A method and system for controlling unmanned vehicle clusters based on dynamic master-slave switching

Abstract

The present invention discloses a control method and system for an unmanned vehicle cluster based on dynamic master-slave switching. The method comprises: initializing the unmanned vehicle, selecting a formation, determining the initial position and speed of each of the unmanned vehicles; collecting and monitoring the status data of the unmanned vehicle, calculating the fitness function value, and judging whether it is necessary to switch the master vehicle; establishing a multi-layer fitness evaluation method, identifying the vehicle that is most suitable as the new master vehicle, and generating a switching preparation signal; coordinating the original master vehicle and the candidate master vehicle, and performing role conversion through smooth transition; after the master-slave switching is completed, readjusting the formation based on the obstacle avoidance and regression algorithm of the dynamic window. The system comprises: a communication architecture module, an initialization module, a decision module, a switching preparation module, a smooth transition module, and an adjustment module. The present invention solves the problems of the current unmanned vehicle cluster in the selection of the master vehicle and the transfer of the computational burden by introducing a flexible switching mechanism and an adaptive control strategy.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned vehicle cluster control, and in particular to an unmanned vehicle cluster control method and system based on dynamic master-slave switching. Background Art

[0002] Unmanned vehicle swarms are an important form of multi-vehicle collaboration and are widely used in logistics distribution, environmental monitoring, agricultural operations, military reconnaissance and other fields. In recent years, with the development of artificial intelligence, sensor technology and communication technology, the research on unmanned vehicle swarms has gradually deepened, especially in formation control, path planning, task allocation and other aspects.

[0003] However, despite the development of unmanned vehicle swarm technology, it still faces many challenges. Most existing control methods rely on fixed master-slave role allocation, which lacks flexibility and adaptability. When the master vehicle fails or other situations occur, the swarm may face the risk of losing the command center, affecting the overall operation efficiency.

[0004] The development trend of unmanned vehicle cluster control technology in the future is mainly reflected in the following aspects: (1) Dynamic master-slave switching. Researchers are exploring more flexible master-slave switching mechanisms to deal with emergencies such as master vehicle failures and task changes. Dynamic switching can improve the cluster's adaptability and robustness; (2) Intelligent decision-making and self-learning. Combining machine learning and deep learning algorithms, unmanned vehicles can self-learn and optimize during operation, thereby improving the intelligence of decision-making; (3) Multi-sensor fusion. Through the fusion of multi-sensor data, the unmanned vehicle's perception of the environment is improved, so that paths can be planned and obstacles can be avoided more effectively in complex environments; (4) Communication and coordination. Improve the communication efficiency and stability between unmanned vehicles, ensure real-time information exchange in complex environments, and maintain the cluster's collaborative combat capabilities.

[0005] In the actual application of unmanned vehicle cluster control, the current unmanned vehicle cluster control technology still has some objective technical defects: (1) The difficulty of selecting the master vehicle. At the beginning of the task, it is very important to select the optimal master vehicle. If it is randomly assigned, it may lead to low efficiency in task execution and affect the overall performance. (2) The risk of master vehicle failure. The master vehicle cannot continue to perform its duties due to failure, task impairment or poor communication, resulting in the cluster losing the control center and affecting task execution. (3) The problem of computing burden transfer. The master vehicle usually bears the largest amount of computing. Temporary replacement of the master vehicle will cause the computing burden to transfer rapidly, which may cause stability and safety risks of cluster operation. (4) Lack of unified standards. At present, various unmanned vehicle control technologies and standards on the market have not yet been unified, making it difficult for unmanned vehicles of different brands and models to communicate and cooperate with each other. (5) Insufficient real-time performance. In complex environments, the current dynamic switching mechanism may fail to respond to environmental changes in a timely manner, resulting in insufficient flexibility in operation. (6) Algorithm robustness. Existing algorithms may perform poorly under certain circumstances (such as extreme weather or complex obstacle environments), affecting the success rate of the task. Summary of the invention

[0006] In view of this, the purpose of an embodiment of the present invention is to provide a method and system for controlling an unmanned vehicle cluster based on dynamic master-slave switching, which solves the main problems existing in the current unmanned vehicle cluster in terms of master vehicle selection, failure processing, and computational burden transfer by introducing a flexible switching mechanism and an adaptive control strategy.

[0007] A method for controlling an unmanned vehicle cluster based on dynamic master-slave switching, comprising:

[0008] Establish a communication priority mechanism and give high priority to information related to master-slave switching.

[0009] Initialize the unmanned vehicles, select a formation, and determine the initial position and speed of each unmanned vehicle.

[0010] The state data of the unmanned vehicle is collected and monitored through multi-sensor fusion technology, the fitness function value is calculated, and it is determined whether the main vehicle needs to be switched.

[0011] A multi-layered fitness evaluation method is established to identify the most suitable vehicle as the new host vehicle and generate a switching preparation signal.

[0012] Coordinate the original host vehicle and the candidate host vehicles to switch roles through smooth transition.

[0013] After the master-slave switch is completed, the formation is readjusted based on the obstacle avoidance and regression algorithm based on the dynamic window.

[0014] In a preferred embodiment of the present invention, in the above-mentioned unmanned vehicle cluster control method based on dynamic master-slave switching, the establishment of a communication priority mechanism and giving high priority to information related to master-slave switching include:

[0015] Categorize data into high priority data, medium priority data, and low priority data.

[0016] High-priority channels, medium-priority channels and low-priority channels are designed to transmit data of different priority levels. The bandwidth increases with the increase of priority, and the delay decreases with the increase of priority.

[0017] Prioritize scheduling of higher priority channels and dynamically allocate occupied bandwidth.

[0018] For the medium priority data and the low priority data, a time slice rotation mechanism is adopted. When the high priority data is not transmitted, a short time slice is allocated to the medium priority channel and the low priority channel for data transmission.

[0019] The technical effect is: high-priority channels, medium-priority channels and low-priority channels are designed respectively, the bandwidth increases with the increase of priority, and the delay decreases with the increase of priority, which can ensure that key data is processed first in the communication link, avoid command delays caused by network congestion, and improve the response speed of the cluster.

[0020] In a preferred embodiment of the present invention, in the above-mentioned unmanned vehicle cluster control method based on dynamic master-slave switching, the steps of initializing the unmanned vehicles, selecting the formation, and determining the initial position and speed of each of the unmanned vehicles include:

[0021] Select the formation based on mission requirements, environmental conditions, number of vehicles, and objectives to be achieved.

[0022] The formation form includes at least one of a linear formation, a triangular formation, a diamond formation and a square formation.

[0023] For n unmanned vehicles, select one vehicle as the main vehicle , determine the reference center point of the formation or the point near the mission target as the main vehicle Initial position , the main vehicle The initial orientation is set to , the main vehicle The initial velocity is set to ,in, is the preset target formation speed, For adjusting the main vehicle The adjustment coefficient of the startup speed, 0 < ≤ 1.

[0024] For each car , based on the main vehicle The position of the slave vehicle is calculated Location , , ,in, For the main vehicle With the car The set distance between For the slave car With the main vehicle The relative angle between them.

[0025] For each of the slave vehicles , requiring the main vehicle Keep the deviation between the directions ,in, For the slave car Relative to the main vehicle Adjust the angle according to your posture.

[0026] For each of the slave vehicles , according to the main vehicle The relative position relationship between the speed and the vehicle is set from the initial speed of the vehicle ,in, For the slave car With the main vehicle The distance error between An adjustment factor used to control the sensitivity of speed changes with distance.

[0027] Perform status checks on all the unmanned vehicles to confirm whether each of the unmanned vehicles is in a preset position and status.

[0028] Its technical effects are: selecting a suitable formation form based on mission requirements, environmental conditions and the number of vehicles to ensure that the unmanned vehicle can efficiently perform tasks in different scenarios; the initial speed of the main vehicle is set as an adjustment of the preset target formation speed to ensure smoothness at startup, which helps to maintain the stability and consistency of the entire formation.

[0029] In a preferred embodiment of the present invention, in the above-mentioned unmanned vehicle cluster control method based on dynamic master-slave switching, the state data of the unmanned vehicle is collected and monitored by multi-sensor fusion technology, the fitness function value is calculated, and the determination of whether the master vehicle switching is required includes:

[0030] The surrounding environment data of each unmanned vehicle is collected through a variety of sensors, and the surrounding environment data is filtered.

[0031] For each of the unmanned vehicles, a fitness function is calculated to quantify its performance in the current environment and task. ,in, is the distance from the unmanned vehicle to the target point, is the current speed of the unmanned vehicle, is the deviation of the unmanned vehicle from the ideal formation path, The environmental safety factor is calculated based on data from multiple sensors. is the remaining power or fuel volume of the unmanned vehicle, , , , and is the performance coefficient.

[0032] If one of the vehicles is detected to be suitable for , the current slave vehicle is taken as a candidate master vehicle and it is determined that a master vehicle switch is required, wherein, For fitment from the car, For the suitability of the main vehicle, is the fitness threshold.

[0033] Its technical effect is: dynamically adjusting performance coefficients (such as distance, speed, etc.) based on multi-sensor data to ensure that the fitness function can accurately reflect the performance of the unmanned vehicle in the current task and environment. If it is detected that the fitness function of a slave vehicle meets the switching conditions, it will be used as a candidate master vehicle. When the original master vehicle fails or is otherwise unavailable, it can quickly identify the most suitable replacement to ensure the continuous operation of the cluster and ensure the stability and continuity of the unmanned vehicle cluster when performing tasks.

[0034] In a preferred embodiment of the present invention, in the above-mentioned unmanned vehicle cluster control method based on dynamic master-slave switching, the establishment of a multi-layer fitness evaluation method, identifying the vehicle most suitable as the new master vehicle, and generating a switching preparation signal includes:

[0035] Calculate the actual distance between the original host vehicle and the candidate host vehicle .

[0036] Set the preset ideal relative distance between the original host vehicle and the candidate host vehicle .

[0037] Calculate relative distance error .

[0038] Set distance error threshold ,like , it is considered that the distance between the candidate main vehicle and the original main vehicle meets the requirements.

[0039] Calculate the expected relative orientation of the original vehicle and the candidate vehicle ,in, is the ideal relative angle between the original host vehicle and the candidate host vehicle.

[0040] Calculate the actual orientation of the candidate vehicle .

[0041] Calculate relative angle error .

[0042] Setting the angle error threshold ,like , it is considered that the orientation between the candidate main vehicle and the original main vehicle meets the requirements.

[0043] When the distance and orientation between the candidate main vehicle and the original main vehicle meet the requirements, it is determined that the main vehicle needs to be switched.

[0044] Generate a switching preparation signal, and send the switching preparation signal to the candidate master vehicle and other slave vehicles through a corresponding priority communication channel.

[0045] Its technical effect is: by establishing a multi-layer fitness evaluation method, calculating the actual distance between the original main vehicle and the candidate main vehicle and the preset ideal relative distance, timely judging the adaptability of the candidate main vehicle, and after setting the distance error threshold, effectively screening out the candidate main vehicles whose distance meets the requirements, ensuring the stability of the formation during the switching process.

[0046] In a preferred embodiment of the present invention, in the above-mentioned unmanned vehicle cluster control method based on dynamic master-slave switching, after determining that the master vehicle switching is required, it also includes:

[0047] The obstacle data obtained through multi-sensor fusion is used to determine whether the environmental safety factor of the area where the candidate main vehicle is located meets the standard. If it is safe, the switch continues.

[0048] The energy endurance of the candidate main vehicle is checked, and if the remaining energy of the candidate main vehicle meets the task requirements, the switching continues.

[0049] Its technical effects are: obtaining obstacle data through multi-sensor fusion technology and judging the environmental safety factor of the area where the candidate main vehicle is located. Only when the safety factor meets the standard will the main vehicle switch be carried out to avoid switching in a dangerous environment; checking the remaining energy of the candidate main vehicle to ensure that its energy meets the mission requirements, reducing the risk of mission interruption due to insufficient energy.

[0050] In a preferred embodiment of the present invention, in the above-mentioned unmanned vehicle cluster control method based on dynamic master-slave switching, the coordination of the original master vehicle and the candidate master vehicle to perform role conversion through smooth transition includes:

[0051] Adjust the speed of the candidate vehicle based on the relative position with the original vehicle and the target position. , the calculation formula is

[0052] ,in, The candidate main vehicle The adjustment speed, is the ideal relative distance set between the master vehicle and the slave vehicle, is the ideal relative angle set between the master vehicle and the slave vehicle, ( , ) is the actual relative position difference between the original host vehicle and the candidate host vehicle, is the angular velocity of the original vehicle, is the speed of the original owner vehicle, and is the adjustment factor.

[0053] Adjust the angular velocity of the candidate vehicle , the calculation formula is ,in, is the relative angle between the candidate host vehicle and the original host vehicle, is the relative angle between the original owner vehicle and one of the slave vehicles, and is the adjustment factor.

[0054] The candidate host vehicle is determined based on the calculated speed and angular velocity Gradually adjust position and posture to make smooth transitions.

[0055] The technical effect is that the candidate master vehicle gradually adjusts its position and attitude according to the calculated speed and angular velocity to ensure the stability of the switching process. By adjusting the speed of the candidate master vehicle based on the relationship between the relative position of the original master vehicle and the candidate master vehicle and the target position, the candidate master vehicle is ensured to gradually approach the ideal position, reducing sudden changes caused by switching, thereby maintaining the stability of the formation.

[0056] In a preferred embodiment of the present invention, in the above-mentioned unmanned vehicle cluster control method based on dynamic master-slave switching, after the master-slave switching is completed, the obstacle avoidance and regression algorithm based on the dynamic window is used to readjust the formation, which includes:

[0057] Recalculate the ideal path and set a new target point based on the current status and mission objectives of the candidate vehicle .

[0058] The current linear speed of the candidate main vehicle and angular velocity Based on this, define the range of the dynamic window.

[0059] The actual range of the dynamic window is limited according to defined constraints, wherein the constraints include a maximum linear velocity, a minimum linear velocity, a maximum angular velocity, and a minimum angular velocity of the candidate host vehicle.

[0060] Calculate the evaluation function for evaluating the dynamic window ,in, Used to evaluate the angle between the vehicle and the target direction, Used to assess the safe distance between the vehicle and obstacles. Used to measure speed. Used to calculate the proximity between the current path and the ideal target path, is the standardization factor, , , and is the weight coefficient.

[0061] The evaluation function is selected The value that maximizes the velocity and angular velocity combination is used as the current motion plan.

[0062] Sensor data is used to detect surrounding obstacles in real time, and the motion plan is adjusted according to the relative position and size of the obstacles.

[0063] If the current path in the motion plan deviates from the ideal path, the linear speed of the candidate vehicle is adjusted by a regression algorithm. and angular velocity ,in, is the error value between the current path and the ideal path, is the direction error, is the proportional control gain.

[0064] According to the adjusted motion plan, current control instructions are generated to readjust the formation.

[0065] Its technical effects are: defining the range of the dynamic window and setting restrictions based on the speed and rotation ability of the candidate main vehicle to ensure that the unmanned vehicle can avoid obstacles during movement, and ensuring that no collision occurs during movement by detecting surrounding obstacles in real time and adjusting the motion plan; by calculating the evaluation function used to evaluate the dynamic window, comprehensively evaluating the angle between the vehicle and the target direction, the safe distance of the obstacle, the speed, and the proximity of the current path to the ideal target path, more comprehensively reflecting the pros and cons of the current motion state, and ensuring the accuracy and feasibility of the motion plan; by real-time monitoring of the environment and dynamically adjusting the motion plan, ensuring that the unmanned vehicle can quickly respond to external changes when performing tasks.

[0066] In a preferred embodiment of the present invention, in the above-mentioned unmanned vehicle cluster control method based on dynamic master-slave switching, after the master-slave switching is completed, it also includes:

[0067] The status of each unmanned vehicle is continuously monitored, including position, speed, angle, and distance from the target, so that each unmanned vehicle maintains a corresponding relative position in the formation.

[0068] The sensor data is used to continuously update the status of the unmanned vehicles and assess the stability of the formation.

[0069] Adjust the control strategy according to the state of the unmanned vehicle and environmental changes.

[0070] Its technical effect is: by continuously monitoring the status of unmanned vehicles and environmental changes, the control strategy is adjusted in real time to ensure that the unmanned vehicles maintain corresponding relative positions in the formation, which significantly enhances the stability, safety and adaptability of the unmanned vehicle cluster.

[0071] An unmanned vehicle cluster control system based on dynamic master-slave switching, comprising:

[0072] The communication architecture module is used to establish a communication priority mechanism and give high priority to information related to master-slave switching.

[0073] The initialization module is used to initialize the unmanned vehicles, select the formation, and determine the initial position and speed of each of the unmanned vehicles.

[0074] The decision module is used to collect and monitor the status data of the unmanned vehicle through multi-sensor fusion technology, calculate the fitness function value, and determine whether it is necessary to switch the main vehicle.

[0075] The switching preparation module is used to establish a multi-layer fitness evaluation method, identify the vehicle most suitable as the new host vehicle, and generate a switching preparation signal.

[0076] The smooth transition module is used to coordinate the original host vehicle and the candidate host vehicle to perform role conversion through smooth transition.

[0077] The adjustment module is used to readjust the formation based on the obstacle avoidance and regression algorithm of the dynamic window after the master-slave switching is completed.

[0078] The beneficial effects of the embodiments of the present invention are:

[0079] The present invention prioritizes information related to master-slave switching to ensure that key data (such as master vehicle fitness, switching requests, etc.) can be transmitted quickly and reliably, significantly reducing information delays, ensuring rapid response in emergency situations, and improving the command and control efficiency of the cluster. After dividing data into high, medium, and low priorities, the corresponding channels are designed for transmission, optimizing the allocation of communication resources and improving the overall communication performance of the system.

[0080] The present invention can comprehensively evaluate the status of the unmanned vehicle according to the environment and task requirements by calculating the fitness function value in real time, ensuring that the selection of the main vehicle is more targeted and improving the efficiency and stability of the cluster when performing tasks. The fitness function comprehensively considers multiple factors such as distance, speed, deviation, safety factor and remaining energy, making the evaluation more comprehensive and accurate.

[0081] The present invention establishes a multi-layer fitness evaluation method, which can systematically evaluate the adaptability of candidate master vehicles when the master vehicle is switched, ensure the selection of the most suitable vehicle, improve the reliability of master vehicle switching, and avoid mission failure caused by improper selection. By calculating the distance and angle errors, the stable relationship between the candidate master vehicle and the original master vehicle can be ensured during the switching process, avoiding emergencies that affect the overall stability of the formation.

[0082] When the role switching between the master vehicle and the candidate master vehicle is carried out, the present invention achieves a smooth transition by adjusting the speed and angular velocity, ensures the stability of the switching process, avoids system instability caused by sudden changes, ensures the overall coordination of the cluster, and enhances the reliability of task execution. By coordinating the relative positions of the original master vehicle and the candidate master vehicle, the interference of the role switching on other slave vehicles is reduced, so that the entire cluster can operate continuously and stably.

[0083] The obstacle avoidance capability of the present invention based on the dynamic window method enables the unmanned vehicle to calculate the optimal path in real time and avoid obstacles, thus improving the safety of the system in complex environments. The speed and angular velocity of the unmanned vehicle are adjusted through the regression algorithm to ensure that it can accurately track the ideal path, effectively enhancing the success rate of task execution and ensuring that the unmanned vehicle can efficiently complete the scheduled task. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0085] Figure 1 The present invention is a flow chart of the unmanned vehicle cluster control method based on dynamic master-slave switching. DETAILED DESCRIPTION

[0086] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0087] Please refer to Figure 1 The first embodiment of the present invention provides a method for controlling an unmanned vehicle cluster based on dynamic master-slave switching, which includes: establishing a communication priority mechanism to give high priority to information related to the master-slave switching; initializing the unmanned vehicles, selecting a formation, and determining the initial position and speed of each of the unmanned vehicles; collecting and monitoring the status data of the unmanned vehicles through multi-sensor fusion technology, calculating the fitness function value, and determining whether the master vehicle switching is required; establishing a multi-layer fitness evaluation method to identify the vehicle that is most suitable as the new master vehicle and generate a switching preparation signal; coordinating the original master vehicle and the candidate master vehicle to perform role conversion through smooth transition; after the master-slave switching is completed, readjusting the formation based on the obstacle avoidance and regression algorithm of the dynamic window.

[0088] In a preferred embodiment of the present invention, in the above-mentioned unmanned vehicle cluster control method based on dynamic master-slave switching, the establishment of a communication priority mechanism and giving high priority to information related to master-slave switching include: dividing the data into high priority data, medium priority data and low priority data; specifically, the high priority data includes the fitness function value of the master vehicle at least one of the following: update, main vehicle switching request, switching preparation signal, switching completion confirmation information and emergency fault information; the medium priority data includes at least one of the unmanned vehicle position information, unmanned vehicle speed information, path planning data and obstacle detection data; the low priority data includes at least one of environmental monitoring data and status data synchronization; high priority channels, medium priority channels and low priority channels are designed respectively to transmit data of different priority levels, the bandwidth increases with the increase of priority, and the delay decreases with the increase of priority; higher priority channels are prioritized for scheduling, and the occupied bandwidth is dynamically allocated; for the medium priority data and the low priority data, a time slice rotation mechanism is adopted, and when the high priority data is not transmitted, a short time slice is allocated to the medium priority channel and the low priority channel for data transmission.

[0089] Specifically, in the unmanned vehicle cluster system, the priority scheduling and communication adjustment process includes: when high-priority data (such as a master vehicle switching request or a fault report) is detected, the data is immediately placed in the queue of the high-priority channel; the high-priority data is immediately transmitted to the relevant vehicles to ensure that they receive the information in a very short time and perform corresponding processing; when the high-priority data is sent, the data transmission of the medium and low priority channels is restored; the data flow of each channel is dynamically monitored, and when the data volume of the high-priority channel is large, its bandwidth is temporarily increased to ensure its data transmission speed; in the master-slave switching process, especially in the switching preparation stage and the switching completion stage, more bandwidth is preferentially allocated to the high-priority channel to ensure the rapid synchronization of switching related information step; when the communication network is congested, adopt a priority downgrade strategy, for example, temporarily downgrade some medium-priority data to low priority to free up bandwidth for more important information transmission; temporarily suspend transmission of low-priority data, and then perform supplementary transmission after high-priority data is sent; monitor the real-time data transmission status of each channel, and adjust the transmission parameters of each channel according to the monitoring results, such as adjusting the channel bandwidth ratio, adjusting the scheduling order of data queues, etc., to ensure that the priority transmission of master-slave switching related information can still be guaranteed under different network loads; simulate different communication loads and scenarios (such as high-density obstacle environment, complex terrain environment) in the laboratory environment, test the performance of the multi-channel communication mechanism, and optimize the priority allocation strategy. In the actual unmanned vehicle formation environment, detect the impact of the communication priority mechanism on the master-slave switching process, and continuously adjust the parameters through field data feedback to ensure high efficiency in real scenarios.

[0090] The technical effect is: high-priority channels, medium-priority channels and low-priority channels are designed respectively, the bandwidth increases with the increase of priority, and the delay decreases with the increase of priority, which can ensure that key data is processed first in the communication link, avoid command delays caused by network congestion, and improve the response speed of the cluster.

[0091] In a preferred embodiment of the present invention, in the above-mentioned unmanned vehicle cluster control method based on dynamic master-slave switching, the initialization of the unmanned vehicles, selection of the formation, and determination of the initial position and speed of each of the unmanned vehicles include: selecting the formation form according to task requirements, environmental conditions, number of vehicles and completion goals; the formation form includes at least one of a linear formation, a triangular formation, a diamond formation and a square formation; specifically, in a linear formation, the unmanned vehicles are arranged along a straight line, which is suitable for narrow roads or scenes that require concentrated forward movement, and the ideal position of the slave vehicles is arranged along the direction of movement of the main vehicle, and the distance and angle are kept consistent; in a triangular formation, the main vehicle is in the front and the other vehicles are arranged in a triangular shape in the rear, which is suitable for tasks that require a better observation field of view or dispersed sensor measurements; in a diamond formation, the vehicles form a diamond formation around the main vehicle, which can respond quickly in multiple directions and is suitable for movement in complex environments; in a square formation, the main vehicle is located in the center or front, and the other slave vehicles form a square arrangement, which is suitable for balanced coverage areas.

[0092] For n unmanned vehicles, select one vehicle as the main vehicle , determine the reference center point of the formation or the point near the mission target as the main vehicle Initial position , the main vehicle The initial orientation is set to , the main vehicle The initial velocity is set to ,in, is the preset target formation speed, For adjusting the main vehicle The adjustment coefficient of the startup speed, 0 < ≤ 1; for each slave vehicle , based on the main vehicle The position of the slave vehicle is calculated Location , , ,in, For the main vehicle With the car The set distance between For the slave car With the main vehicle The relative angle between the two; for each of the slave vehicles , requiring the main vehicle Keep the deviation between the directions ,in, For the slave car Relative to the main vehicle The posture adjustment angle; for each of the slave vehicles , according to the main vehicle The relative position relationship between the speed and the vehicle is set from the initial speed of the vehicle ,in, For the slave car With the main vehicle The distance error between is the adjustment coefficient used to control the sensitivity of speed changes with distance; and May be located at the left and right rear of the main vehicle, with a distance and Same, but at an angle and They are negative and positive respectively, reflecting the left-right symmetrical arrangement of the two vehicles relative to the main vehicle.

[0093] Perform status verification on all the unmanned vehicles, including position deviation, speed consistency, posture consistency, communication connection status, etc., to confirm whether each of the unmanned vehicles is in a preset position and status.

[0094] Its technical effects are: selecting a suitable formation form based on mission requirements, environmental conditions and the number of vehicles to ensure that the unmanned vehicle can efficiently perform tasks in different scenarios; the initial speed of the main vehicle is set as an adjustment of the preset target formation speed to ensure smoothness at startup, which helps to maintain the stability and consistency of the entire formation.

[0095] In a preferred embodiment of the present invention, in the above-mentioned unmanned vehicle cluster control method based on dynamic master-slave switching, the state data of the unmanned vehicles are collected and monitored by multi-sensor fusion technology, the fitness function value is calculated, and the judgment on whether the master vehicle switching is required includes: collecting the surrounding environment data of each unmanned vehicle through a variety of sensors, and filtering the surrounding environment data; specifically, obtaining position information and attitude information from GPS, inertial measurement unit and lidar, the attitude information includes pitch angle, roll angle and yaw angle; lidar and radar provide accurate distance measurement, and the camera is used to identify the type of obstacle; by fusing the detection results of radar and camera, the relative distance between the master vehicle and the slave vehicle is estimated. and relative angle ; The inertial measurement unit and on-board sensors provide linear velocity and angular velocity data.

[0096] For each of the unmanned vehicles, a fitness function is calculated to quantify its performance in the current environment and task. ,in, is the distance from the unmanned vehicle to the target point, is the current speed of the unmanned vehicle, is the deviation of the unmanned vehicle from the ideal formation path, The environmental safety factor is calculated based on data from multiple sensors. is the remaining power or fuel volume of the unmanned vehicle, , , , and is the performance coefficient; if it is detected that the fitness of one of the slave vehicles meets , the current slave vehicle is taken as a candidate master vehicle and it is determined that a master vehicle switch is required, wherein, For fitment from the car, For the suitability of the main vehicle, is the fitness threshold.

[0097] Its technical effect is: dynamically adjusting performance coefficients (such as distance, speed, etc.) based on multi-sensor data to ensure that the fitness function can accurately reflect the performance of the unmanned vehicle in the current task and environment. If it is detected that the fitness function of a slave vehicle meets the switching conditions, it will be used as a candidate master vehicle. When the original master vehicle fails or is otherwise unavailable, it can quickly identify the most suitable replacement to ensure the continuous operation of the cluster and ensure the stability and continuity of the unmanned vehicle cluster when performing tasks.

[0098] In a preferred embodiment of the present invention, in the above-mentioned unmanned vehicle cluster control method based on dynamic master-slave switching, the establishment of a multi-layer fitness evaluation method to identify the vehicle most suitable as the new master vehicle and generate a switching preparation signal includes: calculating the actual distance between the original master vehicle and the candidate master vehicle ; Set the preset ideal relative distance between the original host vehicle and the candidate host vehicle ; Calculate relative distance error ; Set the distance error threshold ,like , it is considered that the distance between the candidate main vehicle and the original main vehicle meets the requirement; calculate the expected relative direction between the original main vehicle and the candidate main vehicle ,in, is the ideal relative angle between the original host vehicle and the candidate host vehicle; calculates the actual direction of the current candidate host vehicle ; Calculate relative angle error ; Set the angle error threshold ,like , it is considered that the orientation between the candidate master vehicle and the original master vehicle meets the requirements; when the distance and orientation between the candidate master vehicle and the original master vehicle both meet the requirements, it is determined that the master vehicle needs to be switched; a switching preparation signal is generated, and the switching preparation signal is sent to the candidate master vehicle and other slave vehicles through the corresponding priority communication channel.

[0099] The switching preparation signal includes confirmation information of the candidate host vehicle, a target switching timestamp, and a control strategy during the switching process, including instructions for speed adjustment and position correction.

[0100] Its technical effect is: by establishing a multi-layer fitness evaluation method, calculating the actual distance between the original main vehicle and the candidate main vehicle and the preset ideal relative distance, timely judging the adaptability of the candidate main vehicle, and after setting the distance error threshold, effectively screening out the candidate main vehicles whose distance meets the requirements, ensuring the stability of the formation during the switching process.

[0101] In a preferred embodiment of the present invention, in the above-mentioned unmanned vehicle cluster control method based on dynamic master-slave switching, after determining that the master vehicle needs to be switched, it also includes: judging whether the environmental safety factor of the area where the candidate master vehicle is located meets the standard through obstacle data obtained by multi-sensor fusion, and if it is safe, continue switching; checking the energy endurance of the candidate master vehicle, and if the remaining energy of the candidate master vehicle meets the task requirements, continue switching.

[0102] Its technical effects are: obtaining obstacle data through multi-sensor fusion technology and judging the environmental safety factor of the area where the candidate main vehicle is located. Only when the safety factor meets the standard will the main vehicle switch be carried out to avoid switching in a dangerous environment; checking the remaining energy of the candidate main vehicle to ensure that its energy meets the mission requirements, reducing the risk of mission interruption due to insufficient energy.

[0103] In a preferred embodiment of the present invention, in the above-mentioned unmanned vehicle cluster control method based on dynamic master-slave switching, the coordination of the original master vehicle and the candidate master vehicle to perform role conversion through smooth transition includes: adjusting the speed of the candidate master vehicle based on the relationship between the relative position with the original master vehicle and the target position; , the calculation formula is ,in, The candidate main vehicle The adjustment speed, is the ideal relative distance set between the master vehicle and the slave vehicle, is the ideal relative angle set between the master vehicle and the slave vehicle, ( , ) is the actual relative position difference between the original host vehicle and the candidate host vehicle, is the angular velocity of the original vehicle, is the speed of the original owner vehicle, and is the adjustment coefficient; adjust the angular velocity of the candidate main vehicle , the calculation formula is ,in, is the relative angle between the candidate host vehicle and the original host vehicle, is the relative angle between the original owner vehicle and one of the slave vehicles, and is the adjustment coefficient; the candidate main vehicle is based on the calculated speed and angular velocity Gradually adjust position and posture to make smooth transitions.

[0104] The technical effect is that the candidate master vehicle gradually adjusts its position and attitude according to the calculated speed and angular velocity to ensure the stability of the switching process. By adjusting the speed of the candidate master vehicle based on the relationship between the relative position of the original master vehicle and the candidate master vehicle and the target position, the candidate master vehicle is ensured to gradually approach the ideal position, reducing sudden changes caused by switching, thereby maintaining the stability of the formation.

[0105] In a preferred embodiment of the present invention, in the above-mentioned unmanned vehicle cluster control method based on dynamic master-slave switching, after the master-slave switching is completed, the obstacle avoidance and regression algorithm based on the dynamic window is used to readjust the formation, including: recalculating the ideal path according to the current state and mission objectives of the candidate master vehicle, setting a new target point ; At the current linear speed of the candidate main vehicle and angular velocity Based on the above, the range of the dynamic window is defined; the actual range of the dynamic window is limited according to the defined constraints, wherein the constraints include the maximum linear velocity, the minimum linear velocity, the maximum angular velocity and the minimum angular velocity of the candidate main vehicle; and an evaluation function for evaluating the dynamic window is calculated. ,in, Used to evaluate the angle between the vehicle and the target direction, Used to assess the safe distance between the vehicle and obstacles. Used to measure speed. Used to calculate the proximity between the current path and the ideal target path, is the standardization factor, , , and is the weight coefficient; select the evaluation function The speed and angular velocity combination that maximizes the value of is used as the current motion plan; the surrounding obstacles are detected in real time using sensor data, and the motion plan is adjusted according to the relative position and size of the obstacles; if there is a deviation between the current path in the motion plan and the ideal path, the linear speed of the candidate main vehicle is adjusted through a regression algorithm and angular velocity ,in, is the error value between the current path and the ideal path, is the direction error, is the proportional control gain, is the current linear speed of the candidate host vehicle, is the current angular velocity of the candidate main vehicle; according to the adjusted motion plan, a current control instruction is generated to readjust the formation.

[0106] Its technical effects are: defining the range of the dynamic window and setting restrictions based on the speed and rotation ability of the candidate main vehicle to ensure that the unmanned vehicle can avoid obstacles during movement, and ensuring that no collision occurs during movement by detecting surrounding obstacles in real time and adjusting the motion plan; by calculating the evaluation function used to evaluate the dynamic window, comprehensively evaluating the angle between the vehicle and the target direction, the safe distance of the obstacle, the speed, and the proximity of the current path to the ideal target path, more comprehensively reflecting the pros and cons of the current motion state, and ensuring the accuracy and feasibility of the motion plan; by real-time monitoring of the environment and dynamically adjusting the motion plan, ensuring that the unmanned vehicle can quickly respond to external changes when performing tasks.

[0107] In a preferred embodiment of the present invention, in the above-mentioned unmanned vehicle cluster control method based on dynamic master-slave switching, after the master-slave switching is completed, it also includes: continuously monitoring the status of each of the unmanned vehicles, including position, speed, angle, and distance from the target, so that each of the unmanned vehicles maintains a corresponding relative position in the formation; using sensor data to continuously update the status of the unmanned vehicles and evaluate the stability of the formation; adjusting the control strategy according to the status of the unmanned vehicles and environmental changes.

[0108] Its technical effect is: by continuously monitoring the status of unmanned vehicles and environmental changes, the control strategy is adjusted in real time to ensure that the unmanned vehicles maintain corresponding relative positions in the formation, which significantly enhances the stability, safety and adaptability of the unmanned vehicle cluster.

[0109] The second embodiment of the present invention provides an unmanned vehicle cluster control system based on dynamic master-slave switching, which includes: a communication architecture module, which is used to establish a communication priority mechanism and give high priority to information related to master-slave switching; an initialization module, which is used to initialize the unmanned vehicles, select the formation, and determine the initial position and speed of each of the unmanned vehicles; a decision module, which is used to collect and monitor the status data of the unmanned vehicles through multi-sensor fusion technology, calculate the fitness function value, and determine whether the master vehicle needs to be switched; a switching preparation module, which is used to establish a multi-layer fitness evaluation method, identify the vehicle that is most suitable as the new master vehicle, and generate a switching preparation signal; a smooth transition module, which is used to coordinate the original master vehicle and the candidate master vehicle, and perform role conversion through smooth transition; an adjustment module, which is used to readjust the formation after the master-slave switching is completed based on the obstacle avoidance and regression algorithm of the dynamic window.

[0110] The computer program product of the unmanned vehicle cluster control method and device based on dynamic master-slave switching provided in the embodiments of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the method in the previous method embodiment. The specific implementation can be found in the method embodiment, which will not be repeated here.

[0111] Specifically, the storage medium can be a general storage medium, such as a mobile disk, a hard disk, etc. When the computer program on the storage medium is run, it can execute the above-mentioned unmanned vehicle cluster control method based on dynamic master-slave switching, thereby significantly improving the coordination ability, safety and task execution efficiency of the unmanned vehicle cluster.

[0112] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that can be executed by a processor. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0113] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for controlling an unmanned vehicle cluster based on dynamic master-slave switching, characterized in that: include: Establish a communication priority mechanism to give high priority to information related to master-slave switching; Initializing the unmanned vehicles, selecting a formation, and determining the initial position and speed of each of the unmanned vehicles; The state data of the unmanned vehicle is collected and monitored by multi-sensor fusion technology, and the fitness function value is calculated to determine whether a main vehicle switch is required; Establish a multi-layered fitness assessment method to identify the most suitable vehicle as the new host vehicle and generate a switching preparation signal; Coordinate the original host vehicle and candidate host vehicles to switch roles through smooth transition; After the master-slave switch is completed, the formation is readjusted based on the obstacle avoidance and regression algorithm of the dynamic window; The collecting and monitoring of the state data of the unmanned vehicle by multi-sensor fusion technology, calculating the fitness function value, and determining whether the main vehicle needs to be switched include: Collecting surrounding environment data of each unmanned vehicle through a variety of sensors, and filtering the surrounding environment data; For each of the unmanned vehicles, a fitness function is calculated to quantify its performance in the current environment and task. ,in, is the distance from the unmanned vehicle to the target point, is the current speed of the unmanned vehicle, is the deviation of the unmanned vehicle from the ideal formation path, It is an environmental safety factor calculated based on data from multiple sensors. is the remaining power or fuel volume of the unmanned vehicle, , , , and is the performance coefficient; If one of the vehicles is detected to be suitable for , the current slave vehicle is taken as a candidate master vehicle and it is determined that a master vehicle switch is required, wherein, For fitment from the car, For the suitability of the main vehicle, is the fitness threshold; The method of establishing a multi-layer suitability evaluation method, identifying the vehicle most suitable as the new host vehicle, and generating a switching preparation signal comprises: Calculate the actual distance between the original host vehicle and the candidate host vehicle ; Set the preset ideal relative distance between the original host vehicle and the candidate host vehicle ; Calculate relative distance error ; Set distance error threshold ,like , it is considered that the distance between the candidate main vehicle and the original main vehicle meets the requirement; Calculate the expected relative orientation of the original vehicle and the candidate vehicle ,in, is the ideal relative angle between the original host vehicle and the candidate host vehicle; Calculate the actual orientation of the candidate vehicle ; Calculate relative angle error ; Setting the angle error threshold ,like , it is considered that the orientation between the candidate main vehicle and the original main vehicle meets the requirements; When the distance and direction between the candidate main vehicle and the original main vehicle meet the requirements, it is determined that the main vehicle needs to be switched; Generate a switching preparation signal, and send the switching preparation signal to the candidate master vehicle and other slave vehicles through a corresponding priority communication channel; After determining that the main vehicle needs to be switched, the following steps are also included: Through the obstacle data obtained by multi-sensor fusion, it is judged whether the environmental safety factor of the area where the candidate main vehicle is located meets the standard. If it is safe, the switch continues; Performing an energy endurance check on the candidate main vehicle, and continuing to switch if the remaining energy of the candidate main vehicle meets the task requirements; The coordination of the original master vehicle and the candidate master vehicle to perform role conversion through smooth transition includes: Adjust the speed of the candidate vehicle based on the relative position with the original vehicle and the target position. , the calculation formula is ,in, The candidate main vehicle The adjustment speed, is the ideal relative distance set between the master vehicle and the slave vehicle, is the ideal relative angle set between the master vehicle and the slave vehicle, ( , ) is the actual relative position difference between the original host vehicle and the candidate host vehicle, is the angular velocity of the original vehicle, is the speed of the original owner vehicle, and is the adjustment factor; Adjust the angular velocity of the candidate vehicle , the calculation formula is ,in, is the relative angle between the candidate host vehicle and the original host vehicle, is the relative angle between the original owner vehicle and one of the slave vehicles, and is the adjustment factor; The candidate host vehicle is determined based on the calculated speed and angular velocity Gradually adjust position and posture to make smooth transitions.

2. The unmanned vehicle cluster control method based on dynamic master-slave switching according to claim 1 is characterized in that: The communication priority mechanism is established to give high priority to the information related to the master-slave switching, including: Classify data into high priority data, medium priority data and low priority data; High priority channels, medium priority channels and low priority channels are designed to transmit data of different priority levels. The bandwidth increases with the priority level, and the delay decreases with the priority level. Prioritize scheduling of higher priority channels and dynamically allocate occupied bandwidth; For the medium priority data and the low priority data, a time slice rotation mechanism is adopted. When the high priority data is not transmitted, a short time slice is allocated to the medium priority channel and the low priority channel for data transmission.

3. The unmanned vehicle cluster control method based on dynamic master-slave switching according to claim 1 is characterized in that: Initializing the unmanned vehicles, selecting the formation, and determining the initial position and speed of each unmanned vehicle includes: Select the formation based on mission requirements, environmental conditions, number of vehicles, and completion objectives; The formation includes at least one of a linear formation, a triangular formation, a diamond formation and a square formation; For n unmanned vehicles, select one vehicle as the main vehicle , determine the reference center point of the formation or the point near the mission target as the main vehicle Initial position , the main vehicle The initial orientation is set to , the main vehicle The initial velocity is set to ,in, is the preset target formation speed, For adjusting the main vehicle The adjustment coefficient of the startup speed, 0 < ≤ 1; For each car , based on the main vehicle The position of the slave vehicle is calculated Location , , ,in, For the main vehicle With the car The set distance between For the slave car With the main vehicle The relative angle between For each of the slave vehicles , requiring the main vehicle Keep the deviation between the directions ,in, For the slave car Relative to the main vehicle Posture adjustment angle; For each of the slave vehicles , according to the main vehicle The relative position relationship between the speed and the vehicle is set from the initial speed of the vehicle ,in, For the slave car With the main vehicle The distance error between is the adjustment factor used to control the sensitivity of speed changes with distance; Perform status checks on all the unmanned vehicles to confirm whether each of the unmanned vehicles is in a preset position and status.

4. The unmanned vehicle cluster control method based on dynamic master-slave switching according to claim 1 is characterized in that: After the master-slave switching is completed, the obstacle avoidance and regression algorithm based on the dynamic window is used to readjust the formation, including: Recalculate the ideal path and set a new target point based on the current status and mission objectives of the candidate vehicle ; The current linear speed of the candidate main vehicle and angular velocity Based on this, define the range of the dynamic window; Limiting the actual range of the dynamic window according to defined constraints, wherein the constraints include the maximum linear velocity, the minimum linear velocity, the maximum angular velocity, and the minimum angular velocity of the candidate host vehicle; Calculate the evaluation function for evaluating the dynamic window ,in, Used to evaluate the angle between the vehicle and the target direction, Used to assess the safe distance between the vehicle and obstacles. Used to measure speed. Used to calculate the proximity between the current path and the ideal target path, is the standardization factor, , , and is the weight coefficient; The evaluation function is selected The speed and angular velocity combination that maximizes the value is taken as the current motion plan; Using sensor data to detect surrounding obstacles in real time, and adjusting the motion plan according to the relative position and size of the obstacles; If the current path in the motion plan deviates from the ideal path, the linear speed of the candidate vehicle is adjusted by a regression algorithm. and angular velocity ,in, is the error value between the current path and the ideal path, is the direction error, is the proportional control gain; According to the adjusted motion plan, current control instructions are generated to readjust the formation.

5. The unmanned vehicle cluster control method based on dynamic master-slave switching according to claim 1 is characterized in that: After the master-slave switch is completed, it also includes: Continuously monitor the status of each of the unmanned vehicles, including position, speed, angle, and distance from the target, so that each of the unmanned vehicles maintains a corresponding relative position in the formation; Using sensor data to continuously update the status of the unmanned vehicles and assess the stability of the formation; Adjust the control strategy according to the state of the unmanned vehicle and environmental changes.

6. An unmanned vehicle cluster control system based on dynamic master-slave switching, characterized in that: include: The communication architecture module is used to establish a communication priority mechanism and give high priority to information related to master-slave switching; An initialization module, used to initialize the unmanned vehicles, select a formation, and determine the initial position and speed of each of the unmanned vehicles; A decision module, used to collect and monitor the status data of the unmanned vehicle through multi-sensor fusion technology, calculate the fitness function value, and determine whether it is necessary to switch the main vehicle; A handover preparation module, used to establish a multi-layer fitness evaluation method, identify the most suitable vehicle as the new host vehicle, and generate a handover preparation signal; The smooth transition module is used to coordinate the original host vehicle and the candidate host vehicle to perform role conversion through smooth transition; The adjustment module is used to readjust the formation of the team based on the obstacle avoidance and regression algorithm of the dynamic window after the master-slave switch is completed; The operations performed by the decision module include: Collecting surrounding environment data of each unmanned vehicle through a variety of sensors, and filtering the surrounding environment data; For each of the unmanned vehicles, a fitness function is calculated to quantify its performance in the current environment and task. ,in, is the distance from the unmanned vehicle to the target point, is the current speed of the unmanned vehicle, is the deviation of the unmanned vehicle from the ideal formation path, It is an environmental safety factor calculated based on data from multiple sensors. is the remaining power or fuel volume of the unmanned vehicle, , , , and is the performance coefficient; If one of the vehicles is detected to be suitable for , the current slave vehicle is taken as a candidate master vehicle and it is determined that a master vehicle switch is required, wherein, For fitment from the car, For the suitability of the main vehicle, is the fitness threshold; The operations performed by the switching preparation module include: Calculate the actual distance between the original host vehicle and the candidate host vehicle ; Set the preset ideal relative distance between the original host vehicle and the candidate host vehicle ; Calculate relative distance error ; Set distance error threshold ,like , it is considered that the distance between the candidate main vehicle and the original main vehicle meets the requirement; Calculate the expected relative orientation of the original vehicle and the candidate vehicle ,in, is the ideal relative angle between the original host vehicle and the candidate host vehicle; Calculate the actual orientation of the candidate vehicle ; Calculate relative angle error ; Setting the angle error threshold ,like , it is considered that the orientation between the candidate main vehicle and the original main vehicle meets the requirements; When the distance and direction between the candidate main vehicle and the original main vehicle meet the requirements, it is determined that the main vehicle needs to be switched; Generate a switching preparation signal, and send the switching preparation signal to the candidate master vehicle and other slave vehicles through a corresponding priority communication channel; After determining that the main vehicle needs to be switched, the switching preparation module may further perform the following operations: Through the obstacle data obtained by multi-sensor fusion, it is judged whether the environmental safety factor of the area where the candidate main vehicle is located meets the standard. If it is safe, the switch continues; Performing an energy endurance check on the candidate main vehicle, and continuing to switch if the remaining energy of the candidate main vehicle meets the task requirements; The operations performed by the smooth transition module include: Adjust the speed of the candidate vehicle based on the relative position with the original vehicle and the target position. , the calculation formula is ,in, The candidate main vehicle The adjustment speed, is the ideal relative distance set between the master vehicle and the slave vehicle, is the ideal relative angle set between the master vehicle and the slave vehicle, ( , ) is the actual relative position difference between the original host vehicle and the candidate host vehicle, is the angular velocity of the original vehicle, is the speed of the original owner vehicle, and is the adjustment factor; Adjust the angular velocity of the candidate vehicle , the calculation formula is ,in, is the relative angle between the candidate host vehicle and the original host vehicle, is the relative angle between the original owner vehicle and one of the slave vehicles, and is the adjustment factor; The candidate host vehicle is determined based on the calculated speed and angular velocity Gradually adjust position and posture to make smooth transitions.

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