A method and system for controlling a tracked unmanned platform formation based on adaptive state updating and autonomous formation decision

Abstract

The application discloses a kind of based on adaptive state update and autonomous formation decision track type unmanned platform formation control method and system.The method is by constructing the state controller of adaptive update frequency, according to platform motion state and obstacle distance dynamic adjustment communication frequency;Combining artificial potential field and formation knowledge base, according to obstacle distribution and platform speed autonomous selection formation mode;Adopt master-slave control architecture, master platform solves global target position, and predicts slave platform state through consistency formation controller;Introduce time-varying formation motion controller, time compensation is carried out to control quantity.System contains perception module, state controller, autonomous decision module etc., through laser radar and camera real-time monitoring environment, combined with dynamic rectangular area obstacle determination mechanism, formation obstacle avoidance behavior is effectively coordinated.The application significantly improves the robustness of formation control in complex environment, reduces communication resource consumption.

Description

Technical Field

[0001] This invention belongs to the field of control algorithm technology for tracked unmanned platforms, and in particular relates to a formation control method and system for tracked unmanned platforms based on adaptive state updates and autonomous formation decision-making. Background Technology

[0002] With the rapid development of artificial intelligence and autonomous driving technologies, tracked unmanned platform swarms have a wide range of applications in industrial production, disaster relief, material transportation, and logistical support. Research on swarm algorithms for tracked unmanned platforms continues to rise, as single-platform execution of complex tasks is limited and cannot handle large volumes of goods or specific cluster tasks. In contrast, multi-platform systems, with their higher fault tolerance and collaborative capabilities, excel in terrain reconnaissance and material transportation. In practical applications, tracked unmanned platforms can adjust their formation and speed in real time according to terrain, ensuring stable passage in complex road conditions. This requires the unmanned vehicles not only to maintain a specific formation but also to autonomously avoid obstacles and adjust their strategies based on the current environment to ensure mission success. However, current unmanned platform swarm algorithms typically use fixed formations, requiring manual modification in different scenarios. Furthermore, the update frequency of unmanned platform states is usually fixed during swarm planning, leading to wasted computing power in open environments, while in complex multi-obstacle scenarios, the low update frequency makes it difficult for the unmanned platform system to perform real-time obstacle avoidance. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for formation control of tracked unmanned platforms based on adaptive state updates and autonomous formation decision-making, so as to achieve efficient frequency updates and autonomous formation selection and real-time obstacle avoidance in complex multi-obstacle scenarios.

[0004] To achieve the objectives of this invention, on the one hand, this invention provides a formation control method for tracked unmanned platforms based on adaptive state updates and autonomous formation decision-making, comprising the following steps:

[0005] S1. Based on the motion state and pose information data of each unmanned platform, construct an adaptive update frequency state controller. The state controller adaptively adjusts the sampling and communication frequency of the data according to the platform itself and the surrounding environment, and updates the motion state and pose information data in real time.

[0006] S2. Compile a formation knowledge base and establish an artificial potential field to enable unmanned platform formations to autonomously select target formations based on the distribution of obstacles and the motion state of the unmanned platforms during operation.

[0007] S3. Based on the selected target formation, updated motion state and pose information data, calculate the target position of each unmanned platform in the target formation, construct a consistent formation controller, and obtain the current position and velocity estimate of each unmanned platform.

[0008] S4. Based on the target position, the current position, and the speed estimate, the master control platform determines the motion control amount of each slave control platform while remotely controlling the movement, so that it moves towards the target position. At the same time, time compensation is performed based on the motion control amount to obtain the final motion control amount of each slave control platform, guiding the unmanned platform formation to achieve cooperative obstacle avoidance.

[0009] On the other hand, the present invention also provides a system for a formation control method for tracked unmanned platforms based on adaptive state updates and autonomous formation decisions, comprising the following modules:

[0010] The perception module, including LiDAR and cameras, is used to detect obstacles in the environment. When an obstacle hinders the movement of the unmanned platform, the formation needs to be changed accordingly to bypass the obstacle and avoid collision. Specifically, a rectangle is generated by extending a distance to the left and right of the vehicle's current position and the target position set in the formation as the center line. If an obstacle is located within the rectangular area, it is determined that the obstacle will hinder the vehicle from moving according to the current formation; otherwise, it is determined that the obstacle does not hinder the vehicle's movement.

[0011] The state controller module is used to adjust the communication frequency between unmanned platforms in real time, which reduces the transmission pressure on the communication system while ensuring that the state update frequency between platforms can meet the needs of obstacle avoidance and formation.

[0012] The autonomous decision-making formation module is used to autonomously select the appropriate formation type, size, and spacing based on the current movement speed of the unmanned platform formation system and the distribution of surrounding obstacles, so as to complete the formation task as efficiently as possible while completing obstacle avoidance.

[0013] The consistency formation controller module is used to set the corresponding target positions for the slave unmanned platforms according to the set formation of the unmanned platforms, while taking into account communication delays and adjusting the estimated values ​​of the position and motion state of each unmanned platform.

[0014] The time-varying formation motion controller module is used to calculate the corresponding control quantities based on the target position and speed of each unmanned platform, while also taking into account the impact of communication delays and adjusting the control quantities accordingly.

[0015] Compared with the prior art, the significant progress of the present invention is as follows: (1) The present invention effectively reduces the transmission pressure of the communication system and ensures that the state update frequency can meet the needs of obstacle avoidance and formation by constructing a state controller; (2) The present invention efficiently completes the formation task and autonomously selects the appropriate formation by constructing an autonomous decision-making formation system; (3) The present invention effectively improves the accuracy of formation control by constructing a consistent formation controller; (4) The present invention improves the control accuracy and enhances the robustness of the formation system by constructing a time-varying formation motion controller.

[0016] To more clearly illustrate the functional characteristics and structural parameters of the present invention, further explanation is provided below in conjunction with the accompanying drawings and specific embodiments. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0018] Figure 1 This is a flowchart of the steps of the present invention;

[0019] Figure 2 This is a flowchart of the autonomous decision-making formation system of the present invention;

[0020] Figure 3 This is a simulation result diagram of the unmanned platform formation passing through an obstacle area according to the present invention. Detailed Implementation

[0021] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] This invention provides a formation control method for tracked unmanned platforms based on adaptive state updates and autonomous formation decision-making, combined with... Figure 1 This includes the following steps:

[0023] S1. Based on the motion state and pose information data of each unmanned platform, construct an adaptive update frequency state controller. The state controller adaptively adjusts the sampling and communication frequency of the data according to the platform itself and the surrounding environment, and updates the motion state and pose information data in real time.

[0024] When the unmanned platforms interact with each other to transmit motion state and pose information data and control quantities, the update frequency is variable. The state controller with an adaptive update frequency in S1 is shown in the following formula:

[0025] ;

[0026] in, To update frequency, As the reference frequency, The distance between the unmanned platform and the nearest obstacle. For safe distance threshold, This is the frequency adjustment coefficient. As the reference speed, For the speed of tracked unmanned platforms;

[0027] The data sampling and communication frequency of the formation system is not fixed; it adaptively adjusts based on the platform system itself and the surrounding environment.

[0028] When the formation is relatively tight, and there are many obstacles around it that are close to the unmanned platform, the frequency of status updates will increase; conversely, the frequency of status updates will decrease.

[0029] Through the aforementioned adaptive update frequency, the frequency at which the unmanned platform transmits its motion state, pose information, and motion control quantities will change in real time according to the surrounding environment and the platform's own motion state.

[0030] The control and communication architecture between the unmanned platforms adopts a centralized architecture. The entire system is managed by a control center that receives status information sent by all platforms and environmental perception information obtained by sensors, and sends the calculated control quantities to each unmanned platform.

[0031] S2. Compile a formation knowledge base and establish an artificial potential field to construct an autonomous decision-making formation system. This system enables unmanned platform formations to autonomously select target formations based on obstacle distribution and platform motion during operations. Figure 2 ,

[0032] S2-1. Constructing an artificial potential field: When the unmanned platform encounters an obstacle, it acquires the distance information between the obstacle and the unmanned platform to construct an artificial potential field, as shown in the following formula:

[0033] ;

[0034] in, The potential energy value of the artificial potential field for the unmanned platform. This is a proportionality coefficient that adjusts the magnitude of the potential field. The distance between the unmanned platform and the nearest surrounding obstacle. There is a threshold value; the potential energy value exceeds this threshold. The threshold is 0, and it is usually set as a safe distance between the vehicle and surrounding obstacles, such as 10m.

[0035] S2-2, Compile a formation knowledge base: When the average potential energy of the artificial potential field reaches the threshold and the average speed of each unmanned platform is greater than the threshold, it tends to choose to expand the formation and select a column formation that is easier to pass through. Conversely, it tends to choose to maintain or shrink the formation and select a faster square formation.

[0036] When obstacles impede the movement of unmanned platforms, the formation needs to be changed accordingly to bypass the obstacles and avoid collisions. The general idea can be understood as follows: the faster the speed, the greater the peak potential energy of the artificial potential field, and the more inclined to choose a formation with a smaller projection of the direction of movement, such as a column. At the same time, the size and spacing of the formation are larger, which makes it easier for the unmanned platform to avoid obstacles.

[0037] A typical formation knowledge base is shown in the table below:

[0038]

[0039] S2-3. The formation knowledge base constructs a mapping relationship between the artificial potential field, motion state, and target formation: based on the artificial potential field, the motion state of each unmanned platform, and the knowledge base, a suitable formation is selected for obstacle avoidance; the shape of the formation is set to include square, triangular, column, and horizontal formations, and the size transformation of the formation is set to include expanding the formation, maintaining the formation, and shrinking the formation. At the same time, the closest distance between unmanned platforms in the formation is set to 0.5m, 1.0m, and 2.0m, and obstacle avoidance is completed by adjusting the formation in real time.

[0040] S3. Based on the selected target formation, updated motion state and pose information data, calculate the target position of each unmanned platform in the target formation, construct a consistent formation controller to ensure that the state information of each platform tends to be consistent under communication delay conditions, and obtain the current position and velocity estimate of each unmanned platform.

[0041] S3-1. In an unmanned platform formation, one unmanned platform is designated as the master control platform, and the remaining unmanned platforms are slave control platforms. The movement of the master control platform is remotely controlled by a human, and the movement of each slave control platform is controlled by the motion control variables generated by the master control platform. Based on the updated motion state and pose information, the master control platform generates a target formation in the world coordinate system. The target formation includes the target position of each slave control platform in the world coordinate system. The relative target position of each slave control platform in its respective coordinate system is calculated through coordinate system transformation.

[0042] S3-2. Construct a consistent formation controller to ensure that the state information of each slave platform obtained by all master control platforms in S3-1 gradually becomes consistent with the actual state information of the slave platforms, reducing the impact of communication time delay: Based on the prediction compensation method, the master control platform uses the received past motion state and pose information of the slave platforms to predict the current motion state of the slave platforms, reducing the impact of communication delay, as shown in the following formula:

[0043] ;

[0044] in, , respectively for the first At this moment, each platform Position and velocity estimation, Due to communication delay, Received in the previous communication cycle The platform's movement speed, This refers to the location of platform j received in the previous communication cycle.

[0045] S4. Based on the target position, the current position, and the speed estimate, the master control platform determines the motion control amount of each slave control platform while remotely controlling the movement, so that it moves towards the target position. At the same time, time compensation is performed based on the motion control amount to solve the time delay problem, and the final motion control amount of each slave control platform is obtained to guide the unmanned platform formation to achieve cooperative obstacle avoidance movement.

[0046] S4-1. When the unmanned platforms communicate with each other through the state controller, the master control platform combines the target position of each slave control platform obtained in S3-1 with the current position and speed estimate of each slave control platform predicted in S3-2, calculates the error between the two, and obtains the motion control quantity of each slave control platform.

[0047] As shown in the following formula:

[0048] ;

[0049] in, Let t be the control quantity of the slave control platform at time t. For the first An estimate of the position of each platform at time t. For the first The target position of the platform at time t. For the first Current speed estimates for each platform The reference velocity at time t is consistent with the motion velocity of the main control platform. , These are the position proportional adjustment coefficient and the speed proportional adjustment coefficient.

[0050] S4-2. Construct a time-varying formation motion controller for the slave-controlled unmanned platform, perform time compensation on the existing motion control quantities, and add time compensation quantities based on the motion control quantities of each slave-controlled platform. The time compensation quantity is obtained by multiplying the speed of the slave-controlled platform received in the previous communication cycle by a certain proportional coefficient, thereby obtaining the final motion control quantities of each platform, guiding the slave-controlled platform to follow the arm patch and formation position, thereby completing the formation and obstacle avoidance tasks of the entire unmanned platform.

[0051] The time-varying formation motion controller is shown in the following formula:

[0052] ;

[0053] in, To account for the control quantity after time delay, Received in the previous communication cycle The platform's movement speed, The proportional coefficient is used to control the quantity.

[0054] By obtaining the motion control quantities of each slave platform, the slave platforms are guided to follow the arm patch and formation position, thereby completing the formation and obstacle avoidance tasks of the entire unmanned platform.

[0055] In step 2 Set to 20m. Set to 1000, such as Figure 3 The simulation results of this method are shown. During the simulation, three unmanned platforms (labeled as Platform 1, Platform 2, and Platform 3, respectively) moved collaboratively in an environment with obstacles, achieving safe obstacle avoidance through autonomous formation changes.

[0056] During the simulation, there were 3 tracked unmanned platforms. The simulation duration was 30 seconds, the time step Δt = 0.1 seconds, and there were a total of 300 time steps. The safe distance threshold was 2.5 meters, the reference update frequency was ƒ0 = 10 Hz (corresponding to the reference frequency in the patent formula), and the frequency adjustment coefficient was k = 0.5 (used for adaptive adjustment of the communication frequency). The formation spacing parameters were 2.0 meters for compact formation and 3.5 meters for extended formation.

[0057] In the initial phase (0-5 seconds): a compact column formation (2.0 meters apart) is adopted, and the platform moves in a straight line at a speed of 2.0 m / s. In the first obstacle avoidance phase (5-12 seconds): upon encountering a narrow passage or group of obstacles, the autonomous decision-making system is activated, and the formation changes to a line formation (3.5 meters apart), with the update frequency increasing to 15 Hz. In the recovery phase (12-19 seconds): a triangular formation (2.5 meters apart) is adopted to facilitate environmental observation and maneuver preparation. In the second obstacle avoidance phase (19-25 seconds): upon encountering scattered obstacles, the formation changes to an extended column formation (3.0 meters apart) to improve the maneuverability of individual platforms. In the final phase (25-30 seconds): the compact column formation is restored, and the platform continues to move forward.

[0058] The simulation results show that the method of the present invention can effectively realize the cooperative obstacle avoidance movement of multiple unmanned platforms in complex environments, and has good robustness and practicality.

[0059] The present invention discloses a system for formation control of tracked unmanned platforms based on adaptive state updates and autonomous formation decision-making, comprising the following modules:

[0060] The perception module, including LiDAR and cameras, is used to detect obstacles in the environment. When an obstacle hinders the movement of the unmanned platform, the formation needs to be changed accordingly to bypass the obstacle and avoid collision. Specifically, a rectangle is generated by extending a distance to the left and right of the vehicle's current position and the target position set in the formation as the center line. If an obstacle is located within the rectangular area, it is determined that the obstacle will hinder the vehicle from moving according to the current formation; otherwise, it is determined that the obstacle does not hinder the vehicle's movement.

[0061] The state controller module is used to adjust the communication frequency between unmanned platforms in real time, which reduces the transmission pressure on the communication system while ensuring that the state update frequency between platforms can meet the needs of obstacle avoidance and formation.

[0062] The autonomous decision-making formation module is used to autonomously select the appropriate formation type, size, and spacing based on the current movement speed of the unmanned platform formation system and the distribution of surrounding obstacles, so as to complete the formation task as efficiently as possible while completing obstacle avoidance.

[0063] The consistency formation controller module is used to set the corresponding target positions for the slave unmanned platforms according to the set formation of the unmanned platforms, while taking into account communication delays and adjusting the estimated values ​​of the position and motion state of each unmanned platform.

[0064] The time-varying formation motion controller module is used to calculate the corresponding control quantities based on the target position and speed of each unmanned platform, while also taking into account the impact of communication delays and adjusting the control quantities accordingly.

[0065] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0066] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for formation control of tracked unmanned platforms based on adaptive state updates and autonomous formation decision-making, characterized in that, Includes the following steps: S1. Based on the motion state and pose information data of each unmanned platform, construct an adaptive update frequency state controller. The state controller adaptively adjusts the sampling and communication frequency of the data according to the platform itself and the surrounding environment, and updates the motion state and pose information data in real time. When unmanned platforms exchange information and transmit motion state and pose information data and control quantities, the update frequency is variable. The state controller with an adaptive update frequency is constructed as shown in the following formula: ； in, To update frequency, As the reference frequency, The distance between the unmanned platform and the nearest obstacle. For safe distance threshold, This is the frequency adjustment coefficient. As the reference speed, For the speed of tracked unmanned platforms; When the formation is relatively tight, and there are many obstacles around it that are close to the unmanned platform, the frequency of status updates will increase; conversely, the frequency of status updates will decrease. S2. Compile a formation knowledge base and establish an artificial potential field to enable unmanned platform formations to autonomously select target formations based on the distribution of obstacles and the motion state of the unmanned platforms during operation. S3. Based on the selected target formation, updated motion state and pose information data, calculate the target position of each unmanned platform in the target formation, construct a consistent formation controller, and obtain the current position and velocity estimate of each unmanned platform. S4. Based on the target position, the current position, and the speed estimate, the master control platform determines the motion control amount of each slave control platform while remotely controlling the movement, so that it moves towards the target position. At the same time, time compensation is performed based on the motion control amount to obtain the final motion control amount of each slave control platform, guiding the unmanned platform formation to achieve cooperative obstacle avoidance.

2. The method for formation control of tracked unmanned platforms based on adaptive state update and autonomous formation decision-making according to claim 1, characterized in that, S2 includes the following steps: S2-1. Constructing an artificial potential field: When the unmanned platform encounters an obstacle, it acquires the distance information between the obstacle and the unmanned platform to construct an artificial potential field, as shown in the following formula: ； in, The potential energy value of the artificial potential field for the unmanned platform. This is a proportionality coefficient that adjusts the magnitude of the potential field. The distance between the unmanned platform and the nearest surrounding obstacle. There is a threshold value; the potential energy value exceeds this threshold. =0; S2-2, Compile a formation knowledge base: When the average potential energy of the artificial potential field reaches the threshold and the average speed of each unmanned platform is greater than the threshold, the formation tends to be expanded; otherwise, the formation tends to be maintained or reduced. S2-3. The formation knowledge base constructs a mapping relationship between the artificial potential field, motion state, and target formation: the artificial potential field, the motion state of each unmanned platform are matched with the knowledge base to select a suitable formation for obstacle avoidance.

3. The method for formation control of tracked unmanned platforms based on adaptive state update and autonomous formation decision-making according to claim 2, characterized in that, S3 includes the following steps: S3-1. In an unmanned platform formation, one unmanned platform is designated as the master control platform, and the remaining unmanned platforms are slave control platforms. The movement of the master control platform is remotely controlled by a human, and the movement of each slave control platform is controlled by the motion control variables generated by the master control platform. Based on the updated motion state and pose information, the master control platform generates a target formation in the world coordinate system. The target formation includes the target position of each slave control platform in the world coordinate system. The relative target position of each slave control platform in its respective coordinate system is calculated through coordinate system transformation. S3-2. Constructing a Consistent Formation Controller: Based on the prediction compensation method, the master control platform uses the received past motion states and pose information of the slave control platforms to predict the current motion state of the slave control platforms, as shown in the following formula: ； in, , respectively for the first At this moment, each platform Position and velocity estimation, Due to communication delay, Received in the previous communication cycle The platform's movement speed, This refers to the location of platform j received in the previous communication cycle.

4. The method for formation control of tracked unmanned platforms based on adaptive state update and autonomous formation decision-making according to claim 3, characterized in that, S4 includes the following steps: S4-1. When the unmanned platforms communicate with each other through the state controller, the master control platform combines the target position of each slave control platform obtained in S3-1 with the current position and speed estimate of each slave control platform predicted in S3-2, calculates the error between the two, and obtains the motion control quantity of each slave control platform. S4-2. Construct a time-varying formation motion controller for the slave-controlled unmanned platform, perform time compensation on the existing motion control quantities, and add time compensation quantities based on the motion control quantities of each slave-controlled platform. The time compensation quantity is obtained by multiplying the speed of the slave-controlled platform received in the previous communication cycle by a certain proportional coefficient, thereby obtaining the final motion control quantities of each platform, guiding the slave-controlled platform to follow the arm patch and formation position, and completing the formation and obstacle avoidance tasks of the entire unmanned platform.

5. A system for formation control of tracked unmanned platforms based on adaptive state updates and autonomous formation decision-making according to any one of claims 1-4, characterized in that, Includes the following modules: The perception module, including LiDAR and cameras, is used to detect obstacles in the environment. When an obstacle hinders the movement of the unmanned platform, the formation needs to be changed accordingly to bypass the obstacle and avoid collision. Specifically, a rectangle is generated by extending a distance to the left and right of the vehicle's current position and the target position set in the formation as the center line. If an obstacle is located within the rectangular area, it is determined that the obstacle will hinder the vehicle from moving according to the current formation; otherwise, it is determined that the obstacle does not hinder the vehicle's movement. The state controller module is used to adjust the communication frequency between unmanned platforms in real time, which reduces the transmission pressure on the communication system while ensuring that the state update frequency between platforms can meet the needs of obstacle avoidance and formation. The autonomous decision-making formation module is used to autonomously select the appropriate formation type, size, and spacing based on the current movement speed of the unmanned platform formation system and the distribution of surrounding obstacles, so as to complete the formation task as efficiently as possible while completing obstacle avoidance. The consistency formation controller module is used to set the corresponding target positions for the slave unmanned platforms according to the set formation of the unmanned platforms, while taking into account communication delays and adjusting the estimated values ​​of the position and motion state of each unmanned platform. The time-varying formation motion controller module is used to calculate the corresponding control quantities based on the target position and speed of each unmanned platform, while also taking into account the impact of communication delays and adjusting the control quantities accordingly.

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