A trajectory planning evaluation system for unmanned systems

Abstract

A trajectory planning and evaluation system for an unmanned system, the present invention relates to a trajectory planning and evaluation system for an unmanned system. The present invention aims to solve the problem that the existing trajectory planning method does not consider efficiency and resource utilization in complex and changeable environments, sensor failures, incomplete models, etc., resulting in a planned trajectory that wastes time and energy and reduces the benefits of the unmanned system. The system includes: a positioning perception module for locating the unmanned system and obstacles, and obtaining the position coordinates of the unmanned system and obstacles; a trajectory planning module for obtaining the planned trajectory of the unmanned system according to the position coordinates of the unmanned system and obstacles, optimizing the planned trajectory of the unmanned system, obtaining the optimal planned trajectory, and transmitting the optimal planned trajectory to a tracking control module; a tracking control module for commanding a tracking controller to control the unmanned system according to the optimal planned trajectory to complete the path planning of the unmanned system. The present invention is used in the field of trajectory planning of unmanned systems.

Description

Technical Field

[0001] The present invention relates to the field of trajectory planning for unmanned systems, and in particular to a trajectory planning evaluation system for unmanned systems. Background Art

[0002] In recent years, with the rapid development of artificial intelligence and Internet technology, the field of unmanned systems is undergoing a profound revolution. At this stage, unmanned vehicles, drones and other systems with increasing intelligence are gradually reducing their dependence on operators and expanding the range of human perception and action capabilities. Trajectory planning, as the core technology of unmanned systems, is widely used in fields such as autonomous driving, unmanned aerial vehicles, industrial automation and mobile robots. Its main task is to plan the movement path of robots, vehicles or robotic arms to ensure that they perform tasks efficiently and safely in complex environments. This technology helps to improve the efficiency of automated systems, reduce errors, and plays a vital role in fields such as medicine, logistics, entertainment and military.

[0003] However, just as the development of technology is accompanied by challenges and problems, trajectory planning is no exception. Complex and changing environments, sensor failures, incomplete models and other reasons may cause the planned trajectory to fail to meet the requirements, which in turn leads to a series of problems. First, the robot, vehicle or equipment may not perform the task as expected, which may affect the completion time and quality of the task. Secondly, trajectory planning that does not meet the requirements may cause collisions, blockages or other safety issues, increasing the risk of accidents. In addition, if trajectory planning does not take efficiency and resource utilization into consideration, time and energy may be wasted, reducing the benefits of the system. Therefore, accurate and reliable trajectory planning is crucial to the normal operation of unmanned systems, and multiple factors such as mission objectives, environmental conditions and safety need to be considered comprehensively to ensure that the generated trajectory can achieve the expected results. Summary of the invention

[0004] The purpose of the present invention is to solve the problem that the existing trajectory planning methods do not consider efficiency and resource utilization in complex and changeable environments, sensor failures, incomplete models, etc., resulting in a waste of time and energy and reducing the benefits of the unmanned system. A trajectory planning evaluation system for unmanned systems is proposed.

[0005] A trajectory planning evaluation system for an unmanned system comprises: a positioning perception module, a trajectory planning module, and a tracking control module;

[0006] The positioning perception module is used to locate the unmanned system and obstacles, obtain the position coordinates of the unmanned system and obstacles, and transmit the position coordinates of the unmanned system and obstacles to the trajectory planning module;

[0007] The trajectory planning module is used to obtain the planned trajectory of the unmanned system according to the position coordinates of the unmanned system and obstacles, optimize the planned trajectory of the unmanned system, obtain the optimal planned trajectory, and transmit the optimal planned trajectory to the tracking control module;

[0008] The tracking control module is used to control the unmanned system according to the optimal planned trajectory command tracking controller to complete the path planning of the unmanned system.

[0009] The beneficial effects of the present invention are:

[0010] The present invention uses a laser radar to locate an unmanned system to obtain the position coordinates of the unmanned system; uses an RRT algorithm to process the position coordinates of the unmanned system obtained by a positioning perception module to obtain a planned trajectory 1 of the unmanned system; uses an RRT* algorithm to process the position coordinates of the unmanned system obtained by the positioning perception module to obtain a planned trajectory 2 of the unmanned system; uses an APF algorithm to process the position coordinates of the unmanned system obtained by the positioning perception module to obtain a planned trajectory 3 of the unmanned system; by calculating the length of the planned trajectory, the tracking time can be evaluated, which helps to improve the efficiency of task execution. By calculating the degree of change of the trajectory curve, the smoothness can be evaluated, which helps to reduce unnecessary shocks and energy waste. By calculating the distance between the unmanned system and the obstacle, the safety of the trajectory can be evaluated to reduce the risk of collision and accidents. In addition, according to different task types, different weight coefficients can be applied to the trajectory length, the degree of curve change, and the distance from the obstacle according to the task requirements to select the optimal trajectory; the driving time is reduced, the energy consumption is reduced, and the controller based on model predictive control is used to control the unmanned system according to the optimal planned trajectory to complete the tracking control of the planned trajectory. The method of the present invention can verify the performance of the planning algorithm, ensure that the generated trajectory meets the task requirements, and thus improve the reliability and applicability of the unmanned system. This not only contributes to the continuous improvement of technology, but also provides a solid foundation for the widespread application of unmanned systems in various fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 Provide a technical architecture diagram for the unmanned system;

[0012] Figure 2 It is the planning trajectory diagram of the RRT algorithm;

[0013] Figure 3 It is the planning trajectory diagram of the RRT* algorithm;

[0014] Figure 4 This is the planning trajectory diagram of the APF algorithm. DETAILED DESCRIPTION

[0015] Specific implementation method 1: In this implementation method, a trajectory planning evaluation system for an unmanned system includes:

[0016] The present invention focuses on the application in the field of unmanned systems such as drones and unmanned vehicles. These systems are composed of three key modules: positioning perception, trajectory planning, and tracking control. Among them, the positioning perception module is responsible for the positioning of unmanned systems and obstacles (obstacles are called environmental impacts), the trajectory planning module performs complex decision-making, path planning, and behavior planning based on the perception information, and the control module receives the planning information and performs corresponding operations. However, due to factors such as the complexity of the environment and possible failures of the sensor, the planned trajectory may not meet the requirements.

[0017] The present invention uses a laser radar to locate an unmanned system and an obstacle to obtain the position coordinates of the unmanned system and the obstacle; uses an RRT algorithm to process the position coordinates of the unmanned system and the obstacle obtained by the positioning perception module to obtain a planned trajectory 1 of the unmanned system; uses an RRT* algorithm to process the position coordinates of the unmanned system and the obstacle obtained by the positioning perception module to obtain a planned trajectory 2 of the unmanned system; uses an APF algorithm to process the position coordinates of the unmanned system and the obstacle obtained by the positioning perception module to obtain a planned trajectory 3 of the unmanned system; by calculating the length of the planned trajectory, the tracking time can be evaluated, which helps to improve the efficiency of task execution. By calculating the degree of change of the trajectory curve, the smoothness can be evaluated, which helps to reduce unnecessary shocks and energy waste. By calculating the distance between the unmanned system and the obstacle, the safety of the trajectory can be evaluated to reduce the risk of collision and accidents. In addition, according to different task types, different weight coefficients can be applied to the trajectory length, the degree of curve change, and the distance from the obstacle according to the task requirements to select the optimal trajectory; the driving time is reduced, the energy consumption is reduced, and the controller based on model predictive control is used to control the unmanned system according to the optimal planned trajectory to complete the tracking control of the planned trajectory. The method of the present invention can verify the performance of the planning algorithm and ensure that the generated trajectory meets the mission requirements, thereby improving the reliability and applicability of the unmanned system. This not only contributes to the continuous improvement of technology, but also provides a solid foundation for the widespread application of unmanned systems in various fields.

[0018] The technical architecture of unmanned systems such as unmanned vehicles and drones is generally divided into three modules: positioning perception, trajectory planning, and tracking control. The three modules are intertwined and together constitute the core of the unmanned system;

[0019] Positioning perception module, trajectory planning module, tracking control module;

[0020] The positioning perception module is used to locate the unmanned system (unmanned vehicle) and obstacles, obtain the position coordinates of the unmanned system and obstacles, and transmit the position coordinates of the unmanned system and obstacles to the trajectory planning module;

[0021] The trajectory planning module is used to obtain the planned trajectory of the unmanned system according to the position coordinates of the unmanned system and obstacles, optimize the planned trajectory of the unmanned system, obtain the optimal planned trajectory, and transmit the optimal planned trajectory to the tracking control module;

[0022] The tracking control module is used to control the unmanned system according to the optimal planned trajectory command tracking controller to complete the path planning of the unmanned system.

[0023] Specific implementation method 2: This implementation method is different from the specific implementation method 1 in that the positioning perception module is used to locate the position of the unmanned system and the obstacle, and obtain the position coordinates of the unmanned system and the obstacle; the specific process is:

[0024] Laser radar is used to locate the position of unmanned systems and obstacles to obtain their position coordinates.

[0025] The other steps and parameters are the same as those in the first embodiment.

[0026] Specific implementation method three: This implementation method is different from specific implementation methods one or two in that the trajectory planning module is used to obtain the planned trajectory of the unmanned system according to the position coordinates of the unmanned system and obstacles; the specific process is:

[0027] The RRT algorithm is used to process the position coordinates of the unmanned system and obstacles obtained by the positioning perception module to obtain the planning trajectory of the unmanned system 1; the RRT algorithm is a fast random expansion tree algorithm;

[0028] The RRT* algorithm is used to process the position coordinates of the unmanned system and obstacles obtained by the positioning perception module to obtain the planned trajectory 2 of the unmanned system; the RRT* algorithm is an asymptotically optimal algorithm;

[0029] The APF algorithm is used to process the position coordinates of the unmanned system and obstacles obtained by the positioning perception module to obtain the planned trajectory 3 of the unmanned system; the APF algorithm is an artificial potential field method path planning algorithm.

[0030] The other steps and parameters are the same as those in the first or second embodiment.

[0031] Specific implementation method 4: This implementation method is different from any one of specific implementation methods 1 to 3 in that the trajectory planning module is used to obtain the planned trajectory of the unmanned system according to the position coordinates of the unmanned system and the obstacle, and optimize the planned trajectory of the unmanned system to obtain the optimal planned trajectory; the specific process is:

[0032] 1. Calculate the length of the unmanned system planned trajectory, the curve change rate of the unmanned system planned trajectory, and the distance between the unmanned system planned trajectory and the obstacle;

[0033] 2. Based on the length of the unmanned system planned trajectory, the curve change rate of the unmanned system planned trajectory, and the distance between the unmanned system planned trajectory and the obstacle, the score of the kth planned trajectory is obtained;

[0034] 3. Obtain the scores of N planned trajectories, and select the planned trajectory with the highest score among the N planned trajectory scores as the optimal trajectory.

[0035] The other steps and parameters are the same as those in Specific Embodiments 1 to 3.

[0036] Specific implementation method five: This implementation method is different from one of the specific implementation methods one to four in that the length of the unmanned system planned trajectory, the curve change rate of the unmanned system planned trajectory, and the distance between the unmanned system planned trajectory and the obstacle are calculated in the first embodiment; the specific process is:

[0037] Trajectory planning evaluation index design, the present invention evaluates the tracking time by calculating the length of the planned trajectory, evaluates the smoothness by calculating the rate of change of the trajectory curve, and evaluates the safety of the trajectory by calculating the distance between the unmanned system and the obstacle.

[0038] 1. Calculate the length of the planned trajectory of the unmanned system; the specific process is:

[0039] The planning trajectory is a series of planning points, and the distances between planning points are relatively close. The length L of the path can be approximated by calculating the sum of the lengths between adjacent planning points. The expression is:

[0040]

[0041] Among them, n is the total number of planning points, p i and p i+1 are the positions of the i-th and i+1-th planning points, respectively, ||p i+1 -p i ||2 represents the Euclidean distance between the i-th and i+1-th planning points, and || ||2 is the binary norm.

[0042] 1.2. Calculate the curve change rate of the planned trajectory of the unmanned system; the specific process is:

[0043] Smoothness can be measured by the derivative of acceleration. The smaller the absolute value of the derivative of acceleration, the smaller the change in the trajectory and the smoother the trajectory.

[0044] The curve change rate of the planned trajectory is expressed by the sum of the derivatives of the acceleration on the trajectory. The formula for the sum of the derivatives of the acceleration on the trajectory is:

[0045]

[0046] Among them, y iIndicates the ordinate value of the i-th planning point, y i+1 Indicates the ordinate value of the i+1th planning point, y i+2 Indicates the ordinate value of the i+2th planning point, y i+3 Indicates the ordinate value of the i+3th planning point, T c represents the sampling time, J represents the sum of the derivatives of the acceleration on the trajectory;

[0047] 13. Calculate the distance between the planned trajectory of the unmanned system and the obstacle; the specific process is:

[0048] The distance between the planned trajectory and the obstacle is expressed as

[0049]

[0050] Where m represents the number of obstacles, p oj represents the position of the jth obstacle, ||p i -p oj ||2 represents the Euclidean distance between the i-th planning point and the j-th obstacle;

[0051] Considering the actual situation, when the obstacle is far away from the current planning point, it can be considered that the obstacle will not affect the unmanned system.

[0052] When the distance between the obstacle and the current planning point exceeds 5m, it is assumed that the obstacle will not affect the unmanned system, that is, at this time ||p i -p oj ||2=0.

[0053] The other steps and parameters are the same as those in Specific Embodiments 1 to 4.

[0054] Specific implementation method 6: This implementation method is different from the first one of the specific implementation methods 1 to 5 in that the score of the kth planned trajectory is obtained based on the length of the unmanned system planned trajectory, the curve change rate of the unmanned system planned trajectory and the distance between the unmanned system planned trajectory and the obstacle; the specific process is:

[0055] Evaluation rule design for trajectory planning quality: When comparing N planned paths, based on the above three indicators, the scoring rules shown in Table 1 are given to evaluate the planned paths. L , S J and S D Represents the scores of the three indicators, s Lk represents the length index score of the k-th planned trajectory, s Jk represents the curve change rate index score of the k-th planning trajectory, s Dk represents the distance index score between the k-th planned trajectory and the obstacle;

[0056] Table 1 Evaluation indicators and scores

[0057]

[0058]

[0059] 2. Calculate the length index score of the k-th planned trajectory, the curve change rate index score of the k-th planned trajectory, and the distance index score between the k-th planned trajectory and the obstacle respectively; the specific process is:

[0060] According to formulas (1)-(3), the smaller L, the smaller J and the larger D, the higher the corresponding trajectory quality. Lk 、s Jk and Dk The calculation formula is as follows:

[0061]

[0062] Among them, L k represents the length of the kth planned trajectory, L max represents the maximum length of N planned trajectories, k = 1, 2, ..., N; J k represents the sum of the absolute values ​​of the acceleration derivatives of the k-th planned trajectory, J max represents the maximum value of the sum of the absolute values ​​of the acceleration derivatives in the N planned trajectories; D k Represents the distance between the kth planned trajectory and the obstacle, max N {D k} represents the maximum value of the distance between N planned trajectories and obstacles, min N {D k} represents the minimum distance between N planned trajectories and obstacles, s Lk represents the length index score of the k-th planned trajectory, s Jk represents the curve change rate index score of the k-th planning trajectory, s Dk represents the distance index score between the k-th planned trajectory and the obstacle;

[0063] 22. Based on the length index score of the k-th planned trajectory, the curve change rate index score of the k-th planned trajectory, and the distance index score between the k-th planned trajectory and the obstacle, the score of the k-th planned trajectory is obtained.

[0064] The other steps and parameters are the same as those in Specific Implementation Methods 1 to 5.

[0065] Specific implementation method seven: This implementation method is different from specific implementation methods one to six in that, in the second implementation method, the score of the kth planned trajectory is obtained based on the length index score of the kth planned trajectory, the curve change rate index score of the kth planned trajectory, and the distance index score between the kth planned trajectory and the obstacle; the expression is:

[0066] s k =w1×s Lk +w2×s Jk +w3×s Dk

[0067] Among them, w1, w2 and w3 are weight coefficients, s k is the score of the k-th trajectory.

[0068] To summarize, according to different task types and requirements, for the paths planned by different methods or different stages, the weight coefficients of w1, w2 and w3 can be adjusted or selected according to the task requirements or habitual preferences, and according to the above score calculation rules, the planned path with the highest score under the current task requirements or habitual preferences can be obtained, thereby evaluating or selecting the current optimal planned trajectory of the unmanned system.

[0069] The other steps and parameters are the same as those in Specific Embodiments 1 to 6.

[0070] Specific implementation eight: This implementation differs from any one of specific implementations one to seven in that the tracking control module is used to control the unmanned system according to the optimal planned trajectory command tracking controller to complete the path planning of the unmanned system; the specific process is:

[0071] A controller based on model predictive control (MPC) is used to control the unmanned system according to the optimal planned trajectory to complete the path planning of the unmanned system.

[0072] The other steps and parameters are the same as those in Specific Embodiments 1 to 7.

[0073] The following examples are used to verify the beneficial effects of the present invention:

[0074] Embodiment 1:

[0075] In order to verify and demonstrate the effectiveness of the planning trajectory evaluation method of the present invention, the present invention will be verified using an unmanned vehicle as the background, and the quality of different trajectories planned by the unmanned vehicle in a complex environment will be compared and analyzed.

[0076] Assume that there are several circular and rectangular obstacles randomly distributed in the environment;

[0077] Use laser radar to locate the unmanned system and obtain the position coordinates of the unmanned system and obstacles;

[0078] The vehicle is planned using the RRT algorithm, RRT* algorithm and APF algorithm respectively, and the planned paths are as follows: Figure 2 , Figure 3 and Figure 4 shown.

[0079] The planning trajectories obtained by the three algorithms are calculated, L1 = 46.32, L2 = 42.15, L3 = 42.05, so L max =46.32, J1=0.15, J2=0.03, J3=0.03, J max =0.15, D1 = 5487.63, D2 = 3285.43, D3 = 4234.64, then we know max3{D i}=5487.63,min3{D i}=3285.43.

[0080] From the above, we can get s L1 =1,s L2 =0.91, s L3 =0.908, s J1 =1,s J2 =0.2,s J3 =0.2,s D1 =1,s D2 =0.43, s D3 =0, take w1=w2=w3=1 / 3, then s1=1,s2=0.513,s3=0.369.

[0081] Therefore, it can be seen that the trajectory planned by the APF algorithm has higher quality and is selected as the optimal trajectory.

[0082] A controller based on model predictive control is used to control the unmanned system according to the optimal planned trajectory to complete the path planning of the unmanned system.

[0083] The present invention may also have many other embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art may make various corresponding changes and modifications based on the present invention, but these corresponding changes and modifications should all fall within the scope of protection of the claims attached to the present invention.

Claims

1. A trajectory planning evaluation system for an unmanned system, characterized in that: The system includes: a positioning perception module, a trajectory planning module, and a tracking control module; The positioning perception module is used to locate the unmanned system and obstacles, obtain the position coordinates of the unmanned system and obstacles, and transmit the position coordinates of the unmanned system and obstacles to the trajectory planning module; The trajectory planning module is used to obtain the planned trajectory of the unmanned system according to the position coordinates of the unmanned system and obstacles, optimize the planned trajectory of the unmanned system, obtain the optimal planned trajectory, and transmit the optimal planned trajectory to the tracking control module; The tracking control module is used to control the unmanned system according to the optimal planned trajectory command tracking controller to complete the path planning of the unmanned system; The trajectory planning module is used to obtain the planned trajectory of the unmanned system according to the position coordinates of the unmanned system and obstacles, optimize the planned trajectory of the unmanned system, and obtain the optimal planned trajectory; the specific process is:

1. Calculate the length of the unmanned system planned trajectory, the curve change rate of the unmanned system planned trajectory, and the distance between the unmanned system planned trajectory and the obstacle; 2. Based on the length of the unmanned system planned trajectory, the curve change rate of the unmanned system planned trajectory, and the distance between the unmanned system planned trajectory and the obstacle, the score of the kth planned trajectory is obtained; 3. Obtain the scores of N planned trajectories, and select the planned trajectory with the highest score among the N planned trajectory scores as the optimal trajectory; The first step is to calculate the length of the unmanned system planned trajectory, the curve change rate of the unmanned system planned trajectory, and the distance between the unmanned system planned trajectory and the obstacle; the specific process is:

1. Calculate the length of the planned trajectory of the unmanned system; the specific process is: The planning trajectory is a continuous planning point. The length L of the path is approximated by calculating the sum of the lengths between adjacent planning points. The expression is: Among them, n is the total number of planning points, p i and p i+1 are the positions of the i-th and i+1-th planning points, respectively, ||p i+1 -p i ||2 represents the Euclidean distance between the i-th and i+1-th planning points, and || ||2 is the second norm; 1.

2. Calculate the curve change rate of the planned trajectory of the unmanned system; the specific process is: The curve change rate of the planned trajectory is expressed by the sum of the derivatives of the acceleration on the trajectory. The formula for the sum of the derivatives of the acceleration on the trajectory is: Among them, y i Indicates the ordinate value of the i-th planning point, y i+1 Indicates the ordinate value of the i+1th planning point, y i+2 Indicates the ordinate value of the i+2th planning point, y i+3 Indicates the ordinate value of the i+3th planning point, T c represents the sampling time, J represents the sum of the derivatives of the acceleration on the trajectory; 13. Calculate the distance between the planned trajectory of the unmanned system and the obstacle; the specific process is: The distance between the planned trajectory and the obstacle is expressed as Where m represents the number of obstacles, p oj represents the position of the jth obstacle, ||p i -p oj ||2 represents the Euclidean distance between the i-th planning point and the j-th obstacle; When the distance between the obstacle and the current planning point exceeds 5m, it is assumed that the obstacle will not affect the unmanned system, that is, at this time ||p i -p oj ||2=0; In the second method, the score of the kth planned trajectory is obtained based on the length of the unmanned system planned trajectory, the curve change rate of the unmanned system planned trajectory, and the distance between the unmanned system planned trajectory and the obstacle; the specific process is:

2. Calculate the length index score of the k-th planned trajectory, the curve change rate index score of the k-th planned trajectory, and the distance index score between the k-th planned trajectory and the obstacle respectively; the specific process is: s Lk 、s Jk and Dk The calculation formula is as follows: Among them, L k represents the length of the kth planned trajectory, L max represents the maximum length of N planned trajectories, k = 1, 2, ..., N; J k represents the sum of the absolute values ​​of the acceleration derivatives of the k-th planned trajectory, J max represents the maximum value of the sum of the absolute values ​​of the acceleration derivatives in the N planned trajectories; D k Represents the distance between the kth planned trajectory and the obstacle, max N {D k } represents the maximum value of the distance between N planned trajectories and obstacles, min N {D k } represents the minimum distance between N planned trajectories and obstacles, s Lk represents the length index score of the k-th planned trajectory, s Jk represents the curve change rate index score of the k-th planning trajectory, s Dk represents the distance index score between the k-th planned trajectory and the obstacle; 22. Based on the length index score of the k-th planned trajectory, the curve change rate index score of the k-th planned trajectory, and the distance index score between the k-th planned trajectory and the obstacle, the score of the k-th planned trajectory is obtained.

2. The trajectory planning and evaluation system for an unmanned system according to claim 1, characterized in that: The positioning perception module is used to locate the position of the unmanned system and obstacles, and obtain the position coordinates of the unmanned system and obstacles; the specific process is: Laser radar is used to locate the position of unmanned systems and obstacles to obtain their position coordinates.

3. The trajectory planning and evaluation system for an unmanned system according to claim 2, characterized in that: The trajectory planning module is used to obtain the planned trajectory of the unmanned system according to the position coordinates of the unmanned system and obstacles; the specific process is: The RRT algorithm is used to process the position coordinates of the unmanned system and obstacles obtained by the positioning perception module to obtain the planned trajectory of the unmanned system 1; The RRT* algorithm is used to process the position coordinates of the unmanned system and obstacles obtained by the positioning perception module to obtain the planned trajectory of the unmanned system 2; The APF algorithm is used to process the position coordinates of the unmanned system and obstacles obtained by the positioning perception module to obtain the planned trajectory of the unmanned system 3.

4. The trajectory planning and evaluation system for an unmanned system according to claim 3, characterized in that: The score of the k-th planned trajectory is obtained based on the length index score of the k-th planned trajectory, the curve change rate index score of the k-th planned trajectory, and the distance index score between the k-th planned trajectory and the obstacle; the expression is: s k =w1×s Lk +w2×s Jk +w3×s Dk Among them, w1, w2 and w3 are weight coefficients, s k is the score of the kth trajectory.

5. The trajectory planning and evaluation system for an unmanned system according to claim 4, characterized in that: The tracking control module is used to control the unmanned system according to the optimal planned trajectory command tracking controller to complete the path planning of the unmanned system; the specific process is: A controller based on model predictive control is used to control the unmanned system according to the optimal planned trajectory to complete the path planning of the unmanned system.

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