-robot collaborative control device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the multi-robot collaborative control method in the above-mentioned embodiment one.

[0214] Reference below Fig. 9 , which shows a schematic diagram of the structure of a multi-robot collaborative control device suitable for implementing the embodiment of the present application. The multi-robot collaborative control device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Desctions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Fig. 9 The multi-robot collaborative control device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0215] like Fig. 9As shown, the multi-robot cooperative control device may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the multi-robot cooperative control device are also stored. The processing device 1001, ROM1002 and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the multi-robot collaborative control device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a multi-robot collaborative control device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0216] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0217] The multi-robot collaborative control device provided by the present application adopts the multi-robot collaborative control method in the above embodiment, which can solve the technical problem that the existing multi-robot system lacks an effective collaborative control mechanism. Compared with the prior art, the beneficial effects of the multi-robot collaborative control device provided by the present application are the same as the beneficial effects of the multi-robot collaborative control method provided by the above embodiment, and the other technical features in the multi-robot collaborative control device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0218] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0219] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0220] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, wherein the computer-readable program instructions are used to execute the multi-robot collaborative control method in the above-mentioned embodiment.

[0221] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0222] The above-mentioned computer-readable storage medium may be included in the multi-robot collaborative control device; or it may exist independently without being assembled into the multi-robot collaborative control device.

[0223] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the multi-robot collaborative control device, the multi-robot collaborative control device: constructs multiple robots into a multi-robot system, uses a graph theory algorithm to describe the directed communication topology graph between each robot according to the task requirements of the multi-robot system, and based on the multi-robot system and the directed communication topology graph, uses a hierarchical control framework to design a multi-robot collaborative control algorithm, and the multi-robot collaborative control algorithm includes a decision layer, a distributed estimation layer and a local control layer.

[0224] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0225] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0226] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0227] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned multi-robot collaborative control method, and can solve the technical problem that the existing multi-robot system lacks an effective collaborative control mechanism. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the multi-robot collaborative control method provided by the above-mentioned embodiment, and will not be repeated here.

[0228] The present application also provides a computer program product, including a computer program, which implements the steps of the multi-robot collaborative control method as described above when executed by a processor.

[0229] The computer program product provided by the present application can solve the technical problem that the existing multi-robot system lacks an effective collaborative control mechanism. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the multi-robot collaborative control method provided by the above embodiment, which will not be repeated here.

[0230] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or directly / indirectly applied in other related technical fields, are included in the patent protection scope of the present application.

Claims

1. A multi-robot collaborative control method, characterized in that: The method comprises the following steps: Build multiple robots into a multi-robot system; According to the task requirements of the multi-robot system, a graph theory algorithm is used to describe a directed communication topology graph between the robots; Based on the multi-robot system and the directed communication topology graph, a multi-robot collaborative control algorithm is designed using a hierarchical control framework. The multi-robot collaborative control algorithm includes a decision layer, a distributed estimation layer and a local control layer.

2. The multi-robot collaborative control method according to claim 1, characterized in that: Based on the multi-robot system and the directed communication topology graph, a multi-robot collaborative control algorithm is designed using a hierarchical control framework, and the multi-robot collaborative control algorithm includes the steps of a decision layer, a distributed estimation layer and a local control layer, including: In the decision layer, based on the multi-robot system and the directed communication topology graph, the original activity boundary is expanded by combining the robot size and the safety distance to generate a safe operation space; constructing a node graph using the starting point, the end point and the expansion point, and finding the shortest path from the starting point to the end point in the node graph using a shortest path algorithm; Using cubic spline interpolation method to interpolate the discrete path points of the shortest path to generate a continuous motion trajectory; In the distributed estimation layer, the robot position and velocity information is updated and estimated based on the distributed estimation algorithm; In the local control layer, based on the cooperative tracking control algorithm, the robot position and the speed information are adjusted; The decision layer, the distributed estimation layer and the local control layer are integrated by adopting a hierarchical control framework to obtain a multi-robot collaborative control algorithm.

3. The multi-robot collaborative control method according to claim 2, characterized in that: In the decision layer, based on the multi-robot system and the directed communication topology graph, the step of expanding the original activity boundary by combining the robot size and the safety distance to generate a safe operation space includes: In the decision layer, based on the multi-robot system and the directed communication topology graph, an original activity boundary is set by connecting each point in the point set; Calculate the expansion distance according to the robot size and the safety distance, and expand the original boundary outward by the expansion distance to obtain an expansion boundary; Calculating the center and perpendicular bisector of the expansion boundary based on adjacent points of the expansion boundary, and offsetting the center by a preset distance in the direction of the perpendicular bisector to obtain an expansion point; If the expansion point is within the expansion boundary and does not cross an obstacle, the expansion point is added to the boundary point set, and a safe operation space is generated according to the boundary point set.

4. The multi-robot collaborative control method according to claim 2, characterized in that: The step of constructing a node graph using the starting point, the end point and the expansion point, and finding the shortest path from the starting point to the end point in the node graph using the shortest path algorithm comprises: Acquire a starting point, an end point, and the expansion point from the boundary point set, and construct a node graph using the starting point, the end point, and the expansion point; Selecting a connected node pair from the node pairs in the node graph, and selecting a node pair with the smallest distance from the connected node pairs using a shortest path algorithm; The starting point and the end point of the node pair with the smallest distance are connected to obtain the shortest path.

5. The multi-robot collaborative control method according to claim 2, characterized in that: The step of using the cubic spline interpolation method to interpolate the discrete path points of the shortest path to generate a continuous motion trajectory includes: Based on the cubic spline interpolation method, the cubic spline function coefficients are solved according to the preset boundary conditions; Constructing a cubic polynomial of the shortest path using the cubic spline function coefficients; The cubic polynomials connecting all path segments of the shortest path are concatenated to generate a continuous motion trajectory.

6. The multi-robot collaborative control method according to any one of claims 1 to 5, characterized in that: The step of constructing a plurality of robots into a multi-robot system comprises: According to the model parameter uncertainties and external disturbance terms of multiple robots with Euler-Lagrangian dynamics models, the dynamic equations of the three-degree-of-freedom robot are established; After redefining the variables of the three-degree-of-freedom robot dynamics equation, transforming the three-degree-of-freedom robot dynamics equation; Integrate the dynamic equations of multiple three-degree-of-freedom robots to obtain a multi-robot system.

7. The multi-robot collaborative control method according to any one of claims 1 to 5, characterized in that: The step of using a graph theory algorithm to describe a directed communication topology graph between the robots according to the task requirements of the multi-robot system comprises: According to the task requirements of the multi-robot system, obtaining vertex sets, edge sets and weight matrices of the multiple robots; Based on the vertex set, the edge set and the weight matrix, define a directed communication topology graph; Defining a Laplacian matrix according to the weight matrix; Based on the information status between each robot and the corresponding central node, a diagonal weight matrix is ​​defined; Based on a graph theory algorithm, the directed communication topology graph, the Laplace matrix and the diagonal weight matrix are analyzed to describe the directed communication topology graph between the robots.

8. The multi-robot collaborative control method according to any one of claims 1 to 5, characterized in that: Based on the multi-robot system and the directed communication topology graph, a multi-robot collaborative control algorithm is designed using a hierarchical control framework. After the multi-robot collaborative control algorithm includes the steps of a decision layer, a distributed estimation layer and a local control layer, it also includes: According to the robot position and velocity information of the distributed estimation algorithm, the relevant error variables are set; Adding the relevant error variables to the distributed estimation algorithm, and combining and simplifying the distributed estimation algorithm to obtain a compact form of the distributed estimation algorithm; Combining the robot position and the velocity information to estimate the error form, obtaining a Lyapunov function; According to the compact form of the distributed estimation algorithm and the Lyapunov function, a stability analysis is performed on the multi-robot collaborative control algorithm to obtain a stability analysis result; Based on the stability analysis results, simulation experiment conditions are set, and a simulation experiment is performed to verify the multi-robot collaborative control algorithm according to the simulation experiment conditions.

9. The multi-robot collaborative control method according to claim 8, characterized in that: The step of setting simulation experiment conditions based on the stability analysis results, and performing simulation experiment verification on the multi-robot collaborative control algorithm according to the simulation experiment conditions includes: Based on the design requirements and the stability analysis results, the simulation experiment conditions are determined by setting the control parameters of the multi-robot collaborative control algorithm, wherein the control parameters include the gain coefficient, the weight in the control law, and the path planning related parameters; Based on the task requirements, a simulation experiment is performed to verify the multi-robot collaborative control algorithm according to the simulation experiment conditions.

10. A multi-robot collaborative control device, characterized in that: The multi-robot collaborative control device comprises: System building blocks for building multiple robots into a multi-robot system; A topology description module, used to describe a directed communication topology graph between the robots using a graph theory algorithm according to the task requirements of the multi-robot system; An algorithm integration module is used to design a multi-robot collaborative control algorithm based on the multi-robot system and the directed communication topology graph using a hierarchical control framework, wherein the multi-robot collaborative control algorithm includes a decision layer, a distributed estimation layer, and a local control layer.

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