Distributed multi-robot competition cooperative control method resistant to noise and communication interference

Abstract

The application discloses a kind of distributed multi-robot competition collaborative control methods of anti-noise and communication interference, comprising the following steps: obtaining the position information of each robot in multi-robot system;Obtain the position information of the tracking target, with the Euclidean distance of each robot and target as input signal;Apply the zero neural dynamics real-time solver with anti-noise performance, and solve the optimal robot for executing target tracking;With the consistency estimator of communication interference compensation, realize distributed multi-robot system, and compensate the influence of communication interference;Control multi-robot system to complete target tracking task;Under the condition that noise and communication interference exist simultaneously, the application provides a reliable method for the competition collaborative control of multi-robot system, effectively improves control efficiency, robustness and accuracy.

Description

Technical Field

[0001] This invention relates to the field of multi-robot competitive and cooperative control technology, specifically to a distributed multi-robot competitive and cooperative control method that is resistant to noise and communication interference. Background Technology

[0002] With the continuous development of robotics technology, the collaborative decision-making capabilities of multi-robot systems have been significantly enhanced, demonstrating enormous application potential in dynamic target search and tracking tasks in complex environments. Compared to single robots, multi-robot systems, through networked collaborative operations, greatly improve task execution efficiency, expand perception and working range, and significantly enhance system redundancy and fault tolerance. In practical applications of multi-robot target tracking tasks, competitive collaborative control strategies are typically adopted to rationally allocate system resources and reduce overall cluster energy consumption. The winner-takes-all strategy, as a typical competitive mechanism, has been widely used to implement competitive behavior in multi-robot systems. Specifically, this mechanism, based on the state information of each robot (such as its relative distance to the target), selects the robot with the optimal state in real time. One robot acts as the "winner" and performs the task, while the rest of the robots remain stationary or on standby. This state-based competition mechanism fundamentally avoids motion conflicts and redundant energy consumption caused by task overlap within the cluster, significantly improving the overall collaborative efficiency of the system while achieving optimal allocation of cluster resources.

[0003] However, in practical applications, multi-robot systems are inevitably affected by noise and communication interference. Noise can interfere with the decision-making results of a winner-takes-all strategy, leading to disordered winner selection and difficulty in accurately completing tasks. Communication interference can affect distributed consensus estimation, introducing information divergence into multi-robot systems, causing robots that should be on standby to be incorrectly activated, thus hindering the normal progress of tasks. Existing solutions typically only consider the impact of noise or focus on changes in communication structure under ideal communication conditions, and cannot simultaneously handle noise and communication interference. Research in this area remains lacking. Therefore, there is an urgent need for a distributed multi-robot control scheme that can effectively resist noise and communication interference in complex, non-ideal physical and network environments, thereby achieving high reliability and high precision. Summary of the Invention

[0004] To address the aforementioned shortcomings in the prior art, this invention provides a distributed multi-robot competitive cooperative control method that is resistant to noise and communication interference.

[0005] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows: A distributed multi-robot competitive cooperative control method resistant to noise and communication interference includes the following steps: S1. Obtain the position information of the target being tracked and the position information of each robot in the multi-robot system, calculate the distance between each robot and the target being tracked, and use this distance to form an input vector; S2. The optimal robot selection process based on the winner-takes-all strategy is transformed into a quadratic programming problem with equality and inequality constraints; S3. Construct a noise-resistant nullable neurodynamic real-time solver: Introduce a nonlinear activation function and an error integral term into the solver, and obtain the optimal robot selection vector by solving the quadratic programming problem in real time, so as to resist the interference of external noise on the decision results; S4. Construct a consensus estimator with communication interference compensation: Introduce a real-time disturbance observer into the distributed consensus estimator to estimate unknown communication interference and use the observation results to compensate for the transmission error caused by communication interference, thereby realizing the distributed application of the solver in a multi-robot system.

[0006] Furthermore, the winner-takes-all strategy in S2 is expressed as follows:

[0007] In the formula, For the output vector The Each element represents the on / off instruction information indicating whether the robot has been selected to perform the target tracking task; This represents a winner-takes-all strategy. For input vectors The There are n elements, of which: Condition 1 is: if Belongs to the input vector Center front The largest element, at this time the corresponding output element This indicates that the robot has been selected to perform the task; Condition 2 is: if Not part of the input vector Center front The largest element, at this time the corresponding output element ; when If the robot has a historical selection record, it is controlled to return to the initial position and wait for the call; otherwise, it is controlled to remain stationary.

[0008] Furthermore, the quadratic programming problem in S2 is defined as follows:

[0009] In the formula, For scalar parameters, and They represent the first and second parts of the input vector, respectively. Yamato Larger elements; T is the matrix transpose operation; It is a column vector whose elements are all 1s; ,in for 3D identity matrix; for A dimensional column vector whose constituent elements are 1-dimensional column vector sum A column vector of all zeros. It is a real number.

[0010] Furthermore, the noise-resistant nullification neural dynamics real-time solver in S3 is defined as follows:

[0011] In the formula, These are the convergence parameters; This represents finding the inverse of a matrix. The state matrix; It is a coefficient matrix; A vector containing the output of the winner-takes-all strategy; A vector containing the input of a winner-takes-all strategy; They are respectively Regarding time The derivative; For integration variables; External noise interference; Let be the nonlinear activation function.

[0012] Furthermore, the consistency estimator with communication interference compensation in S4 is defined as follows:

[0013] In the formula, This is the average estimate of the values ​​of all robots in a multi-robot system. For state variables, Indicates the first The robot and the first The communication status of the robot, if Then the first The robot can receive the first... Information about the robot is required, but not vice versa. This is the communication interference vector. For communication interference The estimated value of a vector, its index. For the first in the corresponding vector One element, For a specific moment; For convergence parameters, For the output vector The Each element.

[0014] Furthermore, the execution process of the target tracking task is as follows: The optimal robot selection vector output by the noise-resistant nullified neurodynamics real-time solver. Each component in As the first The activation weight of each robot; According to the control strategy:

[0015] Generate motion speed commands for each robot, where, For speed gain; The first The real-time location of the robot and the tracking target; The square of the Euclidean norm; The speed command is sent to the underlying controller of each robot, driving the selected robot to track the target, while causing the unselected robots to return to their initial positions to stand by.

[0016] The present invention has the following beneficial effects: The solution of this invention can effectively resist noise and communication interference in complex non-ideal physical and network environments, thereby realizing a highly reliable and high-precision distributed multi-robot control solution. Attached Figure Description

[0017] Figure 1 is a flowchart of the distributed multi-robot competitive cooperative control method that is resistant to noise and communication interference according to the present invention; Figure 2 is a schematic diagram of the implementation of the noise-resistant nullable neurodynamic real-time solver function in the application of the present invention; Figure 3 is a simulation diagram of the motion process of achieving optimal robot selection and multi-robot system under the application of the present invention; Figure 4 is a schematic diagram of the robot used in implementing the present invention; Figure 5 illustrates the process of implementing the target tracking task of a multi-robot system under the application of this invention. Detailed Implementation

[0018] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0019] A competitive cooperative control method for a distributed multi-robot system resistant to noise and communication interference, wherein the multi-winner-takes-all strategy can be described as...

[0020] in, For input vectors The element It serves as the input information for selecting the optimal robot in a distributed multi-robot target tracking control method that is resistant to noise and communication interference. For the output vector The Each element represents the on / off instruction information indicating whether the robot has been selected to perform the target tracking task; This represents a winner-takes-all operation; the equation holds true under the following conditions: Condition 1: If Belongs to the input vector Center front The largest element, at this time the corresponding output element The robot is selected to perform the task; Condition 2: If Not part of the input vector Center front The largest element, at this time the corresponding output element ;when If the robot has a historical selection record, it is controlled to return to the initial position and wait for the call; otherwise, it is controlled to remain stationary.

[0021] To achieve real-time solution for the winner-takes-all strategy, it is transformed into a quadratic programming problem of the following form:

[0022] in, For a scalar parameter, and They represent the first and second parts of the input vector, respectively. Yamato Larger elements; T is the matrix transpose operation; It is a column vector whose elements are all 1s; ,in for 3D identity matrix; for A dimensional column vector whose constituent elements are 1-dimensional column vector sum The problem is represented by a column vector of all zeros. To facilitate the solution, the quadratic programming problem is transformed into a system of nonlinear algebraic equations of the following form using the Caro-Kuhn-Tucker conditions and the nonlinear complementary problem function:

[0023] in, for 3D identity matrix; for A column vector of all zeros; and These are the Lagrange multipliers corresponding to the equality constraints and inequality constraints of the quadratic programming problem, respectively. Let be the auxiliary vector, where for 3D column vector, Therefore, the error function of the nonlinear algebraic equation system is defined as follows:

[0024] Based on the aforementioned error function, and applying null-based neural dynamics, a noise-resistant real-time solver for null-based neural dynamics was designed as follows:

[0025] in, For convergence parameters; matrix The specific definition is

[0026] in, ,symbol For Hadema division; superscript This represents finding the inverse of a matrix. External noise interference.

[0027] To accelerate the convergence speed of the noise-resistant nullification neurodynamics real-time solver, a nonlinear activation function is introduced into the solver, defined as follows:

[0028] in The convergence parameter is used; the nonlinear activation function is introduced into a noise-resistant nullable neurodynamics real-time solver to obtain:

[0029] To enable the distributed application of the noise-resistant nullable neural dynamics real-time solver in multi-robot systems and to resist the effects of communication interference, a consensus estimator with communication interference compensation is constructed, defined as follows:

[0030] in, This is the average estimate for the robot; For state variables; For communication interference; For communication interference The estimated value, subscript For the first in the corresponding vector One element; Indicates the first The robot and the first The communication status of the robot, if Then the first The robot can receive the first... Information about the robot is required, but the reverse is not true. The convergence parameters are used to combine the zero-form neural dynamics real-time solver with noise resistance to form a competitive and cooperative control method for distributed multi-robot systems that is resistant to noise and communication interference.

[0031] A competitive cooperative control method for a distributed multi-robot system resistant to noise and communication interference, wherein the input is the distance between each robot and the tracking target, and the input is expressed as...

[0032] in, The first The real-time positions of the robot and the target being tracked are determined; using the method described above, the optimal robot for performing the target tracking task is selected, and the following control strategy is employed to control this optimal robot:

[0033] in, For speed gain; For the first The initial position of each robot.

[0034] The workflow of the present invention will be described below with reference to a specific embodiment.

[0035] Assume a scenario where a multi-robot system consisting of eight robots is being controlled, and the size of the work area is... (Unit: meters), set the initial position coordinates of the eight robots as follows: , , , , , , , (Unit: meters); The initial position coordinates of the target being tracked are: (Unit: meter), its motion method can be represented as: ,in, This is the position vector within the horizontal plane. The task duration is 10 seconds. Specific parameter settings are as follows: , , , , , , , Linear noise and communication interference are introduced into both computer simulations and physical experiments. Linear noise is defined as: Communication interference is defined as: Using MATLAB software, the proposed noise- and communication-interference-resistant distributed multi-robot system competitive cooperative control method was simulated to select the optimal robot for the target tracking task and complete the target tracking task.

[0036] The present invention will now be further described with reference to the accompanying drawings.

[0037] Figure 1 The flowchart of this invention is as follows: First, the position information of each robot in the multi-robot system is obtained; then, the position information of the target being tracked is obtained, using the Euclidean distance between each robot and the target as the input signal; subsequently, a noise-resistant nullified neurodynamics real-time solver is applied to solve for the optimal robot to perform target tracking; next, a consistency estimator with communication interference compensation is used to realize the distributed multi-robot system and compensate for the impact of communication interference; finally, the multi-robot system is controlled to complete the target tracking task.

[0038] Figure 2 This diagram illustrates the implementation of a noise-resistant nullable neurodynamics real-time solver for the application of this invention. The diagram shows the optimal selection of a win-win-all strategy under two different noise conditions, with an output of 1 indicating selection and zero indicating non-selection. Figure 1 This indicates the choice of a noise-resistant nullable neural dynamics real-time solver under the influence of linear noise. Figure 2The figure shows the selection of the noise-resistant nullable neurodynamic real-time solver under the influence of bounded random noise; the content of the figure illustrates that the proposed noise-resistant nullable neurodynamic real-time solver can accurately complete the selection task under different noise conditions.

[0039] Figure 3 To achieve the optimal robot selection and multi-robot system simulation motion process diagram under the application of this invention; by Figure 3 It is evident that, even in the presence of noise and communication interference, the multi-robot system can still select the optimal robot for tracking the target in real time and accurately, and the previously selected optimal robot is also driven back to its initial position, successfully completing the target tracking task. This verifies the feasibility of the proposed noise- and communication-interference-resistant distributed multi-robot system competitive cooperative control method.

[0040] Figure 4 is a schematic diagram of the robot used in the application of this invention; the robot shown in the figure is an E-puck2 wheeled robot used to perform target tracking tasks, and its main controller is a 32-bit STM32F4 microprocessor. This robot can perceive its own position information and that of the target in real time, and communicate with other robots, providing a hardware verification platform for the distributed control method described in this invention.

[0041] Figure 5 This is a diagram illustrating the implementation process of a multi-robot system completing a target tracking task in an embodiment of the present invention; by Figure 5 As can be seen, when the target being tracked is displaced, the system can dynamically select different optimal robots to perform the target tracking task based on the current state, while also driving the previously selected optimal robot to return to its initial position, further verifying the reliability of the invention in actual execution.

[0042] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0043] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0044] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0045] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

[0046] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of this invention.

Claims

1. A distributed multi-robot competitive cooperative control method resistant to noise and communication interference, characterized in that, Includes the following steps: S1. Obtain the position information of the target being tracked and the position information of each robot in the multi-robot system, calculate the distance between each robot and the target being tracked, and use this distance to form an input vector; S2. The optimal robot selection process based on the winner-takes-all strategy is transformed into a quadratic programming problem with equality and inequality constraints; S3. Construct a noise-resistant nullable neurodynamic real-time solver: Introduce a nonlinear activation function and an error integral term into the solver, and obtain the optimal robot selection vector by solving the quadratic programming problem in real time, so as to resist the interference of external noise on the decision results; S4. Construct a consensus estimator with communication interference compensation: Introduce a real-time disturbance observer into the distributed consensus estimator to estimate unknown communication interference and use the observation results to compensate for the transmission error caused by communication interference, thereby realizing the distributed application of the solver in a multi-robot system.

2. The distributed multi-robot competitive cooperative control method resistant to noise and communication interference according to claim 1, characterized in that, The multi-winner-takes-all strategy in S2 is represented as follows: In the formula, For the output vector The Each element represents the on / off instruction information indicating whether the robot has been selected to perform the target tracking task; This represents a winner-takes-all strategy. For input vectors The There are n elements, of which: Condition 1 is: if Belongs to the input vector Center front The largest element, at this time the corresponding output element This indicates that the robot has been selected to perform the task; Condition 2 is: if Not part of the input vector Center front The largest element, at this time the corresponding output element ; when If the robot has a historical selection record, it is controlled to return to the initial position and wait for the call; otherwise, it is controlled to remain stationary.

3. The distributed multi-robot competitive cooperative control method resistant to noise and communication interference according to claim 1, characterized in that, The quadratic programming problem in S2 is defined as follows: In the formula, For scalar parameters, and They represent the first and second parts of the input vector, respectively. dahedi Large elements; T represents the matrix transpose operation; It is a column vector whose elements are all 1s; ,in for 3D identity matrix; for A dimensional column vector whose constituent elements are 1-dimensional column vector sum A column vector of all zeros. It is a real number.

4. The distributed multi-robot competitive cooperative control method resistant to noise and communication interference according to claim 1, characterized in that, The noise-resistant nullable neurodynamic real-time solver in S3 is defined as follows: In the formula, These are the convergence parameters; This represents finding the inverse of a matrix. The state matrix; It is a coefficient matrix; A vector containing the output of the winner-takes-all strategy; A vector containing the input of a winner-takes-all strategy; They are respectively Regarding time The derivative; For integration variables; External noise interference; Let be the nonlinear activation function.

5. The distributed multi-robot competitive cooperative control method resistant to noise and communication interference according to claim 1, characterized in that, The consistency estimator with communication interference compensation in S4 is defined as follows: In the formula, This is the average estimate of the values ​​of all robots in a multi-robot system. For state variables, Indicates the first The robot and the first The communication status of the robot, if Then the first The robot can receive the first... Information about the robot is required, but not vice versa. This is the communication interference vector. For communication interference The estimated value of a vector, its index. For the first in the corresponding vector One element, For a specific moment; For convergence parameters, For the output vector The Each element.

6. The distributed multi-robot competitive cooperative control method resistant to noise and communication interference according to claim 1, characterized in that, The execution process of the target tracking task is as follows: The optimal robot selection vector output by the noise-resistant nullified neurodynamics real-time solver. Each component in As the first The activation weight of each robot; According to the control strategy: Generate motion speed commands for each robot, where, For speed gain; The first The real-time location of the robot and the tracking target; The square of the Euclidean norm; The speed command is sent to the underlying controller of each robot, driving the selected robot to track the target, while causing the unselected robots to return to their initial positions to stand by.

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