Trajectory planning methods, devices, aircraft, and computer-readable storage media

Abstract

This application discloses a trajectory planning method, apparatus, aircraft, and computer-readable storage medium. The method includes: generating a critical path point set based on static obstacles corresponding to the aircraft, the local trajectory of the previous frame, and the global navigation path; determining a control point set of a cubic B-spline curve based on the user-set speed corresponding to the aircraft, the critical path point set, and a cubic B-spline basis function; determining a dynamic obstacle avoidance penalty function based on dynamic obstacles and the control point set, determining a static obstacle avoidance penalty function based on the obstacle avoidance path point set, and determining an optimization model based on the dynamic and static obstacle avoidance penalty functions; and generating a target trajectory corresponding to the aircraft based on the optimization model and the current flight parameters of the aircraft. This application achieves the goal of avoiding both static and dynamic threats, improves the accuracy of flight trajectories, and enhances the stability, comfort, and safety of aircraft flight.

Description

Technical Field

[0001] This application relates to the field of aircraft technology, and in particular to a trajectory planning method, apparatus, aircraft, and computer-readable storage medium. Background Technology

[0002] Currently, in the field of multi-rotor aircraft, the application scenarios involving non-cooperative static and dynamic obstacles pose a significant challenge to the autonomous and safe flight of flying cars. Furthermore, the limited maneuverability of aircraft makes obstacle avoidance even more difficult. Therefore, it is necessary to accurately plan the trajectory of the aircraft.

[0003] In related technologies, trajectory planning can be categorized into sampling-based, search-based, optimization-based, and data-based methods. Sampling-based methods randomly scatter points in the configuration space, connect them with line segments to construct a route map or route tree, and then search for the optimal path. However, sampling and node update strategies affect time efficiency and path quality, and they struggle to handle dynamic obstacles. Search-based methods discretize the state space into a 3D grid map at a certain resolution and use heuristic search algorithms to obtain feasible or optimal paths. However, resolution and heuristic search algorithms affect search efficiency, and they struggle to handle continuous spaces with high space and time complexity. Optimization-based methods establish multi-constraint optimization models and use gradient descent to solve for the optimal trajectory. The objectives and constraints are clearly defined and have wide applicability, but non-convex optimization problems are time-consuming, and the solution quality is greatly affected by initial values. Data-based methods construct large deep neural network models using deep learning and reinforcement learning methods, train them with large amounts of data, and then input aircraft state and environmental information into the large model to generate trajectories. They have strong adaptability and generalization capabilities, but their trajectory quality is greatly affected by data quality, and the model's interpretability is poor.

[0004] Therefore, improving the accuracy of aircraft flight trajectories is a problem that urgently needs to be solved. Summary of the Invention

[0005] The main objective of this application is to provide a trajectory planning method, apparatus, aircraft, and computer-readable storage medium, aiming to solve the technical problem of how to improve the accuracy of aircraft flight trajectories.

[0006] To achieve the above objectives, this application provides a trajectory planning method, comprising:

[0007] Based on the static obstacles corresponding to the aircraft, the local trajectory of the previous frame, and the global navigation path, a set of critical path points is generated, wherein the set of critical path points includes the set of obstacle avoidance path points corresponding to the static obstacles.

[0008] Based on the user-defined speed, critical path point set, and cubic B-spline basis function of the aircraft, determine the control point set of the cubic B-spline curve.

[0009] Based on dynamic obstacles and control point sets, a dynamic obstacle avoidance penalty function is determined, a static obstacle avoidance penalty function is determined based on obstacle avoidance path point sets, and an optimization model is determined based on the dynamic obstacle avoidance penalty function and the static obstacle avoidance penalty function.

[0010] Based on the optimization model and the current flight parameters of the aircraft, the target trajectory corresponding to the aircraft is generated.

[0011] Furthermore, to achieve the above objectives, this application also provides an aircraft, the aircraft comprising:

[0012] The first generation module is used to generate a set of critical path points based on the static obstacles corresponding to the aircraft, the local trajectory of the previous frame, and the global navigation path, wherein the set of critical path points includes the set of obstacle avoidance path points corresponding to the static obstacles.

[0013] The first determining module is used to determine the control point set of the cubic B-spline curve based on the user-defined speed, critical path point set, and cubic B-spline basis function corresponding to the aircraft.

[0014] The second determination module is used to determine the dynamic obstacle avoidance penalty function based on the dynamic obstacle and the control point set, determine the static obstacle avoidance penalty function based on the obstacle avoidance path point set, and determine the optimization model based on the dynamic obstacle avoidance penalty function and the static obstacle avoidance penalty function.

[0015] The second generation module is used to generate the target trajectory corresponding to the aircraft based on the optimization model and the current flight parameters of the aircraft.

[0016] In addition, to achieve the above objectives, this application also provides a trajectory planning device, which includes: a memory, a processor, and a trajectory planning program stored in the memory and executable on the processor. When the trajectory planning program is executed by the processor, it implements the steps of the aforementioned trajectory planning method.

[0017] In addition, to achieve the above objectives, this application also provides a computer-readable storage medium storing a trajectory planning program, which, when executed by a processor, implements the steps of the aforementioned trajectory planning method.

[0018] This application generates a critical path point set based on static obstacles corresponding to the aircraft, the local trajectory of the previous frame, and the global navigation path. Then, based on the user-set speed of the aircraft, the critical path point set, and the cubic B-spline basis function, it determines the control point set of the cubic B-spline curve. Next, based on dynamic obstacles and the control point set, it determines a dynamic obstacle avoidance penalty function, and based on the obstacle avoidance path point set, it determines a static obstacle avoidance penalty function. Finally, based on the dynamic and static obstacle avoidance penalty functions, it determines an optimization model. Then, based on the optimization model and the aircraft's current flight parameters, it generates the target trajectory corresponding to the aircraft. By generating a safe topology path using obstacle avoidance paths for static threats to establish a static obstacle avoidance penalty function, and by using dynamic obstacle data to establish a dynamic obstacle avoidance penalty function that reduces the impact of downwash interference for dynamic threats, and by constructing a multi-constraint optimization model based on static and dynamic obstacle avoidance requirements to generate a safe local trajectory, it achieves the goal of avoiding both static and dynamic threats, improves the accuracy of the flight trajectory, and enhances the stability, comfort, and safety of the aircraft's flight. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0020] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating an embodiment of the trajectory planning method of this application.

[0022] Figure 2 This is a schematic diagram of a backup intersection scenario in an embodiment of the trajectory planning method of this application;

[0023] Figure 3 This is a schematic diagram of the critical path point set in an embodiment of the trajectory planning method of this application;

[0024] Figure 4 This is a schematic diagram of a scenario provided in yet another embodiment of the trajectory planning method of this application;

[0025] Figure 5 This is a schematic diagram of a scenario provided in another embodiment of the trajectory planning method of this application;

[0026] Figure 6 This is a schematic diagram of a scenario provided in another embodiment of the trajectory planning method of this application;

[0027] Figure 7 This is a schematic diagram of the modular structure of the aircraft in this application;

[0028] Figure 8 This is a schematic diagram of the module structure of the trajectory planning device according to an embodiment of this application.

[0029] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0030] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0031] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0032] The main solution of this application is as follows: Based on the static obstacles corresponding to the aircraft, the local trajectory of the previous frame, and the global navigation path, a critical path point set is generated, wherein the critical path point set includes the obstacle avoidance path point set corresponding to the static obstacles; Based on the user-set speed corresponding to the aircraft, the critical path point set, and the cubic B-spline basis function, a control point set of the cubic B-spline curve is determined; Based on the dynamic obstacles and the control point set, a dynamic obstacle avoidance penalty function is determined, a static obstacle avoidance penalty function is determined based on the obstacle avoidance path point set, and an optimization model is determined based on the dynamic obstacle avoidance penalty function and the static obstacle avoidance penalty function; Based on the optimization model and the current flight parameters of the aircraft, the target trajectory corresponding to the aircraft is generated.

[0033] Currently, in the field of multi-rotor aircraft, the application scenarios involving non-cooperative static and dynamic obstacles pose a significant challenge to the autonomous and safe flight of flying cars. Furthermore, the limited maneuverability of aircraft makes obstacle avoidance even more difficult. Therefore, it is necessary to accurately plan the trajectory of the aircraft.

[0034] In related technologies, trajectory planning can be categorized into sampling-based, search-based, optimization-based, and data-based methods. Sampling-based methods randomly scatter points in the configuration space, connect them with line segments to construct a route map or route tree, and then search for the optimal path. However, sampling and node update strategies affect time efficiency and path quality, and they struggle to handle dynamic obstacles. Search-based methods discretize the state space into a 3D grid map at a certain resolution and use heuristic search algorithms to obtain feasible or optimal paths. However, resolution and heuristic search algorithms affect search efficiency, and they struggle to handle continuous spaces with high space and time complexity. Optimization-based methods establish multi-constraint optimization models and use gradient descent to solve for the optimal trajectory. The objectives and constraints are clearly defined and have wide applicability, but non-convex optimization problems are time-consuming, and the solution quality is greatly affected by initial values. Data-based methods construct large deep neural network models using deep learning and reinforcement learning methods, train them with large amounts of data, and then input aircraft state and environmental information into the large model to generate trajectories. They have strong adaptability and generalization capabilities, but their trajectory quality is greatly affected by data quality, and the model's interpretability is poor. Therefore, improving the accuracy of aircraft flight trajectories is a problem that urgently needs to be solved.

[0035] This application establishes a static obstacle avoidance penalty function by generating a safe topology path through obstacle avoidance for static threats and establishing a dynamic obstacle avoidance penalty function that reduces the impact of downwash interference by utilizing dynamic obstacle data for dynamic threats. By constructing a multi-constraint optimization model based on static and dynamic obstacle avoidance requirements, a safe local trajectory is generated. This achieves the goal of avoiding both static and dynamic threats, improves the accuracy of the flight trajectory, and enhances the stability, comfort, and safety of the aircraft flight.

[0036] It should be noted that the execution subject in this embodiment can be a trajectory planning device, or a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or a trajectory planning device capable of performing the above functions. This embodiment does not specifically limit it in this way. The following uses a trajectory planning device as the execution subject as an example to describe this embodiment and the following embodiments.

[0037] Based on this, this application proposes a trajectory planning method according to the first embodiment, please refer to... Figure 1 The trajectory planning method includes steps S101 to S104:

[0038] Step S101: Based on the static obstacles corresponding to the aircraft, the local trajectory of the previous frame, and the global navigation path, generate a set of critical path points, wherein the set of critical path points includes the set of obstacle avoidance path points corresponding to the static obstacles.

[0039] In this application, during trajectory planning, static obstacle information of static obstacles, the corresponding local trajectory of the aircraft in the previous frame, and the global navigation path are obtained based on the aircraft's perception module. A critical path point set is generated based on the obtained parameters. Specifically, the current position and current speed of the aircraft are obtained, along with the global navigation path and the corresponding local trajectory of the previous frame. Based on the current position, current speed, local trajectory of the previous frame, and global navigation path, a fallback path is determined. Static obstacle information of the static obstacles corresponding to the aircraft is obtained. Based on the static obstacle information and the fallback path, a fallback path point set (the set of points formed by the fallback path) is determined. A critical path point set is generated based on the fallback path point set and the initial path point set corresponding to the fallback path. The initial path point set is the set of path points formed by the fallback path.

[0040] Step S102: Based on the user-defined speed, critical path point set, and cubic B-spline basis function corresponding to the aircraft, determine the control point set of the cubic B-spline curve;

[0041] After obtaining the critical path point set, the user-defined speed and cubic B-spline basis function are obtained. Based on the user-defined speed, critical path point set, and cubic B-spline basis function, the control point set of the cubic B-spline curve is determined. Specifically, the target limit speed and critical path point time corresponding to each critical path point in the critical path point set are first determined according to the user-defined speed. Then, based on the cubic B-spline basis function, the target limit speed and critical path point time corresponding to each critical path point, the control point set of the cubic B-spline curve is determined.

[0042] Step S103: Based on the dynamic obstacles and control point set, determine the dynamic obstacle avoidance penalty function, based on the obstacle avoidance path point set, determine the static obstacle avoidance penalty function, and determine the optimization model based on the dynamic obstacle avoidance penalty function and the static obstacle avoidance penalty function.

[0043] After obtaining the control point set of the cubic B-spline curve, the dynamic obstacle avoidance penalty function is determined based on the dynamic obstacles and the control point set. Specifically, the minimum and maximum times corresponding to each dynamic obstacle are determined based on the predicted trajectory of each dynamic obstacle and the aircraft trajectory corresponding to the critical path point set. The dynamic obstacle avoidance penalty function is determined based on the current position of the aircraft, the dynamic obstacles, the minimum time, and the maximum time.

[0044] Meanwhile, based on the set of obstacle avoidance path points, the static obstacle avoidance penalty function is determined. In one feasible implementation, step S103 may further include steps S1031 to S1033:

[0045] Step S1031: Based on the obstacle surface points and movement direction in the obstacle bypass path point set, as well as each critical path point and safety distance, determine the first static parameter corresponding to each critical path point.

[0046] Step S1032: Based on the first static parameters, each key path point and the corresponding motion direction, determine the second static parameters corresponding to each key path point;

[0047] Step S1033: Determine the static obstacle avoidance penalty function based on the second static parameter.

[0048] When determining the static obstacle avoidance penalty function, the obstacle surface points and movement directions in the obstacle avoidance path point set are obtained. Based on the obstacle surface points and movement directions in the obstacle avoidance path point set, as well as each critical path point and safety distance, the first static parameter is determined.

[0049] Obtain each critical path point and its corresponding direction of motion. Based on the first static parameter, the critical path points, and their corresponding directions of motion, determine the second static parameter. Based on the second static parameter corresponding to each static obstacle information, determine the static obstacle avoidance penalty function, where the specific formula is as follows:

[0050]

[0051] d so =(P i -P surf，i ) T V surf，i )-d safe

[0052]

[0053]

[0054] Among them, J so P is the static obstacle avoidance penalty function. i Let Q be the i-th critical path point. j For the j-th control point in the control point set, N j,3 (.) represents the cubic B-spline basis functions, d so P is the first static parameter. surf,i V represents the point on the obstacle surface, i.e., the i-th critical path point. surf,i Critical path point P surf,i The corresponding direction of motion, d safe For a safe distance, G i This is the second static parameter.

[0055] After obtaining the dynamic obstacle avoidance penalty function and the static obstacle avoidance penalty function, an optimization model is determined based on the dynamic obstacle avoidance penalty function and the static obstacle avoidance penalty function. Furthermore, in a feasible implementation, step S103 may also include steps S1031 to S1032:

[0056] Step S1031: Determine the trajectory smoothness penalty function based on the critical path point set, and determine the dynamic feasibility penalty function based on the control point set of the cubic B-spline curve;

[0057] Step S1032: Determine the optimization model based on the dynamic obstacle avoidance penalty function, the static obstacle avoidance penalty function, the trajectory smoothness penalty function, and the dynamic feasibility penalty function.

[0058] After obtaining the dynamic obstacle avoidance penalty function and the static obstacle avoidance penalty function, the aircraft determines the trajectory smoothness penalty function based on the critical path point set. Specifically, the trajectory smoothness penalty function is calculated based on the jerk of each critical path point. Furthermore, in a feasible implementation, step S1031 may also include step a:

[0059] Step a: Determine the trajectory smoothness penalty function based on the jerk corresponding to each critical path point in the critical path point set.

[0060] Specifically, the jerk corresponding to each critical path point in the critical path point set is obtained, and a trajectory smoothness penalty function is calculated based on each jerk. The formula for this trajectory smoothness penalty function is as follows:

[0061]

[0062] Among them, J s Let j be the trajectory smoothness penalty function. i Let be the jerk corresponding to the i-th critical path point, n be the number of control points in the control point set, and n-3=l be the number of critical path points.

[0063] Simultaneously, a dynamic feasibility penalty function is determined based on the control point set of the cubic B-spline curve. Specifically, the three-dimensional velocity, Z-axis velocity, three-dimensional acceleration, upper limit of the three-dimensional velocity, maximum Z-axis velocity, and maximum value of the three-axis combined acceleration corresponding to each control point in the control point set are obtained. The dynamic feasibility penalty function is calculated based on the obtained parameters. Furthermore, in a feasible implementation, step S1031 may also include steps b1 to b2:

[0064] Step b1: Obtain the three-dimensional velocity, Z-axis velocity, three-dimensional acceleration, three-dimensional velocity limit, maximum Z-axis velocity, and maximum combined acceleration of each control point in the control point set.

[0065] Step b2: Determine the first penalty function based on the three-dimensional velocity and the upper limit of the three-dimensional velocity, determine the second penalty function based on the Z-axis velocity and the maximum Z-axis velocity, and determine the third penalty function based on the three-dimensional acceleration and the maximum value of the three-axis combined acceleration.

[0066] Step b3: Determine the dynamic feasibility penalty function based on the first penalty function, the second penalty function, and the third penalty function.

[0067] When determining the dynamic feasibility penalty function, obtain the three-dimensional velocity, Z-axis velocity, three-dimensional acceleration, upper limit of three-dimensional velocity, maximum Z-axis velocity, and maximum value of the three-axis combined acceleration for each control point in the control point set.

[0068] Next, a first penalty function is determined based on the three-dimensional velocity and its upper limit; a second penalty function is determined based on the Z-axis velocity and its maximum Z-axis velocity; and a third penalty function is determined based on the three-dimensional acceleration and the maximum value of the combined acceleration across the three axes. Based on the first, second, and third penalty functions, a dynamic feasibility penalty function is determined. By considering the aircraft's Z-axis tracking capability limitations in the velocity decision and establishing penalty functions for the combined three-axis velocity and Z-axis velocity, the aircraft is ensured to track optimized trajectories with steep altitude increases and decreases. The specific formula is as follows:

[0069]

[0070]

[0071]

[0072] J d =J υ +J υ，z +J a ;

[0073] Among them, J d J is the dynamic feasibility penalty function. v J is the first penalty function. v,z J is the second penalty function. a For the third penalty function, v i Let v be the three-dimensional velocity of the i-th control point in the control point set. upper,i Let v be the three-dimensional acceleration of the i-th control point in the control point set. z,i Let v be the Z-axis velocity of the i-th control point in the control point set. max,z The maximum speed along the Z-axis is a. i Let a be the three-dimensional acceleration of the i-th control point in the control point set. max This represents the maximum combined acceleration of the three axes.

[0074] After obtaining the dynamic feasibility penalty function, an optimization model is determined based on the dynamic obstacle avoidance penalty function, static obstacle avoidance penalty function, trajectory smoothness penalty function, and dynamic feasibility penalty function. Penalty functions for path smoothness and dynamic feasibility are established based on non-uniform B-spline control points, and combined with static and dynamic obstacle avoidance penalty terms to construct a multi-constraint optimization model to generate smooth, safe, and executable local trajectories, improving flight stability and comfort. The specific formula for the optimization model is:

[0075] J = w s J s +w d J d +w do J do +w so J so ;

[0076] Where J is the optimization model, J s J is the trajectory smoothness penalty function. d J is the dynamic feasibility penalty function. do J is the dynamic obstacle avoidance penalty function. so w is a static obstacle avoidance penalty function. s 、w d 、w do 、w so These are the weight coefficients corresponding to the trajectory smoothness penalty function, dynamic feasibility penalty function, dynamic obstacle avoidance penalty function, and static obstacle avoidance penalty function, respectively.

[0077] Step S104: Based on the optimization model and the current flight parameters of the aircraft, generate the target trajectory corresponding to the aircraft.

[0078] After obtaining the optimized model, the current flight parameters of the aircraft are obtained, and the target trajectory corresponding to the aircraft is generated based on the current flight parameters and the optimized model. Specifically, the current flight parameters are calculated using common calculation methods in related technologies to obtain the relevant parameters of the optimized model, and the target trajectory corresponding to the aircraft is generated based on the optimized model using the relevant parameters.

[0079] It should be noted that after generating the target trajectory, the velocity and acceleration of the optimal control point corresponding to the target trajectory are determined. It is then determined whether the velocity and acceleration of the optimal control point satisfy the corresponding constraints. If they do, the target trajectory is output, and the target trajectory is sampled to obtain multiple local trajectory sampling points. It is then determined whether each local trajectory sampling point satisfies the safe distance constraints with static and dynamic obstacles in space and time. If all are satisfied, the aircraft performs flight control based on the target trajectory. If any local trajectory sampling point does not satisfy the safe distance constraints with static or dynamic obstacles, a braking trajectory is generated and sent to the aircraft's control module for execution. If the velocity and acceleration of the optimal control point do not satisfy the corresponding constraints, a redistribution time interval is obtained, and the time node vector U of the control point set Q is adjusted based on the redistribution time interval. For example, if the redistribution time interval is Δt, then the adjusted U = [t0 + Δt, t1 + Δt, ..., t m-1 +Δt,t m +Δt], and return to execute step S102 to obtain the new target trajectory.

[0080] By generating a critical path point set based on static obstacles corresponding to the aircraft, the local trajectory of the previous frame, and the global navigation path, and then determining the control point set of the cubic B-spline curve based on the user-set speed of the aircraft, the critical path point set, and the cubic B-spline basis function, a dynamic obstacle avoidance penalty function is then determined based on dynamic obstacles and the control point set. A static obstacle avoidance penalty function is determined based on the obstacle avoidance path point set, and an optimization model is determined based on both the dynamic and static obstacle avoidance penalty functions. Finally, based on the optimization model and the current flight parameters of the aircraft, the target trajectory corresponding to the aircraft is generated. By generating a safe topology path using obstacle avoidance paths for static threats to establish a static obstacle avoidance penalty function, and by using dynamic obstacle data to establish a dynamic obstacle avoidance penalty function that reduces the impact of downwash interference for dynamic threats, a multi-constraint optimization model is constructed based on static and dynamic obstacle avoidance requirements to generate a safe local trajectory. This achieves the goal of avoiding both static and dynamic threats, improves the accuracy of the flight trajectory, and enhances the stability, comfort, and safety of the aircraft's flight.

[0081] In one feasible implementation, step S101 may further include steps S201 to S202:

[0082] Step S201: Determine an alternate path based on the aircraft's current position, current speed, previous frame local trajectory, and global navigation path;

[0083] Step S202: Generate a critical path point set based on the static obstacles corresponding to the aircraft and the initial path point set corresponding to the backup path.

[0084] During trajectory planning, the current position and current velocity of the aircraft are obtained, along with the global navigation path and the corresponding local trajectory of the previous frame. Based on the current position, current velocity, local trajectory of the previous frame, and global navigation path, a fallback path is determined. Specifically, in one feasible implementation, step S201 may include steps S2011 to S2013:

[0085] Step S2011: Based on the current position and current speed of the aircraft, determine the first projection point in the local trajectory of the previous frame;

[0086] Step S2012: Determine the second projection point in the global navigation path of the aircraft corresponding to the last local trajectory point in the previous frame's local trajectory.

[0087] Step S2013: Determine the backup path based on the first projection point, the second projection point, the local trajectory of the previous frame, and the global navigation path.

[0088] After obtaining the current position, current speed, and local trajectory of the previous frame, the current position is projected onto the local trajectory of the previous frame based on the current position and current speed, and the first projection point corresponding to the current aircraft is determined in the local trajectory of the previous frame.

[0089] Simultaneously, the last local trajectory point in the local trajectory of the previous frame is obtained, and the second projection point corresponding to the last local trajectory point is determined in the global navigation path. The second projection point can be the intersection point between the local trajectory of the previous frame and the global navigation path.

[0090] After obtaining the first and second projection points, based on the first and second projection points, the local trajectory of the previous frame, and the global navigation path, a backup path is determined, such as... Figure 2 As shown, the curve below represents the global navigation path, the yellow curve in the middle represents the local trajectory of the previous frame, and the path corresponding to the purple discrete point set is the backup path. When generating the backup path, the aircraft's path length limitation is considered; that is, the backup path is less than or equal to this path length limitation.

[0091] After obtaining the backup path, the static obstacle information corresponding to the aircraft's static obstacles is obtained. Based on the static obstacle information and the backup path, the obstacle avoidance path point set (the set of points formed by the obstacle avoidance path) corresponding to the static obstacle is determined. A critical path point set is generated based on the obstacle avoidance path point set and the initial path point set corresponding to the backup path. The initial path point set is the set of path points formed by the path points in the backup path. Specifically, in a feasible implementation, step S202 may include steps S2021 to S2024:

[0092] Step S2021: Sample the backup paths based on the distance to the critical path points to obtain the initial path point set;

[0093] Step S2022: Based on the static grid map corresponding to the static obstacle information, determine whether there is a collision segment in the initial path point set;

[0094] Step S2023: Determine the obstacle bypass path point set based on the collision segment, wherein the obstacle bypass path point set includes obstacle surface points and corresponding movement directions;

[0095] Step S2024: Generate the critical path point set based on the obstacle avoidance path point set and the initial path point set.

[0096] After obtaining the backup path, the corresponding critical path point distance is obtained. The backup path is sampled based on the critical path point distance to obtain the initial path point set, that is, to generate discrete initial path points.

[0097] After obtaining the initial pathpoint set, static obstacle information corresponding to the aircraft's static obstacles is acquired. A static grid map is then built based on this information. The existence of collision segments in the initial pathpoint set is determined using the static grid map. Specifically, each initial pathpoint in the initial pathpoint set is checked against the static grid map corresponding to the static obstacle. If an initial pathpoint in the initial pathpoint set is present in the static grid map, it is connected to form a collision segment. Based on this collision segment, a set of obstacle avoidance pathpoints is determined. This obstacle avoidance pathpoint set includes obstacle surface points and their corresponding directions of motion, such as... Figure 3 As shown, the dashed line represents the obstacle bypass path formed by the set of obstacle bypass path points. The red solid points in the dashed line are the obstacle bypass path points corresponding to each initial path point in the collision segment. The green curve represents the backup path, and the arrows corresponding to the red solid points represent the movement directions of the obstacle bypass path points.

[0098] Next, based on the obstacle bypass path point set and the initial path point set, a critical path point set is generated. This critical path point set includes obstacle bypass path points in the obstacle bypass path point set and path points in the initial path point set other than the initial path points included in the collision segment.

[0099] By detecting collision segments and searching for safe obstacle avoidance paths corresponding to the collision segments in the static occupied grid map, the time consumption is reduced. The point set of "obstacle surface points - movement direction" (obstacle avoidance path point set) is used to facilitate the establishment of a static obstacle avoidance penalty function, thereby achieving the goal of avoiding static threats.

[0100] The trajectory planning method proposed in this embodiment determines an alternate path based on the aircraft's current position, current speed, previous frame local trajectory, and global navigation path. Then, based on the static obstacles corresponding to the aircraft and the initial path point set corresponding to the alternate path, a critical path point set is generated. By detecting collision segments and searching for safe obstacle avoidance paths corresponding to the collision segments in the static occupied grid map, the critical path point set is obtained, thereby improving the accuracy of the flight trajectory and further enhancing the stability, comfort, and safety of the aircraft's flight.

[0101] In one feasible implementation, step S102 may include steps S301 to S302:

[0102] Step S301: Based on the user-defined speed, determine the target speed limit and critical path time corresponding to each critical path point in the critical path point set.

[0103] Step S302: Based on the cubic B-spline basis function, the target limit speed corresponding to each critical path point, and the critical path point time, determine the control point set of the cubic B-spline curve.

[0104] After obtaining the critical path point set, the user-defined speed is obtained, and based on the user-defined speed, the target limit speed and critical path point time corresponding to each critical path point in the critical path point set are determined. The target limit speed is the upper speed limit corresponding to each critical path point, and the critical path point time is the time when the aircraft reaches each critical path point, obtained according to the target limit speed. Specifically, in a feasible implementation, step S301 may include steps S3011 to S3014:

[0105] Step S3011: Based on the user-defined speed and the upper limit of steep ascent and descent speed, determine the first speed limit corresponding to each critical path point;

[0106] Step S3012: Based on the speed limit and corresponding acceleration limit of the previous critical path point in the first speed limit, determine the speed limit of the current critical path point and obtain the second speed limit corresponding to each critical path point.

[0107] Step S3013: Based on the speed limit of the next critical path point in the second speed limit and the corresponding acceleration limit, determine the speed limit of the current critical path point and obtain the target limit speed corresponding to each critical path point.

[0108] Step S3014: Based on the distance to critical path points and the target speed limit, determine the time set, wherein the time set includes the critical path point time corresponding to each critical path point.

[0109] After obtaining the user-defined speed, the maximum speed for steep ascents and descents is obtained. Based on the user-defined speed and the maximum speed for steep ascents and descents, the first speed limit corresponding to each critical path point is determined, such as... Figure 4 As shown, Figure 4 The upper limit of the speed calculated for steep ascents and descents is the upper limit of the steep ascent and descent speed. The yellow curve represents the speed set by the user. For each critical path point, the upper limit of the first speed at that critical path point is less than or equal to the upper limit of the steep ascent and descent speed, and the upper limit of the first speed at that critical path point is less than or equal to the speed set by the user.

[0110] Next, based on the speed limit and corresponding acceleration limit of the previous critical path point in the first speed limit, the speed limit of the next critical path point is determined, and the second speed limit corresponding to each critical path point is obtained. Specifically, for each critical path point, the speed limit of the previous critical path point is obtained from the first speed limit, and the speed limit of the current critical path point is calculated based on the speed limit of the previous critical path point and the acceleration limit of the aircraft to obtain the second speed limit, such as... Figure 5 As shown.

[0111] After obtaining the second speed limit for each critical path point, the speed limit for the current critical path point is determined based on the speed limit of the next critical path point in the second speed limit and its corresponding acceleration limit. This yields the target speed limit for each critical path point. Specifically, for each critical path point, the speed limit of the next critical path point is obtained from the second speed limit. Based on the speed limit of the next critical path point and the aircraft's acceleration limit, the speed limit of the current critical path point is calculated to obtain the target speed limit. Figure 6 As shown.

[0112] After obtaining the target speed limit, a time set is determined based on the distance to the critical path point and the target speed limit. Specifically, for each critical path point, the critical path point time corresponding to the critical path point is calculated based on the speed limit of the previous critical path point in the target speed limit, the speed limit of the current critical path point, and the distance to the critical path point. Then, the time set is obtained through the critical path point times corresponding to each critical path point.

[0113] After obtaining the target speed limit and critical path point time corresponding to each critical path point, a cubic B-spline basis function is obtained. Based on the cubic B-spline basis function, the target speed limit and critical path point time corresponding to each critical path point, the control point set of the cubic B-spline curve is determined. Specifically, in a feasible implementation, step S302 may include steps S3021 to S3022:

[0114] Step S3021: Based on the target speed limit, obtain the first velocity and first acceleration of the first critical path point in the critical path point set, and the second velocity and second acceleration of the last critical path point in the critical path point set.

[0115] Step S3022: Based on the critical path point set, the first velocity, first acceleration, second velocity, and second acceleration at the critical path point time, determine the control point set of the cubic B-spline curve.

[0116] After obtaining the target speed limit, the first speed V0 of the first critical path point and the second speed V of the last critical path point in the critical path point set are obtained based on the target speed limit. l And obtain the first acceleration A0 of the first critical path point in the critical path point set, and the second acceleration A of the last critical path point in the critical path point set. l .

[0117] Next, based on the critical path point set, the first velocity V0, the first acceleration A0, and the second velocity V at the critical path point time, l With the second acceleration A l Determine the control point set Q of the cubic B-spline curve, where the cubic B-spline basis functions N i,3 (t) is:

[0118]

[0119] The control point set of the cubic B-spline curve is Q = [Q1, Q2, ..., Q...]. n-1 Q n The time node vector of the control point set Q is U = [t0, t1, ..., t]. m-1 ,t m The critical path set is P = [P1, P2, ..., P]. l-1 ,P l The time set is T = [T1, T2, ..., T]. l-1 ,T l ], where n = 1 + 2, m = n + 4, T i =t i+4 -t i+3 ,but:

[0120]

[0121] The parameterized equation for the non-uniform B-spline can then be obtained as follows:

[0122]

[0123] The above formula can be written as BQ = P, where matrix B can be obtained through Pi, V0, A0, V l With Al The formula is obtained by solving the equation, and then the control point set Q of the cubic B-spline curve can be obtained by using QR decomposition to solve the equation.

[0124] The trajectory planning method proposed in this embodiment determines the target speed limit and critical path point time corresponding to each critical path point in the critical path point set based on the user-defined speed. Then, based on the cubic B-spline basis function, the target speed limit and critical path point time corresponding to each critical path point, the control point set of the cubic B-spline curve is determined. By using the theory of non-uniform B-spline parameterization and using the generated high-quality control point set as the initial solution, the speed of solving the optimization problem and the quality of the generated trajectory are improved, thereby improving the accuracy of the flight trajectory and further improving the stability, comfort and safety of the aircraft flight.

[0125] In one feasible implementation, step S103 may include steps S401 to S403:

[0126] Step S401: Based on the first start time and first trajectory duration corresponding to the predicted trajectory of each dynamic obstacle, and the second start time and second trajectory duration corresponding to the aircraft trajectory of the key path point set, determine the minimum time and maximum time corresponding to each dynamic obstacle respectively.

[0127] Step S402: Based on the aircraft's current position, minimum time, maximum time, bounding boxes corresponding to each dynamic obstacle, and safety distance, determine the collision cost corresponding to each dynamic obstacle;

[0128] Step S403: Determine the dynamic obstacle avoidance penalty function based on the collision cost corresponding to each dynamic obstacle.

[0129] After obtaining the control point set of the cubic B-spline curve, the size information and predicted trajectory of the dynamic obstacles are obtained through the aircraft's perception module. Based on the predicted trajectory of each dynamic obstacle, the corresponding first start time and first trajectory duration are obtained. For example, if there are K dynamic obstacles, for the k-th dynamic obstacle, k∈[1,K], its first start time t k,start The corresponding predicted trajectory Φ k Its first trajectory has a duration of T. k Its trajectory ends at time t. k,end =T k -t k,start The critical path point set corresponds to the aircraft trajectory, which can be the aircraft's trajectory at the second starting time t. o,start The generated trajectory, i.e., the trajectory of the spacecraft at the second initial time t o,start The generated trajectory is the aircraft trajectory Φ o Its second trajectory has a duration of T. oIts trajectory ends at time t. o,end =T o -t o,start Specifically, in one feasible implementation, step S401 may include steps S4011 to S4012:

[0130] Step S4011: Based on the first start time and the second start time, determine the minimum time corresponding to each dynamic obstacle;

[0131] Step S4012: Based on the first start time, the first trajectory duration, the second start time, and the second trajectory duration, determine the maximum time corresponding to each dynamic obstacle.

[0132] For the kth dynamic obstacle, there are three possible relationships between it and the aircraft: (1) t k,end ≤t ostart or t kstart >t oend If the kth dynamic obstacle has no intersection with the aircraft, then there is no risk of collision; (2) t o,start ≤t k,start And t k,start <t o,end And t k，end ≥t o,end , or t o,start ≤t k,start And t k，start <t o，end And t k，ennd <t o，end Then the minimum time t min =t k，start Maximum time t max =min{t o，end , t k,end};(3),t o，start ≥t k，start And t o，start <t k，ennd And t k,end ≥t o,end , or t o,start ≥t k，start And t o,start <t k，end And t k，end <t o,end Then the minimum time t min =t o,start Maximum time t max =min{t o,end , t k，end}

[0133] Furthermore, after obtaining the first start time, the second start time, the second start time, and the duration of the second trajectory, the minimum time t corresponding to each dynamic obstacle is determined based on the first start time and the second start time. min Based on the first start time, the duration of the first trajectory, the second start time, and the duration of the second trajectory, the maximum time t corresponding to each dynamic obstacle is determined. max , where t min =max{t o，start , t k,star}, t max =min{t o，start +T o ,t k，star +T k}

[0134] After obtaining the minimum and maximum times corresponding to each dynamic obstacle, the collision cost corresponding to each dynamic obstacle is determined based on the aircraft's current position, minimum and maximum times, the bounding boxes corresponding to each dynamic obstacle, and the safety distance. Specifically, in a feasible implementation, step S402 may include steps S4021 to S4023:

[0135] Step S4021: Based on the current position of the aircraft and the bounding boxes corresponding to each dynamic obstacle, determine the nearest point between the aircraft and each dynamic obstacle, and determine the three-dimensional relative position information between the nearest point and the current position of the aircraft.

[0136] Step S4022: Determine dynamic parameters based on three-dimensional relative position information and safety distance;

[0137] Step S4023: Based on the dynamic parameters, minimum time, and maximum time of each dynamic obstacle, determine the collision cost corresponding to each dynamic obstacle.

[0138] The formula for collision cost is:

[0139] t min =max{t o , t k}

[0140] t max =min{t o +T o , t k +T k}

[0141]

[0142]

[0143]

[0144]

[0145] Among them, J do,k The collision cost is the value corresponding to the k-th dynamic obstacle. Φ represents the current position of the aircraft and the three-dimensional relative position information between the nearest point and the bounding box corresponding to the k-th dynamic obstacle. o (t) represents the trajectory of the aircraft in the t-th minute. k The current position at time t, F(.) is a function that finds the nearest point from the current position to the bounding box and calculates the 3D relative position between the given point and the nearest point, r is the radius of the sphere model corresponding to the aircraft, and d safe For safety, E represents the conversion of Euclidean distance to a shorter Z-axis ellipsoidal distance to reduce downwash interference, d k (t) is t k The first dynamic parameter at time, G k (t) is t k The second dynamic parameter at time t min For the minimum time, t max The maximum moment.

[0146] After obtaining the collision cost, the dynamic obstacle avoidance penalty function is determined based on the collision cost corresponding to each dynamic obstacle. The formula for the dynamic obstacle avoidance penalty function is as follows:

[0147]

[0148] Among them, J do Here, K is the dynamic obstacle avoidance penalty function, and K is the number of dynamic obstacles.

[0149] The trajectory planning method proposed in this embodiment determines the minimum and maximum times corresponding to each dynamic obstacle by using the first start time and first trajectory duration corresponding to the predicted trajectory of each dynamic obstacle, and the second start time and second trajectory duration corresponding to the aircraft trajectory of the critical path point set. Then, based on the aircraft's current position, minimum and maximum times, the bounding boxes corresponding to each dynamic obstacle, and the safety distance, the collision cost corresponding to each dynamic obstacle is determined. Subsequently, based on the collision cost corresponding to each dynamic obstacle, the dynamic obstacle avoidance penalty function is determined. By using the predicted trajectory to establish a dynamic obstacle avoidance penalty function that can reduce the impact of downwash interference, the goal of avoiding dynamic threats is achieved, the accuracy of the flight trajectory is improved, and the stability, comfort, and safety of the aircraft flight are further improved.

[0150] This application also provides an aircraft, please refer to... Figure 7 The aircraft includes:

[0151] The first generation module 10 is used to generate a set of key path points based on the static obstacles corresponding to the aircraft, the local trajectory of the previous frame, and the global navigation path, wherein the set of key path points includes the set of obstacle avoidance path points corresponding to the static obstacles.

[0152] The first determining module 20 is used to determine the control point set of the cubic B-spline curve based on the user-set speed, critical path point set, and cubic B-spline basis function corresponding to the aircraft.

[0153] The second determining module 30 is used to determine a dynamic obstacle avoidance penalty function based on dynamic obstacles and control point set, determine a static obstacle avoidance penalty function based on obstacle avoidance path point set, and determine an optimization model based on the dynamic obstacle avoidance penalty function and the static obstacle avoidance penalty function.

[0154] The second generation module 40 is used to generate the target trajectory corresponding to the aircraft based on the optimization model and the current flight parameters of the aircraft.

[0155] The trajectory planning device provided in this application, employing the trajectory planning method described in the above embodiments, can solve the technical problem of how to improve the accuracy of aircraft flight trajectories. Compared with the prior art, the beneficial effects of the trajectory planning device provided in this application are the same as those of the trajectory planning method provided in the above embodiments, and other technical features in the trajectory planning device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0156] This application provides a trajectory planning device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the trajectory planning method in the first embodiment described above.

[0157] The following is for reference. Figure 8 The diagram illustrates a structural schematic of a trajectory planning device suitable for implementing embodiments of this application. The trajectory planning device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 8The trajectory planning device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0158] like Figure 8 As shown, the trajectory planning device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the trajectory planning device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the trajectory planning device to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows trajectory planning devices with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0159] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0160] The trajectory planning device provided in this application, employing the trajectory planning method in the above embodiments, can solve the technical problem of how to improve the accuracy of aircraft flight trajectories. Compared with the prior art, the beneficial effects of the trajectory planning device provided in this application are the same as those of the trajectory planning method provided in the above embodiments, and other technical features in this trajectory planning device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0161] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0162] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0163] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the trajectory planning method in the above embodiments.

[0164] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0165] The aforementioned computer-readable storage medium may be included in the trajectory planning device; or it may exist independently and not be assembled into the trajectory planning device.

[0166] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the trajectory planning device, the trajectory planning device: generates a critical path point set based on the static obstacles corresponding to the aircraft, the local trajectory of the previous frame, and the global navigation path, wherein the critical path point set includes the obstacle avoidance path point set corresponding to the static obstacles; determines a control point set of a cubic B-spline curve based on the user-set speed corresponding to the aircraft, the critical path point set, and the cubic B-spline basis function; determines a dynamic obstacle avoidance penalty function based on the dynamic obstacles and the control point set, determines a static obstacle avoidance penalty function based on the obstacle avoidance path point set, and determines an optimization model based on the dynamic obstacle avoidance penalty function and the static obstacle avoidance penalty function; and generates a target trajectory corresponding to the aircraft based on the optimization model and the current flight parameters of the aircraft.

[0167] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0168] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0169] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0170] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described trajectory planning method, thereby solving the technical problem of how to improve the accuracy of aircraft flight trajectories. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the trajectory planning method provided in the above embodiments, and will not be repeated here.

[0171] This application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the trajectory planning method described above.

[0172] The computer program product provided in this application can solve the technical problem of how to improve the accuracy of aircraft flight trajectories. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the trajectory planning method provided in the above embodiments, and will not be repeated here.

[0173] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A trajectory planning method, characterized in that, include: Based on the static obstacles corresponding to the aircraft, the local trajectory of the previous frame, and the global navigation path, a set of critical path points is generated, wherein the set of critical path points includes the set of obstacle avoidance path points corresponding to the static obstacles. Based on the user-defined speed, critical path point set, and cubic B-spline basis function of the aircraft, determine the control point set of the cubic B-spline curve. Based on dynamic obstacles and control point sets, a dynamic obstacle avoidance penalty function is determined, a static obstacle avoidance penalty function is determined based on obstacle avoidance path point sets, and an optimization model is determined based on the dynamic obstacle avoidance penalty function and the static obstacle avoidance penalty function. Based on the optimization model and the current flight parameters of the aircraft, the target trajectory corresponding to the aircraft is generated.

2. The trajectory planning method as described in claim 1, characterized in that, The step of determining the control point set of the cubic B-spline curve based on the user-defined speed, critical path point set, and cubic B-spline basis function corresponding to the aircraft includes: Based on the user-defined speed, determine the target speed limit and critical path time corresponding to each critical path point in the critical path point set. Based on the cubic B-spline basis function, the target limit velocity corresponding to each critical path point, and the critical path point time, the control point set of the cubic B-spline curve is determined.

3. The trajectory planning method as described in claim 2, characterized in that, The steps for determining the target speed limit and critical path point time corresponding to each critical path point in the critical path point set based on the user-defined speed include: Based on the user-defined speed and the upper limit of steep ascent and descent speeds, determine the first speed limit corresponding to each critical path point; Based on the speed limit and corresponding acceleration limit of the previous critical path point in the first speed limit, determine the speed limit of the current critical path point, and obtain the second speed limit corresponding to each critical path point. Based on the speed limit and corresponding acceleration limit of the next critical path point in the second speed limit, determine the speed limit of the current critical path point and obtain the target limit speed corresponding to each critical path point. Based on the distance to critical path points and the target speed limit, a time set is determined, wherein the time set includes the critical path point time corresponding to each critical path point.

4. The trajectory planning method as described in claim 3, characterized in that, The step of determining the control point set of the cubic B-spline curve based on the cubic B-spline basis function, the target limit velocity corresponding to each critical path point, and the critical path point time includes: Based on the target-limited speed, obtain the first velocity and first acceleration of the first critical path point in the critical path point set, and the second velocity and second acceleration of the last critical path point in the critical path point set; Based on the set of critical path points, the first velocity, first acceleration, second velocity, and second acceleration at the critical path point time, the set of control points for the cubic B-spline curve is determined.

5. The trajectory planning method as described in claim 1, characterized in that, The step of determining the dynamic obstacle avoidance penalty function based on dynamic obstacles and control point sets includes: Based on the first start time and first trajectory duration corresponding to the predicted trajectory of each dynamic obstacle, and the second start time and second trajectory duration corresponding to the aircraft trajectory of the key path point set, the minimum time and maximum time corresponding to each dynamic obstacle are determined respectively. Based on the aircraft's current position, minimum time, maximum time, bounding boxes corresponding to each dynamic obstacle, and safe distance, determine the collision cost corresponding to each dynamic obstacle; The dynamic obstacle avoidance penalty function is determined based on the collision cost corresponding to each dynamic obstacle.

6. The trajectory planning method as described in claim 5, characterized in that, The step of determining the collision cost for each dynamic obstacle based on the aircraft's current position, minimum time, maximum time, bounding boxes corresponding to each dynamic obstacle, and safety distance includes: Based on the aircraft's current position and the bounding boxes corresponding to each dynamic obstacle, the nearest point between the aircraft and each dynamic obstacle is determined, and the three-dimensional relative position information between the nearest point and the aircraft's current position is determined. Based on three-dimensional relative position information and safety distance, determine dynamic parameters; Based on the dynamic parameters, minimum time, and maximum time of each dynamic obstacle, the collision cost corresponding to each dynamic obstacle is determined.

7. The trajectory planning method as described in claim 5, characterized in that, The step of determining the minimum and maximum times corresponding to each dynamic obstacle based on the first start time and first trajectory duration corresponding to the predicted trajectory of each dynamic obstacle, and the second start time and second trajectory duration corresponding to the aircraft trajectory, includes: Based on the first start time and the second start time, determine the minimum time corresponding to each dynamic obstacle; Based on the first start time, the first trajectory duration, the second start time, and the second trajectory duration, the maximum time corresponding to each dynamic obstacle is determined.

8. The trajectory planning method as described in claim 1, characterized in that, The steps for determining the static obstacle avoidance penalty function based on the obstacle avoidance path point set include: Based on the obstacle surface points and movement direction in the obstacle bypass path point set, as well as each critical path point and safety distance, the first static parameter corresponding to each critical path point is determined. Based on the first static parameters, each key path point and the corresponding motion direction, determine the second static parameters corresponding to each key path point. Based on the second static parameter, the static obstacle avoidance penalty function is determined.

9. The trajectory planning method as described in claim 1, characterized in that, The steps for determining the optimization model based on the dynamic obstacle avoidance penalty function and the static obstacle avoidance penalty function include: The trajectory smoothness penalty function is determined based on the critical path point set, and the dynamic feasibility penalty function is determined based on the control point set of the cubic B-spline curve. The optimization model is determined based on the dynamic obstacle avoidance penalty function, the static obstacle avoidance penalty function, the trajectory smoothness penalty function, and the dynamic feasibility penalty function.

10. The trajectory planning method as described in claim 9, characterized in that, The steps for determining the trajectory smoothness penalty function based on the critical path point set include: Based on the jerk corresponding to each critical path point in the critical path point set, the trajectory smoothness penalty function is determined.

11. The trajectory planning method as described in claim 9, characterized in that, The steps for determining the dynamic feasibility penalty function based on the control point set of cubic B-spline curves include: Obtain the three-dimensional velocity, Z-axis velocity, three-dimensional acceleration, three-dimensional velocity limit, maximum Z-axis velocity, and maximum combined acceleration of the three axes for each control point in the control point set; The first penalty function is determined based on the three-dimensional velocity and the upper limit of the three-dimensional velocity; the second penalty function is determined based on the Z-axis velocity and the maximum Z-axis velocity; and the third penalty function is determined based on the three-dimensional acceleration and the maximum value of the three-axis combined acceleration. Based on the first penalty function, the second penalty function, and the third penalty function, a dynamic feasibility penalty function is determined.

12. The trajectory planning method as described in claim 1, characterized in that, The step of generating a key path point set based on static obstacles corresponding to the aircraft, the local trajectory of the previous frame, and the global navigation path includes: Based on the aircraft's current position, current speed, previous frame local trajectory, and global navigation path, determine the backup path; A critical path point set is generated based on the static obstacles corresponding to the aircraft and the initial path point set corresponding to the alternative path.

13. The trajectory planning method as described in claim 12, characterized in that, The step of determining the alternative path based on the aircraft's current position, current speed, previous frame local trajectory, and global navigation path includes: Based on the current position and current speed of the aircraft, the first projection point is determined in the local trajectory of the previous frame; Determine the second projection point in the global navigation path of the aircraft corresponding to the last local trajectory point in the previous frame. The backup path is determined based on the first projection point, the second projection point, the local trajectory of the previous frame, and the global navigation path.

14. The trajectory planning method as described in claim 12, characterized in that, The step of generating a critical path point set based on the static obstacles corresponding to the aircraft and the initial path point set corresponding to the alternative path includes: The alternative paths are sampled based on the distance to the critical path points to obtain an initial set of path points; Based on the static grid map corresponding to static obstacle information, determine whether there are collision segments in the initial path point set; The obstacle avoidance path point set is determined based on the collision segment, wherein the obstacle avoidance path point set includes obstacle surface points and corresponding movement directions; The critical path point set is generated based on the obstacle avoidance path point set and the initial path point set.

15. An aircraft, characterized in that, The aircraft includes: The first generation module is used to generate a set of critical path points based on the static obstacles corresponding to the aircraft, the local trajectory of the previous frame, and the global navigation path, wherein the set of critical path points includes the set of obstacle avoidance path points corresponding to the static obstacles. The first determining module is used to determine the control point set of the cubic B-spline curve based on the user-defined speed, critical path point set, and cubic B-spline basis function corresponding to the aircraft. The second determination module is used to determine the dynamic obstacle avoidance penalty function based on the dynamic obstacle and the control point set, determine the static obstacle avoidance penalty function based on the obstacle avoidance path point set, and determine the optimization model based on the dynamic obstacle avoidance penalty function and the static obstacle avoidance penalty function. The second generation module is used to generate the target trajectory corresponding to the aircraft based on the optimization model and the current flight parameters of the aircraft.

16. A trajectory planning device, characterized in that, The trajectory planning device includes: a memory, a processor, and a trajectory planning program stored in the memory and executable on the processor, wherein the trajectory planning program, when executed by the processor, implements the steps of the trajectory planning method as described in any one of claims 1 to 14.

17. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a trajectory planning program, which, when executed by a processor, implements the steps of the trajectory planning method as described in any one of claims 1 to 14.

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