Implementation method of multi-agent speed measurement-free control device based on STM32

Abstract

The present application relates to the technical field of control or regulation system, more particularly, to a kind of implementation method of multi-agent without speed measurement containing control device based on STM32.The method comprises: establishing the communication topology graph of multi-agent system;Determine the dynamics model of agent node and the containment control protocol without speed measurement;Build distributed hardware system platform based on STM32 microcontroller;Based on the above platform, build collaborative development environment with STM32CubeMX and Keil uVision5 as the core.The present application takes STM32 microcontroller as the core, directly calculates the control instruction of agent and drives execution, only needs its own and neighbor node position as control input, can efficiently and stably achieve containment control target, to solve the challenge of computing power, storage and bandwidth limitation faced when deploying distributed containment control on the hardware platform based on STM32 microcontroller.

Description

Technical Field

[0001] This invention relates to the technical field of control or regulation systems, specifically to an implementation method based on STM32 for multi-agent systems without speed measurement, including a control device. Background Technology

[0002] A Multi-Agent System (MAS) is a distributed computing architecture composed of multiple agents working collaboratively. In this architecture, each agent can independently perceive its surroundings, execute corresponding actions based on its own decisions, and interact and collaborate with other agents through a communication network to jointly complete specific tasks or achieve global goals. Inclusion control is a key issue in the field of MAS collaborative control. Its core objective is to design a distributed control protocol that allows the motion state of follower agents to enter and be maintained within the dynamic convex hull formed by the leader agent. This swarm intelligence decision-making technology system based on inclusion control provides a robust and reliable solution to swarm coordination problems in complex environments, showing broad application prospects in areas such as multi-robot collaborative operations, joint patrols of safe areas, collaborative avoidance of dangerous areas, and intelligent transportation collaborative control.

[0003] Existing verification platforms incorporating control algorithms are mostly limited to simulation testing driven by high-performance CPUs, exhibiting significant drawbacks such as high cost, large size, and high power consumption, hindering practical applications. For example, in CN115454646B, the computing platform relies on the interaction between the main CPU and N ZYNQ terminal CPUs, resulting in high training time and hardware costs, making it unsuitable for miniaturized application scenarios. Therefore, there is an urgent need to develop low-cost, miniaturized, and highly reliable control systems to achieve the practical deployment of multi-agent distributed control systems. STM32 microcontrollers, with their abundant peripheral resources, low cost, and low power consumption, can serve as the hardware platform for implementing such distributed cooperative control systems.

[0004] However, existing multi-agent control algorithms largely rely on the precise speed information of the agents, typically requiring speed sensors. This not only increases system hardware cost and complexity but is also difficult to implement on micro-embedded platforms. Furthermore, deploying distributed multi-agent control on STM32 microcontroller-based hardware platforms faces challenges related to computing power, storage, and bandwidth limitations. Therefore, designing a distributed multi-agent control implementation scheme suitable for STM32-level microcontrollers, while minimizing hardware costs without requiring speed measurement or sacrificing control accuracy, has become a key technical challenge for promoting the practical application of multi-agent systems. Summary of the Invention

[0005] This invention aims to overcome at least one of the defects of the prior art and provide an implementation scheme for a multi-agent inclusive control hardware system with STM32 microcontroller as the core and no speed measurement. With STM32 microcontroller as the core, the system directly calculates and drives the control instructions of the agents. It only needs the position of itself and its neighboring nodes as control inputs to achieve the inclusive control goal efficiently and stably. This solves the challenges of computing power, storage and bandwidth limitations faced when deploying distributed inclusive control on STM32 microcontroller-based hardware platforms.

[0006] The detailed technical solution of this invention is as follows:

[0007] A method for implementing multi-agent velocity measurement without control based on STM32, the method comprising:

[0008] Step 1: Establish the communication topology of the multi-agent system;

[0009] Step 2: Determine the dynamic model of the agent node and the control protocol without velocity measurement;

[0010] Step 3: Construct a distributed hardware system platform based on the STM32 microcontroller;

[0011] Step 4: Based on the above platform, build a collaborative development environment with STM32CubeMX and Keil uVision5 as the core.

[0012] According to a preferred embodiment of the present invention, step one is specifically as follows:

[0013] Establish a A multi-agent system consisting of several agents has a communication topology graph consisting of a directed graph. It means: Among them, Represents a set of agent nodes; Represents a communication link, if the edge , representing a node Received node Information; Let be an adjacency matrix, and its elements are defined as follows: ,when hour, ,otherwise , Adjacency matrix No. Line 1 Column elements represent join weights; nodes The neighbor set is defined as If a given agent node has an empty neighbor set (i.e., no neighbors), then that node is called a leader, and all leaders constitute the leader set. If a given agent node has a non-empty set of neighbors, i.e., it has neighbors, then that node is called a follower, and all followers constitute the follower set. .

[0014] According to a preferred embodiment of the present invention, the dynamic model of the agent node in step two is specifically as follows:

[0015] (1)

[0016] (2)

[0017] in: , , They represent the first An intelligent agent at time Position, speed, and control input; , They represent the first An intelligent agent at time Position and velocity; denote the first The initial positions and initial velocities of the agents are respectively... ;

[0018] When the agent is the leader Its control protocol is as follows:

[0019] (3)

[0020] in, Indicates the first An intelligent agent at time speed;

[0021] When the agent is a follower, that is At that time, its control protocol is:

[0022] (4)

[0023] in, For intelligent agents At any moment Location, For Neighbor Intelligent Agent At any moment Location, Adjacency matrix No. Line 1 Column elements, For robustness compensation gain coefficient, It is a sign function, for .

[0024] According to a preferred embodiment of the present invention, the distributed hardware system platform described in step three consists of multiple independently deployed intelligent agent nodes. Each intelligent agent node includes a main controller module, a wireless communication module, a visualization display module, and a power management module. The specific configurations of each module are as follows:

[0025] The main controller module is built using an STM32F103C8T6 microcontroller based on the ARM Cortex-M3 core, integrating Flash memory and SRAM. This module executes a distributed containment control algorithm without speed measurement, performing its iterative calculations in real time. It controls the Bluetooth communication module via the USART interface to enable data exchange between nodes. 2 The C-series bus drives the OLED display module to achieve real-time monitoring of its local operating status;

[0026] Wireless communication module: The HC-05 Bluetooth module is used to realize inter-node communication. The RXD pin of this module is connected to the TXD pin of the main controller module, and the TXD pin is connected to the RXD pin of the main controller module; it is responsible for bidirectional wireless data interaction with neighboring intelligent agent nodes.

[0027] Power management module: responsible for providing a stable and reliable DC power supply to the node system, with dual voltage output to meet the power supply needs of different modules;

[0028] Visualization display module: Employs an OLED display screen, which uses I... 2 The C communication interface connects to the main controller module to enable real-time visual monitoring of local node status information.

[0029] According to a preferred embodiment of the present invention, the STM32CubeMX described in step four is used for hardware abstraction layer design, and its functions include: microcontroller selection, system clock tree configuration, chip pin allocation, and peripheral initialization configuration.

[0030] According to a preferred embodiment of the present invention, the hardware abstraction layer design specifically includes: selecting an STM32F103C8T6 as the main controller chip and configuring the system clock to 72MHz; allocating a USART for Bluetooth communication, setting parameters to a baud rate of 9600 bps, 8 data bits, no parity, and 1 stop bit, which balances versatility and interoperability between different modules; configuring an I2C interface to drive an OLED display; enabling a timer TIM to generate an interrupt signal with a period of 200 ms as the main scheduling clock for system control tasks, and setting its interrupt priority to high. The selection of this control period comprehensively considers Bluetooth communication latency and processor performance, and can achieve a high control frequency while ensuring system stability.

[0031] According to a preferred embodiment of the present invention, the Keil uVision5 described in step four undertakes the application layer software development task, the main tasks of which include writing core logic code and performing real-time debugging, and using the ST-LINK download debugger to burn the compiled executable program to the target microcontroller.

[0032] According to a preferred embodiment of the present invention, the application layer software development task specifically includes: writing the underlying driver code for HC-05 and OLED based on the HAL library to realize the transmission and reception of Bluetooth data and screen content refresh; using a ring buffer structure to manage serial port received data to improve data throughput efficiency; and executing a specified speed-measuring distributed containment control protocol based on the real-time received neighbor node status information in each control cycle.

[0033] According to a preferred embodiment of the present invention, the application layer software developed using Keil uVision5 specifically implements the following functions:

[0034] Peripheral module control: drives the HC-05 Bluetooth wireless communication module and the OLED visual display module;

[0035] Data communication management: A ring buffer mechanism based on the USART protocol is implemented to efficiently manage the reception and storage of serial data;

[0036] Core algorithm implementation: Execute the distributed containment control protocol without speed measurement;

[0037] Real-time performance guarantee: STM32 timer interrupts are used to achieve precise 200ms control cycle scheduling, ensuring the real-time performance of the system.

[0038] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0039] This invention provides a control hardware architecture and implementation scheme for a multi-agent system with no speed measurement using an STM32 microcontroller. This scheme employs a speed measurement-free inclusivity control algorithm, using the STM32 microcontroller as the core processing and storage unit. By optimizing the algorithm structure and hardware resource scheduling, it eliminates the reliance on high-performance CPUs, significantly reducing system hardware complexity and overall cost. The system implementation method fully conforms to the resource constraints of embedded platforms, possessing advantages such as low cost, small size, light weight, strong scalability, and easy integration, while achieving higher resource utilization efficiency. This scheme facilitates the rapid integration of distributed inclusivity control functions into various terminal products. Attached Figure Description

[0040] Figure 1 This is a flowchart of the proposed solution for a distributed control device-free multi-agent system based on STM32;

[0041] Figure 2 This is the follower node of the present invention. A diagram illustrating the process of state updates;

[0042] Figure 3 This is a diagram of the overall hardware system platform architecture of the intelligent agent node of this invention;

[0043] Figure 4 This is the hardware schematic diagram of the STM32F103C8T6 main controller module of this invention;

[0044] Figure 5 This is the hardware schematic diagram of the wireless communication module HC-05 of the present invention;

[0045] Figure 6 This is a hardware schematic diagram of the OLED visualization display module of this invention;

[0046] Figure 7 This is a communication topology diagram of a multi-agent system provided in an embodiment of the present invention;

[0047] Figure 8 This is a two-dimensional trajectory diagram of the measured trajectory obtained by the hardware platform based on STM32 provided in this embodiment of the invention;

[0048] Figure 9 This is a two-dimensional trajectory diagram based on MATLAB numerical simulation provided in the embodiments of the present invention;

[0049] Figure 10 This is a three-dimensional trajectory diagram of the measured trajectory obtained by the hardware platform based on STM32 provided in this embodiment of the invention;

[0050] Figure 11 This is a 3D trajectory diagram based on MATLAB numerical simulation provided in an embodiment of the present invention. Detailed Implementation

[0051] The present disclosure will be further described below with reference to the accompanying drawings and embodiments.

[0052] Example 1

[0053] This invention uses an STM32 microcontroller as its core, directly calculating and executing control commands for the intelligent agent. It requires only the positions of the agent itself and its neighbors as control inputs to efficiently and stably achieve the control objective. The flowchart of this invention's multi-agent system with speed measurement-free distributed control device based on STM32 is shown below. Figure 1 As shown, the implementation scheme includes the following steps:

[0054] Step 1: Establish the communication topology of the multi-agent system:

[0055] like Figure 7As shown, consider a multi-agent system consisting of five nodes, whose communication topology is a directed graph. It means: Among them, Represents a set of agent nodes; Represents a communication link, if the edge , representing a node Received node Information; Let be an adjacency matrix, and its elements are defined as follows: ,when hour, ,otherwise ,in Adjacency matrix No. Line 1 The column elements represent the connection weights. Therefore, the adjacency matrix of this topological graph can be obtained. as follows:

[0056] (1)

[0057] node The neighbor set is defined as If a given agent node has an empty neighbor set (i.e., no neighbors), then that node is called a leader, and all leaders constitute the leader set. If a given agent node has a non-empty set of neighbors, i.e., it has neighbors, then that node is called a follower, and all followers constitute the follower set. Thus, it can be seen that Agent 1 and Agent 2 are the leaders, while Agent 3, Agent 4 and Agent 5 are the followers.

[0058] Step 2: Determine the dynamic model of the agent node and the control protocol without velocity measurement.

[0059] Consider the first An intelligent agent, Its dynamic model is described as follows:

[0060] (2)

[0061] (3)

[0062] in: , , They represent the first An intelligent agent at time Position, speed, and control input; , They represent the first An intelligent agent at time Position and velocity; denote the first The initial positions and initial velocities of the agents are respectively... .

[0063] When the agent is the leader Its control protocol is as follows:

[0064] (4)

[0065] in, Indicates the first An intelligent agent at time speed;

[0066] When the agent is a follower, that is At that time, its control protocol is:

[0067] (5)

[0068] in, For intelligent agents At any moment Location, For Neighbor Intelligent Agent At any moment Location, Adjacency matrix No. Line 1 Column elements, For robustness compensation gain coefficient, It is a sign function, for

[0069] In this step, the follower node of the multi-agent system The state update process is as follows Figure 2 The control logic block diagram is shown. Each agent obtains its own position in real time through a position sensor, interacts with neighboring nodes through a wireless communication module, and updates its own state according to the control protocol (5).

[0070] Based on the system architecture and dynamics model of this embodiment, the initial state and control parameters are determined as follows:

[0071] In two-dimensional space, the initial velocity and position states of Leader 1, Leader 2, Follower 1, Follower 2, and Follower 3 are set as follows:

[0072] Location: , ;

[0073] speed: , .

[0074] Experimental verification revealed that the robustness compensation coefficient was appropriate. At that time, the system exhibits good robustness.

[0075] Step 3: Construct a distributed hardware system platform based on the STM32 microcontroller:

[0076] In this step, the distributed hardware system platform constructed consists of multiple independently deployed agent nodes, aiming to provide the hardware foundation for implementing control algorithms in a multi-agent system based on an STM32 microcontroller. Each agent node uses an STM32F103C8T6 microcontroller as its main controller module and integrates a wireless communication module, a visualization display module, and a power management module. The overall hardware structure diagram of the system is shown below. Figure 3 As shown, the specific configurations of each module are as follows:

[0077] Main controller module: It adopts an STM32F103C8T6 microcontroller based on the ARM Cortex-M3 core. The microcontroller has a main frequency of 72MHz, integrates 64KB Flash memory and 20KB SRAM on the chip, and operates on a 3.3V DC power supply.

[0078] The main controller module is used to execute a specified distributed containment control algorithm without speed measurement, and complete its iterative calculation task in real time; it controls the HC-05 Bluetooth wireless communication module through the USART interface to realize data interaction between nodes; and it uses I... 2 The C-series bus drives a 0.96-inch OLED display module to achieve real-time monitoring of its local operating status. The hardware schematic of the main controller module can be found here. Figure 4 The pin assignments and functional descriptions of the STM32F103C8T6 minimum system used are detailed in Table 1:

[0079] Table 1 Input / Output Pin List of STM32F103C8T6 Minimum System Board

[0080]

[0081] The key pin assignments include:

[0082] USART1_RX (PA10) receives Bluetooth data, and USART1_TX (PA9) sends data;

[0083] I2C1_SCL (PB6) and I2C1_SDA (PB7) connect to the OLED display module;

[0084] SWCLK (PA14) and SWDIO (PA13) are used for the debug interface.

[0085] Wireless communication module: Utilizing an HC-05 Bluetooth module, the RXD pin connects to the TXD pin of the main microcontroller, and the TXD pin connects to the RXD pin of the main microcontroller, responsible for bidirectional wireless data exchange with neighboring intelligent agent nodes. Its hardware schematic is shown below. Figure 5 .

[0086] Power management module: Responsible for providing a stable and reliable DC power supply to the node system, with dual voltage outputs of 3.3V and 5V, adapting to the power supply requirements of the main control, communication and display modules.

[0087] Visualization module: Used for real-time visual monitoring of local node status information, employing a 0.96-inch OLED display with I... 2 The C interface connects to the main control module, displaying real-time node location coordinates, system status, and communication link quality. Its hardware schematic is shown below. Figure 6 .

[0088] Step 4: Build a collaborative development environment based on STM32CubeMX and Keil uVision5:

[0089] In this step, a collaborative development environment based on STM32CubeMX and Keil uVision5 is built on the aforementioned hardware platform.

[0090] STM32CubeMX is used for hardware abstraction layer design, and its functions include: microcontroller selection, system clock tree configuration, chip pin assignment, and peripheral initialization configuration. Specifically, it includes: selecting the STM32F103C8T6 as the main controller chip and configuring the system clock to 72 MHz; allocating USART for Bluetooth communication, setting parameters to a baud rate of 9600 bps, 8 data bits, no parity, and 1 stop bit; this baud rate balances versatility and interoperability between different modules; configuring I... 2 The C interface drives the OLED display; the timer TIM is enabled to generate an interrupt signal with a period of 200 ms as the main scheduling clock for system control tasks, and its interrupt priority is set to high. The selection of this control period takes into account Bluetooth communication latency and processor performance, and can achieve a high control frequency while ensuring system stability.

[0091] Keil uVision5 was responsible for application layer software development. Its main tasks included writing core logic code and performing real-time debugging, and using the ST-LINK download debugger to burn the compiled executable program to the target microcontroller. Specifically, this included: writing low-level driver code for the HC-05 and OLED based on the HAL library to implement Bluetooth data transmission and reception and screen content refresh; using a ring buffer structure to manage serial port data reception, improving data throughput efficiency; and executing a specified speed-measurement-free distributed containment control algorithm within each control cycle based on real-time received neighbor node status information.

[0092] The application layer software specifically implements the following key functions: (1) Peripheral module control: drives the HC-05 Bluetooth wireless communication module and the OLED visual display module; (2) Data communication management: implements a ring buffer mechanism based on the USART protocol to efficiently manage the reception and storage of serial data; (3) Core algorithm implementation: executes the distributed containment control protocol algorithm without speed measurement; (4) Real-time guarantee: uses STM32 timer interrupt to implement precise 200 ms control cycle scheduling to ensure the real-time performance of the system.

[0093] The system functions are implemented as follows:

[0094] Based on the hardware modules and the initial system configuration of the embodiment, the hardware platform test deployment is as follows: After system startup, Bluetooth pairing and serial port buffer initialization are completed; neighbor location data is received through the USART interface and stored in a circular buffer; control input is calculated based on the control protocol. Update the dynamic state; the state data is processed by I 2 The C-bus synchronizes with the OLED visualization display module, and the status at each time point is also synchronized. Data can be sent to a host computer via the USART serial port for data acquisition. The specific testing steps are as follows:

[0095] 1. System startup and initialization:

[0096] Start the power module to power on all functional modules in a coordinated manner; based on the communication topology constructed in step 1, pre-configure the HC-05 Bluetooth wireless communication module deployed on each STM32 microcontroller in master or slave mode using AT commands to ensure that the network logical topology is consistent with the physical connection; complete the wireless pairing of the HC-05 Bluetooth wireless communication module.

[0097] 2. Wireless data reception and buffering:

[0098] The STM32F103C8T6 microcontrollers deployed on each agent node wirelessly transmit and receive data via a wireless communication module; and efficiently buffer the received serial data using a USART interface and a ring buffer managed by on-chip SRAM.

[0099] 3. Data processing and status updates:

[0100] The application layer program of the STM32F103C8T6 microcontroller extracts and parses data packets from the serial port receive buffer; executes a distributed control algorithm to calculate the control input; updates its own state in real time according to the dynamic model; and writes the updated state into the state register for storage.

[0101] 4. Visualized output of status information:

[0102] The master node communicates via I 2 The C-series bus sends the updated status data to the OLED visualization display module; the OLED module refreshes the display interface, presents the current status information, and sends the node status via the USART serial port. The data is then sent to the host computer for collection.

[0103] 5. Verification:

[0104] Repeat steps 2 to 4 until sufficient implementation data is obtained. After data collection is completed, extract the historical state data recorded by each node; use visualization tools to process the historical data and plot the motion trajectory curves of the multi-agent system nodes measured on the hardware platform; compare and analyze the measured trajectory with the numerical simulation trajectory of the multi-agent system in MATLAB.

[0105] Figure 8 and Figure 9 The figures show the measured trajectories of each agent in a multi-agent system in two dimensions, based on the STM32 hardware platform, and the numerical simulation trajectories based on MATLAB. Figure 10 and Figure 11 The figures show the measured trajectories of each agent in the multi-agent system in three dimensions, based on the STM32 hardware platform, and the numerical simulation trajectories based on MATLAB. Comparative analysis shows that, under the constructed STM32 hardware platform, the convergence of the multi-agent system is similar to that of the numerical simulation, demonstrating not only efficient convergence but also excellent robustness, effectively handling various disturbances and uncertainties.

[0106] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the technical solutions of the present invention, and are not intended to limit the specific implementation of the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the claims of the present invention should be included within the protection scope of the claims of the present invention.

Claims

1. A method for implementing multi-agent systems without velocity measurement, including a control device, based on STM32, characterized in that: The method includes: Step 1: Establish the communication topology of the multi-agent system, as follows: Establish a A multi-agent system consisting of several agents has a communication topology graph consisting of a directed graph. express; Directed graph middle, Represents a set of agent nodes; Represents a communication link, if the edge , representing a node Received node Information; Let be an adjacency matrix, whose elements are defined as: ,when hour, ,otherwise , Adjacency matrix No. Line 1 Column elements represent join weights; node The neighbor set is defined as If a given agent node has an empty neighbor set (i.e., no neighbors), then that node is called a leader, and all leaders constitute the leader set. If a given agent node has a non-empty set of neighbors, i.e., it has neighbors, then that node is called a follower, and all followers constitute the follower set. ; Step 2: Determine the dynamic model of the agent node and the control protocol without velocity measurement; The dynamic model of the agent node is as follows: （1） （2） in: , , They represent the first An intelligent agent at time Position, speed, and control input; , They represent the first An intelligent agent at time Position and velocity; denote the first The initial positions and initial velocities of the agents are respectively... ; When the agent is the leader, that is At that time, its control protocol is: （3） in, Indicates the first An intelligent agent at time speed; When the agent is a follower, that is At that time, its control protocol is: （4） in, For intelligent agents At any moment Location, For Neighbor Intelligent Agent At any moment Location, Adjacency matrix No. Line 1 Column elements, For robust compensation gain coefficients, It is a sign function, for ; Step 3: Construct a distributed hardware system platform based on the STM32 microcontroller; The distributed hardware system platform consists of multiple independently deployed intelligent agent nodes. Each intelligent agent node includes a main controller module, a wireless communication module, a visualization display module, and a power management module. The specific configurations of each module are as follows: The main controller module is built using an STM32F103C8T6 microcontroller based on the ARM Cortex-M3 core, integrating Flash memory and SRAM. This module executes a distributed containment control algorithm without speed measurement, performing its iterative calculations in real time. It controls the Bluetooth communication module via the USART interface to enable data exchange between nodes. 2 The C-series bus drives the OLED display module to achieve real-time monitoring of its local operating status; Wireless communication module: The HC-05 Bluetooth module is used to realize inter-node communication. The RXD pin of this module is connected to the TXD pin of the main controller module, and the TXD pin is connected to the RXD pin of the main controller module; it is responsible for bidirectional wireless data interaction with neighboring intelligent agent nodes. Power management module: responsible for providing a stable and reliable DC power supply to the node system, with dual voltage output to meet the power supply needs of different modules; Visualization display module: Employs an OLED display screen, which uses I... 2 The C communication interface is connected to the main controller module to realize real-time visual monitoring of local node status information; Step 4: Based on the above platform, build a collaborative development environment with STM32CubeMX and Keil uVision5 as the core.

2. The implementation method of multi-agent speed measurement without control device based on STM32 according to claim 1, characterized in that, The STM32CubeMX mentioned in step four is used for hardware abstraction layer design. Its functions include: microcontroller selection, system clock tree configuration, chip pin allocation, and peripheral initialization configuration.

3. The implementation method of multi-agent speed measurement without control device based on STM32 according to claim 2, characterized in that, The hardware abstraction layer design specifically includes: selecting an STM32F103C8T6 as the main controller chip and configuring the system clock to 72MHz; allocating USART for Bluetooth communication, setting parameters to a baud rate of 9600 bps, 8 data bits, no parity, and 1 stop bit, this baud rate balances versatility and interoperability between different modules; configuring I... 2 The C communication interface drives the OLED display; the timer TIM is enabled to generate an interrupt signal with a certain period of time, which serves as the main scheduling clock for system control tasks, and its interrupt priority is set to high.

4. The implementation method of multi-agent speed measurement without control device based on STM32 according to claim 2, characterized in that, The Keil uVision5 is responsible for application layer software development tasks, including writing core logic code and performing real-time debugging, and using the ST-LINK download debugger to burn the compiled executable program to the target microcontroller.

5. The implementation method of multi-agent speed measurement without control device based on STM32 according to claim 4, characterized in that, The specific application layer software development tasks include: writing the underlying driver code for HC-05 and OLED based on the HAL library to realize Bluetooth data transmission and reception and screen content refresh; using a ring buffer structure to manage serial port received data to improve data throughput efficiency; and executing the specified speed-free distributed containment control protocol based on the real-time received neighbor node status information in each control cycle.

6. The implementation method of multi-agent speed measurement without control device based on STM32 according to claim 5, characterized in that, The application layer software developed with Keil uVision5 specifically implements the following functions: Peripheral module control: drives the HC-05 Bluetooth wireless communication module and the OLED visual display module; Data communication management: A ring buffer mechanism based on the USART protocol is implemented to manage the reception and storage of serial data; Core algorithm implementation: Execute the distributed containment control protocol without speed measurement; Real-time performance guarantee: STM32 timer interrupts are used to achieve precise 200ms control cycle scheduling, ensuring the real-time performance of the system.

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