Aerocar motion planning method and device based on flight corridor

Abstract

The invention provides an aerocar motion planning method and device based on a flight corridor, and the method comprises the steps: planning a first flight path through employing a path planning algorithm according to a starting point and an end point of an aerocar in an urban environment digital grid map, and the first flight path comprises a plurality of path nodes; constructing a flight corridor based on a plurality of path nodes of the first flight path; and taking the flight corridor as a hard constraint of a trajectory of the hovercar, and generating a second flight path which is a flight curve by using a trajectory optimization algorithm. According to the method provided by the invention, a low-energy-consumption and high-safety flight path can be planned for the hovercar, so that the accuracy and the stability of tracking control are improved.

Description

Technical Field

[0001] This application relates to the field of flying car technology, and in particular to a flying car motion planning method and device based on a flight corridor. Background Technology

[0002] Currently, most flying car planning methods treat the global planning problem as a path planning problem, without effectively considering kinematic constraints. At the initial stage of motion planning, only energy consumption and time-related costs are considered, while the crucial factor of path safety during the flying car's movement is ignored. Summary of the Invention

[0003] In view of this, this application provides a method and apparatus for motion planning of flying cars based on flight corridors to solve the above-mentioned technical problems.

[0004] In a first aspect, embodiments of this application provide a method for motion planning of a flying car based on a flight corridor, including: Based on the starting point and ending point of the flying car in the digital grid map of the urban environment, a first flight path is planned using a path planning algorithm. The first flight path includes multiple path nodes. A flight corridor is constructed based on multiple path nodes of the first flight path; Using the flight corridor as a hard constraint on the trajectory of the flying car, a second flight path is generated using a trajectory optimization algorithm. The second flight path is a flight curve.

[0005] In one possible implementation, based on the starting and ending points of the flying car in a digital raster map of the urban environment, a path planning algorithm is used to plan a first flight path, including: Step S11: Determine the starting point and ending point of the flying car in the digital raster map of the urban environment; store the starting point as the current node in the open list openList and the closed list closeList respectively; Step S12: Obtain the eight extended nodes of the current node, and determine whether each extended node is in the open list. If yes, proceed to step S13; otherwise, store the extended node in the open list and proceed to step S13. Step S13: Calculate the estimated cost of each extended node of the current node, determine the extended node with the smallest estimated cost as the child node, store the child node in the close list, and update the actual cost from the starting point to the child node, the estimated cost from the child node to the end point, and the parent node pointer; wherein, the estimated cost includes energy consumption cost, time cost, turning cost, and building risk cost. Step S14: Determine if the child node is the endpoint. If yes, proceed to step S15; otherwise, make the child node the current node and proceed to step S12. Step S15: Take each child node in the close list as a path node, and connect each path node in sequence to generate the first flight path.

[0006] In one possible implementation, the estimated generation value of each extended node of the current node is calculated, including: in, It is an estimate of the value of the product. and All are weighted parameters; Indicates the cost-effectiveness of energy consumption. Indicates the value of time. Indicates the value of a turnaround. This indicates the risk value of a building.

[0007] In one possible implementation, the turning value The calculation formula is: in, This indicates the forces acting on the flying car when it turns. This represents the distance between the current node and the extended node. Indicates the mass of the flying car. Indicates turning acceleration. Indicates ground speed. This indicates the minimum turning radius.

[0008] In one possible implementation, a flight corridor is constructed based on multiple path nodes of the first flight path, including: Store all obstacle information around the first flight path in a structure; Construct a circle with the longest diagonal of the bottom rectangle of each obstacle as its diameter, and then construct a cylinder around the obstacle with the height of the obstacle as its height. Iterate through the path nodes in the closeList sequentially. For the current path node, iterate through all obstacle structures and calculate the Euclidean distance from the current path node to all obstacles. and the radius of the circumscribed cylinder Stored in an array; retrieve the nearest distance between the current path node and the circumscribed cylinder from the array, thus obtaining the radius of the sphere centered at the current path node; Obtain the center and radius of two adjacent spheres in sequence, and calculate the distance between the centers of the two adjacent spheres. Determine distance Does it satisfy the following formula: in, It is the radius of the first sphere. It is the radius of the second sphere. It is a regulatory factor; If the conditions are met, the second sphere is retained; otherwise, the second sphere is deleted. All retained spheres are arranged into a flight corridor.

[0009] In one possible implementation, the flight corridor serves as a hard constraint on the trajectory of the flying car, and a second flight path is generated using a trajectory optimization algorithm, including... Establish hard constraints on the trajectory, including: the midpoint between the centers of two adjacent spheres in the flight corridor, the starting point and the ending point are the necessary points of the trajectory, the velocity and acceleration of the starting point, the velocity and acceleration of the ending point, and the trajectory cannot exceed the flight corridor; Based on the Minimum Snap trajectory optimization algorithm, an optimization function under hard constraints is constructed, and the second flight path is obtained by solving for the minimum value of the optimization function.

[0010] Secondly, embodiments of this application provide a flight corridor-based flying car motion planning device, comprising: The first planning unit is used to plan a first flight path based on the starting point and ending point of the flying car in the digital grid map of the urban environment using a path planning algorithm. The first flight path includes multiple path nodes. A construction unit is used to construct a flight corridor based on multiple path nodes of the first flight path; The second planning unit is used to generate a second flight path, which is a flight curve, using the flight corridor as a hard constraint on the trajectory of the flying car.

[0011] Thirdly, embodiments of this application provide an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method of embodiments of this application. Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the methods of embodiments of this application.

[0012] The method described in this application can plan low-energy and high-safety flight paths for flying cars, thereby improving the accuracy and stability of tracking and control. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0014] Figure 1 A flowchart of a flight corridor-based flying car motion planning method provided in an embodiment of this application; Figure 2 A schematic diagram illustrating the process of generating a flight corridor as provided in an embodiment of this application; Figure 3 Ablation comparison chart of trajectory optimization for the first scenario provided in the embodiments of this application; Figure 4 Ablation comparison chart for trajectory optimization in the second scenario provided in the embodiments of this application; Figure 5 Ablation comparison chart of trajectory optimization for the third scenario provided in the embodiments of this application; Figure 6 A graph showing the changes in velocity, acceleration, and Jerk's velocity along the x, y, and z axes of the first scene provided in this embodiment of the application after trajectory optimization; Figure 7 The graph shows the changes in velocity, acceleration, and Jerk's velocity along the x, y, and z axes of the second scene provided in this embodiment of the application after trajectory optimization. Figure 8 The graph shows the changes in velocity, acceleration, and Jerk's velocity along the x, y, and z axes of the third scene provided in this embodiment of the application after trajectory optimization. Figure 9 A framework diagram of motion planning for flying cars based on flight corridors provided in this application embodiment; Figure 10 Functional structure diagram of a flight corridor-based flying car motion planning device provided in an embodiment of this application; Figure 11 A functional structure diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0016] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0017] First, a brief introduction to the design concept of the embodiments of this application will be given.

[0018] Flying cars have attracted widespread attention due to their high mobility, obstacle-crossing ability, and flexibility. As a transportation tool for low-altitude intelligent transportation and three-dimensional intelligent transportation, flying cars have broad development prospects and significant application value in solving traffic congestion, emergency rescue, and future business travel. To fully realize the potential of flying cars in these application scenarios, intelligent driving technology, especially motion planning, is particularly crucial.

[0019] Existing mainstream planning methods include: search-based methods, sampling-based methods, and planning methods based on intelligent algorithms. While related algorithms possess completeness and optimality, they are unsuitable for complex environments due to storage constraints and computational limitations. The Quick Search Random Tree (RRT) algorithm, a classic algorithm based on random sampling, excels in search speed and path feasibility, but the paths it generates are often not optimal, exhibiting redundancy and unevenness. In contrast, planning methods based on intelligent algorithms, such as reinforcement learning, learn optimal strategies gradually through interaction with the environment, demonstrating stronger adaptability and self-learning capabilities. However, their high computational requirements, long training times, and complex reward designs also limit their application.

[0020] Based on the analysis of the above background technology, this application provides a flying car motion planning method based on flight corridors. This method can generate a smoother, safer trajectory for the flying car that conforms to kinematic constraints, which is more conducive to path tracking control.

[0021] The method in this embodiment includes front-end path planning and back-end trajectory optimization, which are linked by a constructed flight corridor. In the front-end planning, an evaluation function that comprehensively considers energy consumption, time, turning, and building risk costs is designed, based on improvements... The algorithm plans a safe and energy-efficient path for the flying car. Next, based on the initial path points planned in the front end, a safe flight corridor is constructed and optimized, expanding the flying car's maneuverability and laying the foundation for back-end optimization. In the back-end optimization, Minimum Snap trajectory optimization is performed based on the constructed safe flight corridor, generating a smooth flight trajectory for the flying car, which is more conducive to path tracking and control.

[0022] This application has the following technical effects: 1. The method in this application adopts the planning principle of front-end planning and back-end optimization. The front-end uses an improved A* algorithm, and the back-end uses Minimum Snap trajectory optimization. The front and back ends are connected by a flight corridor. The flight corridor provides hard constraints for trajectory optimization, and the front-end planning provides a safe and energy-saving path. This ensures that the back-end trajectory optimization has sufficient safe flight space on the one hand, and low energy consumption and high safety of the optimized trajectory on the other hand, thereby improving the accuracy and stability of tracking control.

[0023] 2. The flight corridor constructed in this application is based on an initial safe and energy-saving path planned in advance. The optimized flight corridor not only significantly reduces the number of spheres, but also retains a certain degree of intersection among the remaining spheres. Therefore, trajectory optimization within this corridor can ensure low energy consumption and high safety, and has certain practical value.

[0024] 3. In the equality constraints of the optimization function constructed in this application, the path is not strictly restricted to passing through intermediate path points. Instead, the midpoint between the centers of two adjacent spheres in the flight corridor is taken as the intermediate position constraint. This increases the maneuverability of the flying car in the safe corridor and ensures safety and feasibility.

[0025] After introducing the application scenarios and design concepts of the embodiments of this application, the technical solutions provided by the embodiments of this application will be described below.

[0026] like Figure 1 As shown, this application provides a method for motion planning of flying cars based on flight corridors, including the following steps: Step 101: Based on the starting point and ending point of the flying car in the digital grid map of the urban environment, a first flight path is planned using a path planning algorithm. The first flight path includes multiple path nodes. Step 102: Construct a flight corridor based on multiple path nodes of the first flight path; Step 103: Using the flight corridor as a hard constraint on the trajectory of the flying car, a second flight path is generated using a trajectory optimization algorithm. The second flight path is a flight curve.

[0027] In some embodiments, step 101 specifically includes: Step S11: Determine the starting point and ending point of the flying car in the digital raster map of the urban environment; store the starting point as the current node in the open list openList and the closed list closeList respectively; Step S12: Obtain the eight extended nodes of the current node, and determine whether each extended node is in the open list. If yes, proceed to step S13; otherwise, store the extended node in the open list and proceed to step S13. Step S13: Calculate the estimated cost of each extended node of the current node, determine the extended node with the smallest estimated cost as the child node, store the child node in the close list, and update the actual cost from the starting point to the child node, the estimated cost from the child node to the end point, and the parent node pointer; wherein, the estimated cost includes energy consumption cost, time cost, turning cost, and building risk cost. Step S14: Determine if the child node is the endpoint. If yes, proceed to step S15; otherwise, make the child node the current node and proceed to step S12. Step S15: Take each child node in the close list as a path node, and connect each path node in sequence to generate the first flight path.

[0028] In this embodiment, in step S13, the total estimated cost of the expansion node is calculated by designing the following comprehensive cost function. : in, It is an estimate of the value of the product. and All are weighted parameters; Indicates the cost-effectiveness of energy consumption. Indicates the value of time. Indicates the value of a turnaround. This indicates the risk value of a building.

[0029] In the above comprehensive cost function, and The turning value can be calculated using conventional methods. The calculation formula is: in, This indicates the forces acting on the flying car when it turns. This represents the distance between the current node and the extended node. Indicates the mass of the flying car. Indicates turning acceleration. Indicates ground speed. This indicates the minimum turning radius.

[0030] The risk cost of a building can be represented by setting higher values ​​around the building in a digital raster map, thus indicating a higher level of risk. The matrix below illustrates this: Here, 0 represents passage, inf represents obstacles, and 3 represents the risk cost of setting up around a building.

[0031] By incorporating energy consumption, time, turning costs, and building risk costs into the comprehensive cost function designed above, it can be ensured that the planned path can keep away from urban buildings while maintaining low energy consumption.

[0032] This embodiment uses an improved method. As a path planning method, the algorithm is an improvement. The algorithm fully considers energy consumption costs, time costs, turning costs, and building risk costs, thus planning a better path that balances energy consumption and safety, and leveraging the unique advantages of flying cars in solving traffic congestion in urban three-dimensional transportation.

[0033] This embodiment applies the improved version to three different scenarios. The algorithm's planning performance was ablation-compared, and three planning strategies (considering only energy consumption and time, considering energy consumption, time, and turning costs, and comprehensively considering energy consumption, time, turning costs, and building risk costs) were tested. For simplicity, these three strategies are referred to as Strategy 1, Strategy 2, and Strategy 3, respectively. Specific simulation results are as follows: Table 1: Scenario 1 Table 2: Second Scenario Table 3: Third Scenario Based on the above three sets of simulation results, the following conclusions can be drawn: The effectiveness of front-end path planning depends on the complexity of the environment and the location of the starting point. In the first and second scenarios, due to the relatively simple environment, the starting point is set in an easily accessible location. Combining Tables 1 and 2, it can be seen that although the proposed third planning strategy has an absolute advantage in path safety, its effectiveness in terms of path length and the number of turning points is similar to or slightly higher than that of the second strategy.

[0034] As the environment becomes more complex and the target point is surrounded by obstacles, the superiority of the proposed third strategy becomes even more apparent. As shown in Table 3, the proposed third strategy outperforms the first two strategies in all indicators.

[0035] Based on the above analysis, this application selects improvements in the front end. Algorithm planning of the initial path has certain value and significance.

[0036] Furthermore, in complex urban environments, flying cars need to navigate safely and efficiently among buildings, trees, and other obstacles. (Direct use) The initial path generated by the algorithm may lead to collision risks, increasing energy consumption and time costs. To address this issue, this application constructs a flight corridor based on the generated path nodes and further optimizes the flight corridor to adapt to the actual needs of flying cars.

[0037] This embodiment generates flight corridors based on path nodes. A flight corridor refers to a series of three-dimensional safe passages established along the initial path points. First, obstacles are initialized by constructing a cylinder around the obstacles. Then, all initial path nodes and obstacles are traversed to find the obstacle closest to each path node, and the radius of the spherical corridor centered on each path node is determined. The above process is repeated until the endpoint. Finally, redundant spheres are removed by iterating through each spherical corridor to retain high-quality spheres to form a new flight corridor.

[0038] Specifically, the process of constructing the flight corridor includes: Store all obstacle information around the first flight path in a structure; Construct a circle with the longest diagonal of the bottom rectangle of each obstacle as its diameter, and then construct a cylinder around the obstacle with the height of the obstacle as its height. Iterate through the path nodes in the closeList sequentially. For the current path node, iterate through all obstacle structures and calculate the Euclidean distance from the current path node to all obstacles. and the radius of the circumscribed cylinder Stored in an array; retrieve the nearest distance between the current path node and the circumscribed cylinder from the array, thus obtaining the radius of the sphere centered at the current path node; Obtain the center and radius of two adjacent spheres in sequence, and calculate the distance between the centers of the two adjacent spheres. Determine distance Does it satisfy the following formula: in, It is the radius of the first sphere. It is the radius of the second sphere. It is a regulatory factor; If the conditions are met, the second sphere is retained; otherwise, the second sphere is deleted. All retained spheres are arranged into a flight corridor.

[0039] Figure 2 (a) shows the process of a sphere being formed from its center. The nearest obstacle point in the digital raster map and radius definition. Figure 2 (b) in the diagram is the effect of the generated initial corridor. It can be clearly seen that there are too many spheres in the initial flight corridor, and there are too many redundant spheres between some adjacent spheres, which is not conducive to the optimization of the back-end trajectory. Figure 2 (c) in the diagram is a rendering of the optimized flight corridor. It retains a large spherical volume while minimizing the intersection volume between adjacent spheres, thus providing sufficient maneuverability for flying cars.

[0040] After constructing the flight corridor and ensuring the safe flight space for the flying car, further optimization of the initial path is needed to improve flight stability and reduce energy consumption, generating a continuous and smooth trajectory that conforms to the kinematic constraints of the flying car. This embodiment uses Minimum Snap for trajectory optimization. Existing Minimum Snap strictly restricts the trajectory from passing through intermediate path points, while this application uses the flight corridor as a hard constraint, strictly limiting the trajectory within the safe flight corridor to ensure the safety of the flying car's movement in urban environments. Simultaneously, to increase the flying car's maneuverability within the safe corridor, the midpoint between the centers of two adjacent spheres within the flight corridor is taken as the intermediate position constraint.

[0041] The trajectory optimization problem for flying cars can be understood as a multi-objective optimization problem. The goal is to achieve a final trajectory that passes through the starting point, ending point, and fixed intermediate points, with smooth transitions between adjacent trajectories, and the entire trajectory strictly confined within a constructed flight corridor. To obtain a trajectory that meets these requirements, this embodiment models the problem as a constrained quadratic programming (QP) problem for solution, constructing an optimization function... It can be represented as follows: in, The parameter vector representing the polynomial locus. It is a symmetric matrix of the quadratic terms in the objective function. and Represents equality constraints in trajectory optimization, matrices and This represents the inequality constraints in trajectory optimization.

[0042] After determining the intermediate position constraints, in order to generate a trajectory that accurately passes through the intermediate position, it is also necessary to solve the time allocation problem in trajectory optimization. Time allocation is a key issue in trajectory planning, as it directly affects the quality of the planning results. This embodiment adopts uniform speed allocation. The total time T is calculated based on the total distance S. Then, assuming that the speed within each polynomial curve segment is uniform, the time ratio of each segment is equal to the ratio of the lengths of each segment, as shown in the following formula: Specifically, using the flight corridor as a hard constraint on the trajectory of the flying car, a second flight path is generated using a trajectory optimization algorithm, including: Establish hard constraints on the trajectory, including: the midpoint between the centers of two adjacent spheres in the flight corridor, the starting point and the ending point are the necessary points of the trajectory, the velocity and acceleration of the starting point, the velocity and acceleration of the ending point, and the trajectory cannot exceed the flight corridor; Based on the Minimum Snap trajectory optimization algorithm, an optimization function under hard constraints is constructed, and the second flight path is obtained by solving for the minimum value of the optimization function.

[0043] To verify the effectiveness of the method in this application, tests were conducted in three scenarios, and the results were compared with the path length index before optimization. Specific simulation results are shown in Table 4. Table 4 Implementation results are as follows Figure 3 , Figure 4 and Figure 5 As shown. Figure 6 , Figure 7 as well as Figure 8 This shows the changes in velocity, acceleration, and Jerk's velocity along the x, y, and z axes after trajectory optimization in three scenarios. Figure 3 , Figure 4 and Figure 5 It can be observed that the optimized trajectory is smoother, with no obvious turning points. Smooth connections are chosen at right-angle turns, significantly reducing the number of turns. Simultaneously, the larger spherical space provides ample maneuverability for the flying car during vertical takeoff. Combined with... Figure 6 , Figure 7 as well as Figure 8 It can be seen intuitively that the Jerk curve changes less, indicating that the slower the rate of acceleration change, the smoother the trajectory.

[0044] like Figure 9 As shown, after generating the flight trajectory of the flying car, the flight trajectory is transmitted to the integrated control module. The control center makes a mode switching decision and then sends a PMW wave to the drive motor or propeller to control the movement of the flying car.

[0045] Based on the above embodiments, this application provides a flying car motion planning device based on a flight corridor, see below. Figure 10 As shown, the flight corridor-based flying car motion planning device 200 provided in this application embodiment includes at least: Planning unit 201 is used to plan a first flight path based on the starting point and ending point of the flying car in the digital grid map of the urban environment using a path planning algorithm. The first flight path includes multiple path nodes. Construction unit 202 is used to construct a flight corridor based on multiple path nodes of the first flight path; The processing unit 203 is used to generate a second flight path, which is a flight curve, by using the flight corridor as a hard constraint on the trajectory of the flying car and employing a trajectory optimization algorithm.

[0046] It should be noted that the principle of the flight car motion planning device 200 based on flight corridor provided in this application embodiment to solve the technical problem is similar to the method provided in this application embodiment. Therefore, the implementation of the flight car motion planning device 200 based on flight corridor provided in this application embodiment can refer to the implementation of the method provided in this application embodiment, and the repeated parts will not be described again.

[0047] Based on the above embodiments, this application also provides an electronic device, see below. Figure 11 As shown, the electronic device 300 provided in this application embodiment includes at least: a processor 301, a memory 302, and a computer program stored in the memory 302 and capable of running on the processor 301. When the processor 301 executes the computer program, it implements the flight corridor-based flying car motion planning method provided in this application embodiment.

[0048] The electronic device 300 provided in this application embodiment may further include a bus 303 connecting different components (including processor 301 and memory 302). The bus 303 represents one or more types of bus structures, including memory bus, peripheral bus, local area bus, etc.

[0049] The memory 302 may include a readable medium in the form of volatile memory, such as random access memory (RAM) 3021 and / or cache memory 3022, and may further include read-only memory (ROM) 3023.

[0050] The memory 302 may also include a program tool 3025 having a set (at least one) of program modules 3024, including but not limited to: an operating subsystem, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0051] Electronic device 300 can also communicate with one or more external devices 304 (e.g., keyboard, remote control, etc.), and with one or more devices that enable a user to interact with electronic device 300 (e.g., mobile phone, computer, etc.), and / or with any device that enables electronic device 300 to communicate with one or more other electronic devices 300 (e.g., router, modem, etc.). This communication can be performed through input / output (I / O) interface 305. Furthermore, electronic device 300 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) through network adapter 306. Figure 11 As shown, network adapter 306 communicates with other modules of electronic device 300 via bus 303. It should be understood that, although... Figure 11 As not shown, other hardware and / or software modules may be used in conjunction with electronic device 300, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, Redundant Arrays of Independent Disks (RAID) subsystems, tape drives, and data backup storage subsystems.

[0052] It should be noted that, Figure 11 The electronic device 300 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0053] This application also provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the flight corridor-based flying car motion planning method provided in this application. Specifically, the executable program can be built into or installed in an electronic device 300, so that the electronic device 300 can implement the flight corridor-based flying car motion planning method provided in this application by executing the built-in or installed executable program.

[0054] The flight corridor-based flying car motion planning method provided in this application embodiment can also be implemented as a program product. The program product includes program code. When the program product can run on the electronic device 300, the program code is used to make the electronic device 300 execute the flight corridor-based flying car motion planning method provided in this application embodiment.

[0055] The program product provided in this application embodiment can be any combination of one or more readable media, wherein the readable media can be a readable signal medium or a readable storage medium, and the readable storage medium can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination thereof. Specifically, more specific examples of readable storage media (a non-exhaustive list) include: electrical connections with one or more wires, portable disks, hard disks, RAM, ROM, erasable programmable read-only memory (EPROM), optical fibers, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0056] The program product provided in this application embodiment can be a CD-ROM and include program code, and can also run on a computing device. However, the program product provided in this application embodiment is not limited thereto. In this application embodiment, the readable storage medium can be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0057] It should be noted that although several units or sub-units of the device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of this application, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided and embodied by multiple units.

[0058] Furthermore, while the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. Multitasking and parallel processing may be advantageous in certain environments. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this application. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

Claims

1. A method for motion planning of flying cars based on flight corridors, characterized in that, include: Based on the starting point and ending point of the flying car in the digital grid map of the urban environment, a first flight path is planned using a path planning algorithm. The first flight path includes multiple path nodes. A flight corridor is constructed based on multiple path nodes of the first flight path; Using the flight corridor as a hard constraint on the trajectory of the flying car, a second flight path is generated using a trajectory optimization algorithm. The second flight path is a flight curve.

2. The method according to claim 1, characterized in that, Based on the starting and ending points of the flying car in the urban environment digital grid map, a path planning algorithm is used to plan the first flight path, including: Step S11: Determine the starting point and ending point of the flying car in the digital raster map of the urban environment; store the starting point as the current node in the open list openList and the closed list closeList respectively; Step S12: Obtain the eight extended nodes of the current node, and determine whether each extended node is in the open list. If yes, proceed to step S13; otherwise, store the extended node in the open list and proceed to step S13. Step S13: Calculate the estimated cost of each extended node of the current node, determine the extended node with the smallest estimated cost as the child node, store the child node in the close list, and update the actual cost from the starting point to the child node, the estimated cost from the child node to the end point, and the parent node pointer; wherein, the estimated cost includes energy consumption cost, time cost, turning cost, and building risk cost. Step S14: Determine if the child node is the endpoint. If yes, proceed to step S15; otherwise, make the child node the current node and proceed to step S12. Step S15: Take each child node in the close list as a path node, and connect each path node in sequence to generate the first flight path.

3. The method according to claim 2, characterized in that, Calculate the estimated generation value of each expanded node of the current node, including: in, It is an estimate of the value of the product. and All are weighted parameters; Indicates the cost-effectiveness of energy consumption. Indicates the value of time. Indicates the value of a turnaround. This indicates the risk value of a building.

4. The method according to claim 3, characterized in that, Turning point value The calculation formula is: in, This indicates the forces acting on the flying car when it turns. This represents the distance between the current node and the extended node. Indicates the mass of the flying car. Indicates turning acceleration. Indicates ground speed. This indicates the minimum turning radius.

5. The method according to claim 2, characterized in that, Based on multiple path nodes of the first flight path, a flight corridor is constructed, including: Store all obstacle information around the first flight path in a structure; Construct a circle with the longest diagonal of the bottom rectangle of each obstacle as its diameter, and then construct a cylinder around the obstacle with the height of the obstacle as its height. Iterate through the path nodes in the closeList sequentially. For the current path node, iterate through all obstacle structures and calculate the Euclidean distance from the current path node to all obstacles. and the radius of the circumscribed cylinder Stored in an array; retrieve the nearest distance between the current path node and the circumscribed cylinder from the array, thus obtaining the radius of the sphere centered at the current path node; Obtain the center and radius of two adjacent spheres in sequence, and calculate the distance between the centers of the two adjacent spheres. Determine distance Does it satisfy the following formula: in, It is the radius of the first sphere. It is the radius of the second sphere. It is a regulatory factor; If the conditions are met, the second sphere is retained; otherwise, the second sphere is deleted. All retained spheres are arranged into a flight corridor.

6. The method according to claim 5, characterized in that, Using the flight corridor as a hard constraint on the trajectory of the flying car, a second flight path is generated using a trajectory optimization algorithm, including: Establish hard constraints on the trajectory, including: the midpoint between the centers of two adjacent spheres in the flight corridor, the starting point and the ending point are the necessary points of the trajectory, the velocity and acceleration of the starting point, the velocity and acceleration of the ending point, and the trajectory cannot exceed the flight corridor; Based on the Minimum Snap trajectory optimization algorithm, an optimization function under hard constraints is constructed, and the second flight path is obtained by solving for the minimum value of the optimization function.

7. A motion planning device for flying cars based on a flight corridor, characterized in that, include: The first planning unit is used to plan a first flight path based on the starting point and ending point of the flying car in the digital grid map of the urban environment using a path planning algorithm. The first flight path includes multiple path nodes. A construction unit is used to construct a flight corridor based on multiple path nodes of the first flight path; The second planning unit is used to generate a second flight path, which is a flight curve, using the flight corridor as a hard constraint on the trajectory of the flying car.

8. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as claimed in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the method as described in any one of claims 1-6.

Images