Distributed command filtering fuzzy control method of multi-robot system

Abstract

The invention relates to the technical field of multi-robot systems, and discloses a distributed command filtering fuzzy control method for a multi-robot system, which comprises the following steps that: each robot reads own hardware configuration and software parameters, ensures that all sensors and actuators work normally, and sends a command to the robot after initialization is completed; the robot sends a state report to a central controller or an adjacent robot through a distributed communication protocol; loading a predefined fuzzy rule base, and setting an initial fuzzy rule; environmental data are collected in real time through a sensor, and a fuzzy rule is dynamically adjusted according to real-time data by using an online learning mechanism. According to the distributed command filtering fuzzy control method of the multi-robot system, by reading hardware configuration and software parameters, comprehensive understanding of robot states is achieved, normal work of all sensors and actuators is ensured, the system can ensure that each robot is in the optimal working state when started, and the system is suitable for being used in a large scale. And the reliability and the stability of the system are improved.

Description

Technical Field

[0001] The invention relates to the technical field of multi-robot systems, and in particular to a distributed command filtering fuzzy control method for multi-robot systems. Background Art

[0002] A multi-robot system (MRS) is a system composed of multiple autonomous robots that can work together to complete complex tasks. Multi-robot systems are widely used in many fields. However, existing multi-robot systems often lack a comprehensive self-checking and status reporting mechanism during the initialization phase, which causes some sensors or actuators to work without being detected, affecting the stability and reliability of the system. Most systems use a fixed fuzzy rule base and are unable to dynamically adjust the control strategy according to environmental changes, resulting in poor adaptability in complex environments. At the same time, existing multi-robot systems have deficiencies in task allocation and collaborative control, resulting in unreasonable task allocation, waste of resources, and conflicts between robots. Summary of the invention

[0003] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] The invention discloses a distributed command filtering fuzzy control method for a multi-robot system, comprising: each robot reads its own hardware configuration and software parameters to ensure that all sensors and actuators work normally; after initialization, the robot sends a status report to a central controller or a neighboring robot through a distributed communication protocol; loads a predefined fuzzy rule library and sets initial fuzzy rules; collects environmental data in real time through sensors, and uses an online learning mechanism to dynamically adjust fuzzy rules according to real-time data; distributes tasks according to task requirements and the capabilities of each robot, and uses a distributed optimization method to adjust the action strategy of each robot in real time; generates a control signal according to the adjusted fuzzy rules; smoothes the control signal through a command filter, and the robot executes the filtered control instruction.

[0006] As a further solution of the present invention: each robot reads its own hardware configuration and software parameters to ensure that all sensors and actuators work normally. After initialization, the robot sends a status report to the central controller or the neighboring robot through a distributed communication protocol. The specific steps are: when each robot is started, it first reads its own hardware configuration file, including sensor type, quantity, installation position, actuator type, quantity and installation position information; the robot reads the preset software parameters, including initial position, initial speed, communication frequency and fuzzy rule library path; the robot checks the working status of all sensors and actuators through a self-check program, and the expression is:

[0007]

[0008] Among them, S i (t) is the state score of the ith robot at time t, n is the total number of sensors and actuators, and w j is the weight of the jth sensor or actuator, H ij (t) is the status score of the jth sensor or actuator of the ith robot at time t, where 1 indicates normal operation.

[0009] As a further solution of the present invention: the loading of the predefined fuzzy rule base and setting of the initial fuzzy rules are specifically carried out as follows: when the robot is started, the predefined fuzzy rule base is loaded from the local storage, and a set of initial fuzzy rules for the current task is selected according to a preset configuration file; parameters in the selected fuzzy rules, such as the shape and position of the membership function, are initialized; the scores of the initial fuzzy rules are calculated; and the scores S are calculated according to the calculated scores. i (t), select the fuzzy rule with the highest score as the initial fuzzy rule.

[0010] As a further solution of the present invention: the environmental data is collected in real time by sensors, and the fuzzy rules are adjusted dynamically according to the real-time data by using an online learning mechanism. The specific steps are: each robot collects environmental data in real time through sensors, including obstacle positions, target distances, and states of other robots; the collected data is preprocessed, including data cleaning, filtering, and normalization; an online learning mechanism adaptive neural fuzzy inference system is introduced to dynamically adjust the fuzzy rules according to the real-time data. The expression is:

[0011]

[0012] Among them, Δr j (t) is the weight adjustment of the jth rule at time t, η is the learning rate, which indicates the speed of learning, E(t) is the error function, is the partial derivative of the error function with respect to the weight of the jth rule.

[0013] As a further solution of the present invention: tasks are allocated according to the task requirements and the capabilities of each robot, and the action strategy of each robot is adjusted in real time using a distributed optimization method. The specific steps are: a central controller or a distributed algorithm generates a task allocation table according to the task requirements and the capabilities of each robot; using a distributed gradient descent method, each robot calculates the gradient and adjusts the action strategy according to the current task progress and environmental data.

[0014] As a further solution of the present invention: the control signal is generated according to the adjusted fuzzy rules, and the specific steps include: each robot collects environmental data in real time through sensors, including obstacle positions, target distances, and the status of other robots; fuzzifies the collected environmental data and converts them into fuzzy sets; matches the fuzzy rules corresponding to the current environmental data according to the adjusted fuzzy rules; generates a fuzzy control signal according to the matched fuzzy rules through a fuzzy reasoning method; defuzzifies the fuzzy control signal and converts it into a specific control signal; and calculates the control signal of the robot.

[0015] As a further solution of the present invention: the control signal is smoothed by the command filter, and the robot executes the filtered control command. The specific steps are: each robot uses the control signal generated according to the adjusted fuzzy rule as input; introduces the command filter, the first-order low-pass filter and the smoothed control signal; inputs the generated control signal into the filter, and calculates the filtered output signal, which is expressed as:

[0016]

[0017] Among them, y(t) is the output signal after filtering, u(t) is the control signal without filtering, is the time derivative of the filtered output signal, and α is the filter coefficient.

[0018] As a further solution of the present invention: the filtered control signal is executed by an actuator to control the movement of the robot, and the robot monitors the status of the actuator in real time.

[0019] The present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of a distributed command filtering fuzzy control method for a multi-robot system as described in the first aspect of the present invention is implemented.

[0020] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of a distributed command filtering fuzzy control method for a multi-robot system as described in the first aspect of the present invention is implemented.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] By reading the hardware configuration and software parameters, a comprehensive understanding of the robot status is achieved, ensuring the normal operation of all sensors and actuators. The system can ensure that each robot is in the best working state at startup, improving the reliability and stability of the system. In addition, by sending status reports to the central controller or neighboring robots through the distributed communication protocol, information sharing and collaboration are achieved, enhancing the overall coordination ability of the system. By loading the predefined fuzzy rule library and setting the initial fuzzy rules, the preliminary configuration of the robot control strategy is achieved, ensuring that the system has a certain control capability at startup. The system can quickly enter the working state at startup, reducing the initialization time and improving the response speed of the system. At the same time, by selecting the fuzzy rule with the highest score, the effectiveness of the initial control strategy is ensured, laying the foundation for subsequent dynamic adjustments. By allocating tasks according to task requirements and the capabilities of each robot, and using distributed optimization methods to adjust the action strategies of each robot in real time, efficient task allocation and collaborative control are achieved, improving the overall performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 Schematic diagram of the flow of the distributed command filtering fuzzy control method for multi-robot systems. DETAILED DESCRIPTION

[0024] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and understandable, the specific implementation modes of the present invention are described in detail below in conjunction with the accompanying drawings.

[0025] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0026] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0027] Example

[0028] See also Figure 1 , which is the first embodiment of the present invention, provides a distributed command filtering fuzzy control method for a multi-robot system, comprising:

[0029] S1. Each robot reads its own hardware configuration and software parameters to ensure that all sensors and actuators are working properly. After initialization, the robot sends a status report to the central controller or neighboring robots through a distributed communication protocol.

[0030] Specifically, when each robot starts, it first reads its own hardware configuration file, including the sensor type, quantity, installation location, actuator type, quantity and installation location information. For example, the configuration file of robot A shows that it is equipped with 1 lidar, 2 cameras, 4 infrared sensors, 2 motors and 2 servos. The robot reads the preset software parameters, including the initial position, initial speed, communication frequency and fuzzy rule base path. For example, the initial position of robot A is (0,0), the initial speed is 0.5m / s, and the communication frequency is 10Hz. The robot checks the working status of all sensors and actuators through the self-test program to ensure that they are working properly.

[0031] It should be noted that by reading the hardware configuration and software parameters, it is ensured that each robot can accurately understand its own hardware and software configuration when it starts, so that a comprehensive self-check can be performed during the initialization process to ensure that all sensors and actuators are in normal working condition, which helps to discover potential problems in advance and avoid mission failures due to hardware failures. By sending status reports to the central controller or neighboring robots through distributed communication protocols, information sharing and collaboration are achieved, enhancing the overall coordination ability of the system.

[0032] S2, load the predefined fuzzy rule base and set the initial fuzzy rules;

[0033] Specifically, when the robot starts, it loads a predefined fuzzy rule library from local storage, selects a set of initial fuzzy rules for the current task according to the preset configuration file, such as robot A selects the "carrying task" rule group, initializes the parameters in the selected fuzzy rules, the shape and position of the membership function, such as the membership function is a triangle with vertices of 0.5 meters, 1 meter, and 1.5 meters, and base widths of 1 meter, 1 meter, and 1 meter, calculates the score of the initial fuzzy rule, and selects the fuzzy rule with the highest score as the initial fuzzy rule. For example, after calculation, robot A selects the "obstacle avoidance rule" with the highest score as the initial fuzzy rule.

[0034] It should be noted that by loading the predefined fuzzy rule library and setting the initial fuzzy rules, it is ensured that the robot can immediately use the predefined fuzzy rule library when it starts up, select a set of initial fuzzy rules according to the current task requirements, and initialize the parameters in the fuzzy rules, such as the shape and position of the membership function, to ensure the applicability and effectiveness of the fuzzy rules. The scores of the initial fuzzy rules are calculated, and the fuzzy rules with the highest scores are selected as the initial fuzzy rules, which further optimizes the initial control strategy, so that the system can quickly enter the working state at startup, reduce the initialization time, and improve the response speed of the system.

[0035] S3, collect environmental data in real time through sensors, and use online learning mechanism to dynamically adjust fuzzy rules according to real-time data;

[0036] Specifically, each robot collects environmental data in real time through sensors, including obstacle positions, target distances, and the status of other robots. For example, robot A measures the distance of the obstacle in front as 1.2 meters through lidar, and identifies the position of the target object as (5,5) meters through the camera. The collected data is preprocessed, including data cleaning, filtering, and normalization. For example, a Kalman filter is used to filter the lidar data to remove noise; a normalization method is used to convert the data to a range of 0 to 1; an online learning mechanism adaptive neural fuzzy inference system is introduced to dynamically adjust the fuzzy rules according to real-time data.

[0037] It should be noted that by collecting environmental data in real time and using an online learning mechanism to dynamically adjust fuzzy rules, the robot can perceive environmental changes in real time, including obstacle positions, target distances, and the status of other robots. The quality and accuracy of the data are ensured by preprocessing the collected data, including data cleaning, filtering, and normalization. An online learning mechanism-based adaptive neuro-fuzzy inference system is introduced to dynamically adjust fuzzy rules based on real-time data, so that the system can optimize the control strategy in real time according to environmental changes.

[0038] S4. Assign tasks according to task requirements and the capabilities of each robot, and use distributed optimization methods to adjust the action strategies of each robot in real time;

[0039] Specifically, the central controller or distributed algorithm generates a task allocation table based on the task requirements and the capabilities of each robot. For example, task 1 is assigned to robot A and task 2 is assigned to robot B. Using the distributed gradient descent method, each robot calculates the gradient and adjusts the action strategy based on the current task progress and environmental data. For example, when robot A is executing task 1, it finds an obstacle ahead and adjusts the path through the distributed gradient descent method to avoid the obstacle.

[0040] It should be noted that by allocating tasks according to task requirements and the capabilities of each robot, and using distributed optimization methods to adjust the action strategies of each robot in real time, efficient task allocation and collaborative control are achieved, and the overall performance of the system is improved. The central controller or distributed algorithm can generate a task allocation table based on task requirements and the capabilities of each robot to ensure that each robot can undertake the task that best suits it. Using the distributed gradient descent method, each robot calculates the gradient and adjusts the action strategy based on the current task progress and environmental data, thereby achieving dynamic adjustment and optimization of tasks.

[0041] S5. Generate a control signal according to the adjusted fuzzy rule;

[0042] Specifically, each robot collects environmental data in real time through sensors, including obstacle positions, target distances, and the states of other robots. For example, robot A measures the distance of the obstacle in front to be 1.2 meters and the position of the target object to be (5,5) meters. The collected environmental data is fuzzified and converted into fuzzy sets. For example, the distance 1.2 meters is converted into the fuzzy set "medium". According to the adjusted fuzzy rules, the fuzzy rules corresponding to the current environmental data are matched. For example, the matched fuzzy rules are "if the distance to the obstacle is 'medium', then the steering angle is 'small' and the speed is 'fast'". Through the fuzzy reasoning method, a fuzzy control signal is generated according to the matched fuzzy rules. For example, the generated fuzzy control signal is a steering angle of "small" and a speed of "fast". The fuzzy control signal is defuzzified and converted into a specific control signal.

[0043] It should be noted that by generating control signals according to the adjusted fuzzy rules, it is ensured that the robot can generate accurate control instructions according to the current environmental data and task requirements, so that the robot can generate fuzzy control signals according to real-time environmental data and adjusted fuzzy rules, and convert them into specific control signals through defuzzification processing, thereby improving the accuracy and adaptability of the control signals, enabling the robot to better cope with complex dynamic environments and improving the success rate and efficiency of task completion.

[0044] S6, the control signal is smoothed by the command filter, and the robot executes the filtered control command;

[0045] Specifically, each robot takes the control signal generated according to the adjusted fuzzy rules as input, for example, the control signal generated by robot A is a steering angle of 0.2 and a speed of 0.8. A command filter, a first-order low-pass filter and a smooth control signal are introduced, the generated control signal is input into the filter, and the filtered output signal is calculated, for example, the steering angle after filtering is 0.15 and the speed is 0.7. The filtered control signal is executed by the actuator to control the movement of the robot. The robot monitors the status of the actuator in real time to ensure the correct execution of the control command.

[0046] It should be noted that the control signal is smoothed by the command filter, which ensures the smoothness and stability of the control signal and avoids mechanical shock and control instability caused by sudden changes in the control signal. The robot can generate a control signal based on the adjusted fuzzy rule and generate a smooth control signal through smoothing by the command filter. The filtered control signal is executed by the actuator to control the movement of the robot. The robot monitors the status of the actuator in real time to ensure the correct execution of the control instructions, thereby improving the control accuracy and stability of the system, enabling the robot to perform tasks better and improving the success rate and efficiency of task completion.

[0047] This embodiment also provides a computer device, which is suitable for the distributed command filtering fuzzy control method of a multi-robot system, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the distributed command filtering fuzzy control method of a multi-robot system proposed in the above embodiment.

[0048] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0049] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the distributed command filtering fuzzy control method for a multi-robot system proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0050] Table: Multi-robot system performance comparison

[0051]

[0052] It can be seen from the above table data that the distributed command filtering fuzzy control method of the multi-robot system of the present invention shows significant advantages and innovations in many aspects:

[0053] The initial state scores of robots A, B, and C of the present invention are 0.95, 0.92, and 0.94, respectively, while the initial state scores of robots A, B, and C of the prior art are 0.85, 0.80, and 0.83, respectively, indicating that the present invention can more accurately read and check the hardware configuration and software parameters when the robot is started, ensuring the normal operation of all sensors and actuators and improving the reliability and stability of the system.

[0054] The task completion time of robots A, B and C of the present invention is 120 seconds, 130 seconds and 125 seconds respectively, while the task completion time of robots A, B and C of the prior art is 150 seconds, 160 seconds and 155 seconds respectively, indicating that the present invention significantly shortens the task completion time and improves the efficiency of the system by optimizing task allocation and action strategies.

[0055] The path lengths of robots A, B, and C of the present invention are 100 meters, 110 meters, and 105 meters, respectively, while the path lengths of robots A, B, and C of the prior art are 120 meters, 130 meters, and 125 meters, respectively, indicating that the present invention effectively reduces the path length, energy consumption, and time cost by dynamically adjusting fuzzy rules and optimizing path planning.

[0056] The obstacle avoidance success rates of robots A, B and C of the present invention are 98%, 97% and 96% respectively, while those of robots A, B and C of the prior art are 90%, 88% and 89% respectively, indicating that the present invention significantly improves the obstacle avoidance success rate and enhances the safety and reliability of the system by real-time collection of environmental data and dynamic adjustment of fuzzy rules.

[0057] The communication delays of robots A, B, and C of the present invention are 50 milliseconds, 55 milliseconds, and 52 milliseconds, respectively, while the communication delays of robots A, B, and C of the prior art are 70 milliseconds, 75 milliseconds, and 72 milliseconds, respectively, indicating that the present invention significantly reduces the communication delay and improves the real-time performance and response speed of the system through an efficient distributed communication protocol.

[0058] In summary, by reading the hardware configuration and software parameters, a comprehensive understanding of the robot status is achieved, ensuring the normal operation of all sensors and actuators. The system can ensure that each robot is in the best working state at startup, improving the reliability and stability of the system. In addition, by sending status reports to the central controller or neighboring robots through the distributed communication protocol, information sharing and collaboration are realized, enhancing the overall coordination ability of the system. By loading the predefined fuzzy rule library and setting the initial fuzzy rules, the preliminary configuration of the robot control strategy is realized, ensuring that the system has a certain control capability at startup. The system can quickly enter the working state at startup, reducing the initialization time and improving the response speed of the system. At the same time, by selecting the fuzzy rule with the highest score, the effectiveness of the initial control strategy is ensured, laying the foundation for subsequent dynamic adjustments. By allocating tasks according to task requirements and the capabilities of each robot, and using distributed optimization methods to adjust the action strategies of each robot in real time, efficient task allocation and collaborative control are achieved, improving the overall performance of the system.

[0059] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A distributed command filtering fuzzy control method for a multi-robot system, characterized in that: include, S1. Each robot reads its own hardware configuration and software parameters to ensure that all sensors and actuators are working properly. After initialization, the robot sends a status report to the central controller or neighboring robots through a distributed communication protocol. S2, load the predefined fuzzy rule base and set the initial fuzzy rules; S3, collect environmental data in real time through sensors, and use online learning mechanism to dynamically adjust fuzzy rules according to real-time data; S4. Assign tasks according to task requirements and the capabilities of each robot, and use distributed optimization methods to adjust the action strategies of each robot in real time; S5. Generate a control signal according to the adjusted fuzzy rule; S6. The control signal is smoothed by the command filter, and the robot executes the filtered control command.

2. The distributed command filtering fuzzy control method for a multi-robot system according to claim 1, characterized in that: In step S1, each robot reads its own hardware configuration and software parameters to ensure that all sensors and actuators work properly. After initialization, the robot sends a status report to the central controller or neighboring robots through a distributed communication protocol. The specific steps are: S1.

1. When each robot starts, it first reads its own hardware configuration file, including sensor type, quantity, installation location, actuator type, quantity and installation location information; S1.2, the robot reads the preset software parameters, including initial position, initial speed, communication frequency and fuzzy rule base path; S1.

3. The robot checks the working status of all sensors and actuators through the self-check program. The expression is: Among them, S i (t) is the state score of the ith robot at time t, n is the total number of sensors and actuators, and w j is the weight of the jth sensor or actuator, H ij (t) is the status score of the jth sensor or actuator of the ith robot at time t, where 1 indicates normal operation.

3. The distributed command filtering fuzzy control method for a multi-robot system according to claim 1, characterized in that: In step S2, a predefined fuzzy rule base is loaded and initial fuzzy rules are set. The specific steps are: S2.1, when the robot starts, it loads the predefined fuzzy rule library from the local storage and selects a set of initial fuzzy rules for the current task according to the preset configuration file; S2.2, initializing the parameters in the selected fuzzy rules, such as the shape and position of the membership function; S2.3, calculating the scores of the initial fuzzy rules; S2.4, the calculated score S i (t), select the fuzzy rule with the highest score as the initial fuzzy rule.

4. The distributed command filtering fuzzy control method for a multi-robot system according to claim 1, characterized in that: In step S3, environmental data is collected in real time by sensors, and the fuzzy rules are dynamically adjusted according to the real-time data using an online learning mechanism. The specific steps are: S3.

1. Each robot collects environmental data in real time through sensors, including obstacle locations, target distances, and the status of other robots; S3.2, preprocessing the collected data, including data cleaning, filtering and normalization; S3.3, introduce the online learning mechanism adaptive neural fuzzy inference system, dynamically adjust the fuzzy rules according to real-time data, the expression is: Among them, Δr j (t) is the weight adjustment of the jth rule at time t, η is the learning rate, which indicates the speed of learning, E(t) is the error function, is the partial derivative of the error function with respect to the weight of the jth rule.

5. The distributed command filtering fuzzy control method for a multi-robot system according to claim 1, characterized in that: In step S4, tasks are allocated according to task requirements and the capabilities of each robot, and the action strategy of each robot is adjusted in real time using a distributed optimization method. The specific steps are: S4.

1. The central controller or distributed algorithm generates a task allocation table based on task requirements and the capabilities of each robot; S4.

2. Using the distributed gradient descent method, each robot calculates the gradient and adjusts the action strategy based on the current task progress and environmental data.

6. The distributed command filtering fuzzy control method for a multi-robot system according to claim 1, characterized in that: In step S5, a control signal is generated according to the adjusted fuzzy rule, and the specific steps include: S5.

1. Each robot collects environmental data in real time through sensors, including obstacle locations, target distances, and the status of other robots; S5.2, fuzzifying the collected environmental data and converting them into fuzzy sets; S5.3, matching the fuzzy rules corresponding to the current environment data according to the adjusted fuzzy rules; S5.4, generating a fuzzy control signal according to the matching fuzzy rules through a fuzzy reasoning method; S5.5, defuzzifying the fuzzy control signal and converting it into a specific control signal; S5.

6. Calculate the control signal of the robot.

7. The distributed command filtering fuzzy control method for a multi-robot system according to claim 1, characterized in that: In step S6, the control signal is smoothed by the command filter, and the robot executes the filtered control command. The specific steps are: S6.1, each robot takes as input the control signal generated according to the adjusted fuzzy rules; S6.2, introduce command filter, first-order low-pass filter and smooth control signal; S6.3, input the generated control signal into the filter, and calculate the output signal after filtering. The expression is: Among them, y(t) is the output signal after filtering, u(t) is the control signal without filtering, is the time derivative of the filtered output signal, and α is the filter coefficient.

8. The distributed command filtering fuzzy control method for a multi-robot system according to claim 7, characterized in that: The filtered control signal is executed by the actuator to control the movement of the robot, and the robot monitors the status of the actuator in real time.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the distributed command filtering fuzzy control method for a multi-robot system according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the distributed command filtering fuzzy control method for a multi-robot system according to any one of claims 1 to 8 are implemented.

Images