Unmanned aerial vehicle path planning method for urban low-altitude environment

Abstract

The invention relates to an unmanned aerial vehicle path planning method for an urban low-altitude environment, and the method comprises the following steps: obtaining global map information, carrying out the path planning, and obtaining global path data; processing the global path data, extracting global check points, and obtaining a global check point list; the unmanned aerial vehicle is controlled to fly along the global check point, and dynamic obstacle information in the flight process is obtained; based on the dynamic obstacle information, the speed and direction of the unmanned aerial vehicle are adjusted, and dynamic obstacle avoidance is performed; and repeatedly controlling the unmanned aerial vehicle to fly along the global check point and the dynamic obstacle to avoid until the unmanned aerial vehicle is controlled to reach the flight end point. According to the path planning method, the dynamic adaptability can be enhanced, the unmanned aerial vehicle can quickly make an avoidance response according to an algorithm after detecting the dynamic obstacle, the function of autonomously avoiding the dynamic obstacle is achieved, the adaptability and safety of the unmanned aerial vehicle in the dynamic environment are greatly improved, and the collision risk is effectively reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicles (UAVs), and relates to path planning of UAVs, in particular to a path planning method for UAVs used in urban low-altitude environments. Background Art

[0002] With the gradual opening up of the low-altitude airspace management system and the large-scale application of drone technology, urban low-altitude airspace is becoming the core operating space for new business formats such as drone logistics distribution, emergency rescue, and urban inspection. However, the urban low-altitude environment is complex and contains various static and dynamic obstacles. In order to ensure the safe and autonomous flight of drones, path planning is required. Traditional path planning mainly includes the following three types:

[0003] 1. Global path planning: This method uses global environmental information to plan the overall route to the target point. It can find a global solution, but its real-time performance is poor and it cannot respond to dynamic obstacles in a timely manner.

[0004] 2. Local path planning: Utilizes local environmental information to quickly plan a local flight path. This is more real-time than global path planning. However, due to the lack of global information, it is easy to converge to a local optimal solution, resulting in failure to reach the final destination.

[0005] 3. Hybrid path planning: Combining global path planning with local path planning can improve path planning results. However, it only targets static environments and treats dynamic objects as static objects at the current moment. It lacks avoidance planning when facing dynamic obstacles and has a high collision rate.

[0006] To this end, we propose a UAV path planning method for urban low-altitude environments to solve the above problems. Summary of the Invention

[0007] The purpose of the present invention is to overcome the shortcomings of the existing technology and provide a UAV path planning method for urban low-altitude environments with high avoidance efficiency and strong perception capability.

[0008] In order to solve the above problems, the technical solution of the present invention is:

[0009] A method for UAV path planning in an urban low-altitude environment comprises the following steps:

[0010] Obtain global map information, perform path planning, and obtain global path data;

[0011] Process the global path data, extract the global checkpoints, and obtain the global checkpoint list;

[0012] Control the drone to fly along the global checkpoints and obtain dynamic obstacle information during flight;

[0013] Based on dynamic obstacle information, adjust the speed and direction of the drone to avoid dynamic obstacles;

[0014] Repeatedly control the drone to fly along the global checkpoints and avoid dynamic obstacles until the drone reaches the flight endpoint.

[0015] In a further embodiment, obtaining global map information, performing path planning, and obtaining global path data specifically include:

[0016] Set the current node as the parent node;

[0017] Based on the parent node, several next nodes are found through the direction priority search mechanism;

[0018] Among several next nodes, select the node with the smallest moving cost and set it as the new parent node;

[0019] Repeat the search for the next node until the end point is reached and the global path data is obtained.

[0020] In a further embodiment, based on the parent node, a directional priority search mechanism is used to find several next nodes, specifically including:

[0021] Establish a coordinate system in the global map and calculate the distance parameters of the three axes X, Y, and Z between the current node and the end point;

[0022] If the X-axis distance parameter is greater than the other axis distance parameters, the neighbor nodes are expanded along the X-axis. If there are traversable nodes, the path expansion operation is performed and the traversable nodes are loaded into the global path data.

[0023] Based on the walkable nodes along the X axis, obtain the walkable nodes along other axes;

[0024] If no feasible node is detected along the X axis, neighbor nodes along other axes are expanded in descending order according to the three axial distance parameters until the global map scan is completed or a feasible node is found.

[0025] In a further embodiment, processing the global path data, extracting the global checkpoints, and obtaining the global checkpoint list specifically includes:

[0026] In the global path data, starting from the current node, successively take out three next nodes and determine whether the three nodes are collinear:

[0027] If they are collinear, continue to move down one position from the current node, continue to take out three next nodes, and repeat the judgment of whether they are collinear until the end point is reached;

[0028] If they are not collinear, the middle point of the three nodes is used as the global checkpoint;

[0029] Traverse all global path data, extract global checkpoints, and obtain a global checkpoint list.

[0030] In a further embodiment, the speed and direction of the drone are adjusted based on the dynamic obstacle information to avoid the dynamic obstacle, specifically including:

[0031] Analyze dynamic obstacle information and construct speed obstacle areas;

[0032] Determine whether the dynamic obstacle is within the speed obstacle area. If so, adjust the speed and direction of the drone.

[0033] In a further embodiment, analyzing dynamic obstacle information and constructing a speed obstacle area specifically includes:

[0034] Get dynamic obstacle radius and drone radius;

[0035] Construct an expansion sphere for dynamic obstacles based on the radius of the dynamic obstacle and the radius of the drone;

[0036] With the current position of the UAV as the vertex, a conical surface tangent to the expanded sphere is constructed, and the area inside the conical surface is the speed obstacle area.

[0037] In a further embodiment, the radius of the expanded sphere is the radius of the dynamic obstacle plus the safety radius of the drone.

[0038] In a further embodiment, determining whether a dynamic obstacle is located in a speed obstacle area and, if so, adjusting the speed and direction of the drone may include:

[0039] Get the relative speed between the drone and the dynamic obstacle, and calculate the angle between the relative speed and the axis of the speed obstacle area:

[0040]

[0041] Wherein, θ' is the angle between the relative speed and the axis of the speed obstacle area; is the relative speed between the UAV and the dynamic obstacle; OP is the distance from the UAV position to the center of the inflated sphere;

[0042] Calculate the cone half-apex angle of the speed obstacle area:

[0043]

[0044] Where θ is the cone half-apex angle of the speed obstacle area; r veh is the safety radius of the UAV; R ob + is the radius of the dynamic obstacle;

[0045] If the included angle is smaller than the cone half-apex angle, the relative speed is within the speed obstacle area.

[0046] In a further embodiment, determining whether the dynamic obstacle is located in the speed obstacle area and, if so, adjusting the speed and direction of the drone further includes:

[0047] Add the velocity change vector to the drone's velocity to get the new velocity.

[0048] The speed change vector is the minimum vector from the relative speed end point to the boundary of the speed obstacle area.

[0049] In a further embodiment, the speed change vector satisfies the following formula:

[0050]

[0051] Where, is the velocity change vector; τ is the time interval for calculation; is the maximum acceleration of the drone;

[0052] The new speed is obtained by the following formula:

[0053]

[0054] Where, For the new speed; is the current speed of the drone; is the maximum speed of the drone.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] 1. This path planning method can enhance dynamic adaptability: Compared with the shortcomings of traditional global path planning, which has weak real-time performance and cannot respond to dynamic obstacles in a timely manner, this invention improves on the basis of the A-star algorithm for global path planning and the ORCA algorithm. After the drone detects a dynamic obstacle, it can quickly make an evasive response based on the algorithm, realizing the function of autonomously avoiding dynamic obstacles, greatly improving the adaptability and safety of the drone in dynamic environments and effectively reducing the risk of collision.

[0057] 2. This path planning method can avoid local optimal limitations: To address the problem that local path planning easily converges to the local optimal solution, which causes the drone to be unable to reach the final destination, the present invention extracts global checkpoints as the staged target points of the drone flight in the hybrid algorithm to guide the drone flight. This avoids the situation where the ORCA algorithm alone is easily trapped in the local optimal solution when locally avoiding dynamic obstacles, ensuring that the drone can smoothly reach the destination along a reasonable path.

[0058] 3. This path planning method can improve planning efficiency and energy efficiency: local path planning is integrated into the global path planning algorithm, and the improved three-dimensional A-star global planning algorithm is used to plan checkpoints in advance before the UAV takes off. Compared with using the local path planning algorithm throughout the entire process, it not only avoids the common local optimal problem of local path planning, but also provides clear guidance for the subsequent flight of the UAV, reduces unnecessary path planning calculations, improves overall planning efficiency, and optimizes the overall energy efficiency of the algorithm, reduces the energy consumption of the UAV during flight, and improves its endurance.

[0059] 4. This path planning method can broaden the application scenarios: Traditional hybrid path planning is only applicable to static environments, while the method of the present invention can deal with both static and dynamic obstacles at the same time, and is suitable for more complex and changeable urban low-altitude environments. It can meet application scenarios such as urban logistics distribution and emergency rescue that have higher requirements for drone path planning, greatly broadening the application scope of drone path planning technology and has broad market application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 This is a flow chart of a UAV path planning method for urban low-altitude environments;

[0061] Figure 2 The following is a schematic diagram of a UAV path planning method for urban low-altitude environments;

[0062] Figure 3 This is a global path planning flow chart of a UAV path planning method for urban low-altitude environments;

[0063] Figure 4 A dynamic obstacle avoidance flow chart for a UAV path planning method for urban low-altitude environments;

[0064] Figure 5 A schematic diagram of the speed barrier area for a UAV path planning method used in urban low-altitude environments;

[0065] Figure 6 Schematic diagram of the cone half-apex angle for a UAV path planning method used in urban low-altitude environments;

[0066] Figure 7 A relative speed diagram of a UAV path planning method for urban low-altitude environments;

[0067] Figure 8 A schematic diagram of a speed-correction obstacle zone for a UAV path planning method used in urban low-altitude environments;

[0068] Figure 9 A schematic diagram of a single speed barrier area for a UAV path planning method in an urban low-altitude environment;

[0069] Figure 10 Schematic diagram of multiple speed obstacle areas for a UAV path planning method for urban low-altitude environments;

[0070] Figure 11 A schematic diagram of the maximum speed limit for a UAV path planning method for low-altitude urban environments.

[0071] Figure 12 A schematic diagram of the planning time results of a UAV path planning method for urban low-altitude environments;

[0072] Figure 13 A schematic diagram of the planning efficiency results of a UAV path planning method for urban low-altitude environments;

[0073] Figure 14 This is a schematic diagram of the planned path length results of a UAV path planning method for urban low-altitude environments;

[0074] Figure 15 This is a schematic diagram showing the obstacle avoidance success rate of a UAV path planning method used in urban low-altitude environments.

[0075] Figure 16 This is the second schematic diagram of the obstacle avoidance success rate results of a UAV path planning method used in urban low-altitude environments;

[0076] Figure 17 Schematic diagram of the experimental simulation environment for a UAV path planning method used in urban low-altitude environments. DETAILED DESCRIPTION

[0077] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, features defined as "first", "second", etc. may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "multiple" means two or more.

[0078] Example 1:

[0079] A UAV path planning method for urban low-altitude environments, such as Figures 1 to 17 As shown, the following steps are included:

[0080] S101. Obtain global map information, perform path planning, and obtain global path data:

[0081] like Figure 2 As shown in the figure, for the area where path planning is to be carried out, global map information is obtained through field surveys, online public information, etc., and the target space is structured using a three-dimensional gridding method to construct a grid map containing obstacle grids and free grids. The target space includes: dividing the target space into 1m 3 The starting and destination points are marked in a grid space with a regular cube as the unit, expressed as three-dimensional coordinates (x, y, z). The grid containing obstacles is considered an impassable area and is recorded as an obstacle grid. The rest are free grids.

[0082] Based on the improved three-dimensional A-star algorithm, a heuristic search mechanism is established. The algorithm uses free grids as node units and generates a global optimal path consisting of a free grid coordinate sequence while ensuring the feasibility of the path.

[0083] like Figure 3 As shown, the improved three-dimensional A-star algorithm of the present invention includes the following steps:

[0084] The first step is to select the current node as the parent node of the next node and implement a directional priority search mechanism, prioritizing the detection of nodes around the parent node in the direction farthest from the final target point. When a valid pass node exists in the priority direction, the search iterations for the remaining directions are immediately terminated;

[0085] The direction priority search mechanism includes:

[0086] In the three-dimensional path planning process, it is assumed that the coordinates of the current node n in the three-dimensional grid space are (x, y, z) and the coordinates of the end point are (x t ,y t ,z t ). The system calculates the three axial distance parameters between the current node and the target point, which are Δx=|x t -x|、Δy=|y t -y|、Δz=|z t -z|, establish the basis for directional decision-making;

[0087] Considering the six faces surrounding the current path point (upward, downward, left, right, forward, and backward), if a distance parameter (e.g., Δx) is significantly greater than the other axes (i.e., Δx>Δy and Δx>Δz), a directional search mechanism is initiated: neighbor nodes are expanded along that axis, prioritizing the search for the nine neighboring grid nodes extending in that direction. If a traversable node exists within this priority region, the path expansion operation is immediately executed, adding the qualified node to the candidate node set and triggering the next iteration, without waiting for the traversal of all 26 neighbor nodes on the six faces surrounding the current path point. If no feasible node is detected in the priority region, the suboptimal direction neighborhood is activated in descending order of the axial distance parameters for expansion until the entire neighborhood is scanned or a feasible path is found.

[0088] The second step is to determine whether the destination has been reached. If not, the cost function is applied to the candidate node set, and the node with the lowest cost is selected as the parent node of the next iteration and added to the global path point sequence. The next node selection is continued according to the direction priority search mechanism. The node expansion and selection operations are repeated until the destination is included in the closed list. The starting point of the destination is then added to the global path point sequence, and the global path dataset consisting of ordered grid coordinates is finally output.

[0089] The cost function is shown in formula (1):

[0090] f(n)=g(n)+h(n) (1)

[0091] Where f(n) is the total cost of moving from the starting point S to the end point T via the node x(n), g(n) is the cost of moving from the starting point S to the point x(n) along the generated path, and h(n) is the estimated cost of moving from the point x(n) to the end point T.

[0092] S103. Process the global path data, extract global checkpoints, and obtain a global checkpoint list:

[0093] In the global path point list obtained by the improved 3D A-star algorithm, starting from the starting point, select three consecutive path points, which are recorded as P1: (x1, y1, z1), P2: (x2, y2, z2), and P3: (x3, y3, z3). These three path points are judged to be collinear. If they meet If the three points are not collinear, P2 is recorded as a corner point on the path and marked as a checkpoint in the global path. If the three points are collinear, a sliding window mechanism is implemented, and the verification window is shifted back one node along the path sequence. The collinearity test is repeated until a valid corner point is found or the path end point is reached. This process continues until all global path point datasets are traversed. After the initial screening, the checkpoint list is corrected at the end, and the starting and ending points of the original path are appended to the beginning and end, forming a closed path dataset. This results in a complete set of global checkpoints, which are used as staged target points during the UAV flight mission.

[0094] S105. Control the drone to fly along the global checkpoints and obtain dynamic obstacle information during the flight:

[0095] During the flight, the robot detects in real time whether there are dynamic obstacles. If a dynamic obstacle is detected, the robot uses the improved three-dimensional ORCA algorithm to avoid the obstacle. Otherwise, the robot continues to move towards the checkpoint.

[0096] S107. Based on the dynamic obstacle information, adjust the speed and direction of the drone to avoid the dynamic obstacle:

[0097] When a dynamic obstacle is detected, information such as the obstacle's speed and diameter is obtained, and an improved three-dimensional ORCA algorithm is used for obstacle avoidance. Because the traditional ORCA algorithm only considers the velocity vector value and ignores the drone's own flight state and performance, such as the drone's current flight speed and maximum acceleration, it can easily lead to a path that does not conform to kinematic laws. Therefore, the present invention proposes an improved three-dimensional ORCA algorithm that makes the planned path smoother and more reasonable. The improved three-dimensional ORCA algorithm of the present invention is designed to enable maneuvering and evasion when encountering dynamic obstacles invading the A-star path. The improved ORCA algorithm implements the dynamic obstacle avoidance function by analyzing and processing the collected obstacle position and velocity information to determine whether the obstacle's velocity is within the three-dimensional velocity obstruction zone. If so, a new velocity is obtained to allow the obstacle to escape the velocity obstruction zone, thereby achieving the purpose of avoiding dynamic obstacles.

[0098] like Figure 4 As shown, the improved three-dimensional ORCA algorithm specifically includes the following steps:

[0099] Step 1: Expand the sphere of the dynamic obstacle according to the radius of the dynamic obstacle and the UAV itself. The spherical coordinate equation of the expanded sphere is shown in formula (2):

[0100]

[0101] Where R is the speed of the drone; x, y, and z are the coordinates of the x-axis, y-axis, and z-axis of the inflated sphere, respectively; α is the angle between the ray from the positive direction of the z-axis to the inflated sphere and the xy plane. Depending on the sampling resolution, α is sampled at equal intervals between [0, π]; β is the counterclockwise rotation angle from the positive direction of the x-axis to the inflated sphere in the xy plane. Depending on the sampling resolution, β is sampled at equal intervals between [0, 2π].

[0102] like Figure 5 As shown in the figure, a three-dimensional rectangular coordinate system is established with the current position of the drone as the origin O, and the relative position of the dynamic obstacle is set as P(x ob ,y ob ,z ob ); an expansion sphere is constructed with the relative position P of the dynamic obstacle as the center of the sphere, and the expansion radius is shown in formula (3):

[0103] r=R ob +r veh (3)

[0104] Where r is the radius of the expanded sphere; R ob is the radius of the dynamic obstacle; r veh is the safety radius of the drone.

[0105] Then, a conical surface tangent to the expanded sphere is constructed with the origin O as the vertex and OP as the axis. The range enclosed by the conical surface is the speed obstacle area in the three-dimensional dimension.

[0106] Step 2: When the relative speed between the drone and the dynamic obstacle falls into the three-dimensional speed obstacle zone, the obstacle avoidance algorithm is triggered to adjust the flight trajectory.

[0107] Step 3: Determine whether the speed of the dynamic obstacle is within the three-dimensional speed obstacle area. Let the relative speed vector between the UAV and the dynamic obstacle be Obtain the relative velocity vector by calculation The included angle θ' with the axis OP is compared with the cone half-apex angle θ of the generated three-dimensional speed obstacle area to determine whether it is located in the speed obstacle area.

[0108] like Figure 6 As shown, the cone half-apex angle θ in the speed obstacle area is calculated as shown in formula (4):

[0109]

[0110] In the formula, (r veh +R ob ) is the expansion radius of the dynamic obstacle, i.e. r in formula (2); |OP| is the Euclidean distance from point O to point P.

[0111] like Figure 7As shown, calculate the angle θ' between the relative velocity vector and the axis, as shown in formula (5):

[0112]

[0113] If θ'<θ, it means that the relative speed is within the speed obstacle area, and the speed is changed to get out of the speed obstacle area.

[0114] Step 4: Get the new speed, such as Figure 8 As shown, the relative speed To get out of the speed obstacle area, you need to add a speed change vector Thus, the new speed is obtained; when there is only one speed obstacle area, that is, only one obstacle, It is the minimum vector for the relative speed end point to reach the boundary of the speed obstacle area, thereby completing the local path planning.

[0115] When facing multiple obstacles, take new speeds for multiple obstacle areas respectively, and take the minimum value of the intersection of multiple areas as Perform a speed recovery. Figure 9 As shown, the velocity changes the vector End point position and Direction vector A plane can be determined, the plane is is the normal vector, divided by the plane Choose any direction in the half-space The current relative speed can be successfully Exit the speed barrier area. Figure 10 As shown, multiple obstacle areas will have multiple planes and multiple half-spaces. The light shaded area in the figure is the two half-space areas obtained, and the dark triangle area is the intersection of multiple half-spaces. At the intersection of these half-spaces, find the smallest The new speed is then added to the original speed to achieve dynamic obstacle avoidance.

[0116] Step 5: Figure 11 As shown, in three-dimensional space, the new speed is subject to acceleration limit and maximum speed limit;

[0117] Assume the maximum acceleration of the drone is The time interval of the algorithm calculation is τ, then the restricted The calculation formula (6) is as follows:

[0118]

[0119] Since the speed that the drone can reach has an upper limit, it is also necessary to add a maximum speed limit to the algorithm. Assuming the current speed This is the maximum speed the drone can reach, and then add a speed variable to it The expected speed will exceed the performance range of the drone, so after the speed change, the new speed is redefined and the direction is kept unchanged to obtain the new speed. The calculation formula (7) is as follows:

[0120]

[0121] Where, For the new speed; is the current speed of the drone; is the maximum speed of the drone.

[0122] Step 6: Implement dynamic obstacle avoidance based on the new speed.

[0123] S109, repeatedly control the drone to fly along the global checkpoints and avoid dynamic obstacles until the drone reaches the flight destination:

[0124] Determine whether the destination has been reached. If so, end the planning. If not, continue flying along the checkpoints and avoid obstacles until the destination is reached.

[0125] The present invention also provides a computer device, comprising a processor component: including at least one processor; a storage component: including a memory; program instructions: a computer program stored in the memory; the processor component is configured to read and execute the program instructions in the storage unit to implement steps S101 to S109.

[0126] The present invention provides a computer program product, including computer executable instructions. When the instructions are executed on a processor of an electronic device, the electronic device can execute steps S101 to S109.

[0127] The present invention also provides a computer-readable storage medium storing computer program instructions, wherein the storage medium includes: a non-volatile memory (such as a solid-state drive, a flash memory) or a temporary storage carrier (such as a memory cache). The computer-readable storage medium stores an independent product, and the product implements steps S101 to S109 in the form of a software functional unit. The technical solution of the present invention is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute steps S101 to S109. The aforementioned storage medium includes but is not limited to semiconductor memory (such as ROM, RAM, Flash), magnetic storage media (such as hard disks, tapes), optical storage media (such as CDs, DVDs) and other physical storage devices that can carry program codes (such as USB flash drives, mobile hard disks, etc.).

[0128] The present invention also provides an information data processing terminal, which is used to provide a user input interface to implement steps S101 to S109 in the above method embodiment when executed on an electronic device. The information data processing terminal is not limited to mobile phones, computers, and switches.

[0129] The present invention also provides a method for UAV path planning in urban low-altitude environments, comprising: a computing cluster comprising multiple processor nodes, each node being configured with at least one multi-core processor; a distributed storage system for storing three-dimensional point cloud maps and training data sets as well as required computer programs; a network communication interface configured to receive real-time image streams transmitted by UAV terminals; and a memory database for caching intermediate data during the implementation of the invention; wherein the computing cluster executes the computer program to implement steps S101 to S109.

[0130] The present invention conducted the following experiments based on steps S101 to S109:

[0131] Using the improved 3D A-star algorithm, the drone obtains checkpoints along a collision-free path. The drone then flies toward these checkpoints while detecting any dynamic obstacles. If so, the drone uses the improved 3D ORCA algorithm to perform local path planning to avoid them. Otherwise, the drone continues its journey as planned. This process continues until it reaches the final global destination.

[0132] The improved 3D A-star algorithm adopts a direction priority search method in the search process, which reduces the number of points that must be searched, improves search efficiency, and significantly reduces search time. The planning time performance index of the improved 3D A-star algorithm is tested in different environmental spaces. Long obstacles are randomly generated in the environment, such as Figure 17 shown.

[0133] Five static scenes are specified, and the scene space size is expanded from the smallest 20×20×20 (unit: cubic meters) to 100×100×100 (unit: cubic meters). The proportion of grid space occupied by obstacles in each scene is also different. The specific parameters are shown in Table 1:

[0134] Table 1 Parameter settings for static scenes

[0135]

[0136] The traditional A-star algorithm and the improved A-star algorithm proposed in this invention were used respectively, and the average planning time was taken after multiple performance test experiments in each scenario. The results are as follows Figure 12 As shown in Figure 3, by comparing the planning time required by the traditional A-star algorithm and the improved A-star algorithm under different environmental space sizes, it can be concluded that the improved algorithm is significantly faster than the traditional algorithm.

[0137] The global path planning efficiency index GPP-E (Global Path Planning Efficiency) is proposed to quantitatively analyze the global path planning efficiency performance before and after the algorithm improvement. The specific parameters that affect planning efficiency are usually the straight-line distance from the starting point to the end point of the task, the planning time required, the proportion of grid space occupied by obstacles, etc. The test environment is shown in Table 1. The mathematical definition of GPP-E is obtained by formula (8):

[0138]

[0139] Where L is the straight-line distance of the task, T is the time required for planning, and N obs is the number of obstacle grids, N total is the total number of grids. Figure 13 As shown in the figure, it can be seen that as the size of the grid space increases, the total required planning time also increases. According to the mathematical definition of the GPP-E indicator, the planning efficiency performance of the traditional A-star algorithm and the improved A-star algorithm is compared in different scenarios. In terms of global planning, the improved algorithm is much more efficient than the traditional algorithm.

[0140] For the global path planning algorithm, the length of the planned path can well reflect the performance of the algorithm. The global optimal path must be the shortest safe path that can avoid obstacles. In the different scenarios specified in Table 1, the length of the global planned path obtained by the traditional A-star algorithm and the improved A-star algorithm is compared. Figure 14 shown.

[0141] As the scene changes, the global path lengths planned by the two algorithms will also differ, but in the same environment, the global path lengths planned by the improved A-star algorithm are shorter than those planned by the traditional A-star algorithm.

[0142] The present invention improves the local path planning algorithm ORCA, which is a special algorithm for dynamic obstacle avoidance. Therefore, the obstacle avoidance success rate is the primary indicator for evaluating the performance of the algorithm. The obstacle avoidance success rate indicator is quantitatively defined as shown in formula (9):

[0143]

[0144] The success rate of obstacle avoidance during a UAV mission will be affected by factors such as the complexity of the environment and the performance limitations of the UAV. For the local path planning algorithm, the complexity of the environment is mainly affected by the number of dynamic obstacles in the environment. The size of the controlled environment space is 20×20×20 (unit: cubic meters), and the maximum acceleration of the UAV is set to no more than 16m / s. 2 Under the condition that the maximum speed of the drone does not exceed 3m / s, the number of dynamic obstacles in the space is changed from a minimum of 5 to a maximum of 20, and 100 rounds of tests are performed in each case.

[0145] like Figure 15 As shown in the figure, the number of tasks that will cause collisions per 100 tasks and the corresponding obstacle avoidance success rate. It can be seen that when the number of dynamic obstacles in the 20×20×20 (unit: cubic meters) space is less than or equal to 7, the obstacle avoidance success rate of the local path planning algorithm is 100%. As the number of dynamic obstacles in the space increases, the number of tasks that will cause collisions per 100 tasks increases, and the obstacle avoidance success rate decreases. When the number of dynamic obstacles increases to 20, the obstacle avoidance success rate can still reach 95%.

[0146] Another key factor affecting the success rate of obstacle avoidance is the maximum acceleration limit of the drone. The greater the acceleration, the more flexible the drone can make evasive maneuvers. Therefore, the size of the investigation environment space is 20×20×20 (unit: cubic meters), the number of dynamic obstacles in the space is set to 15, and the maximum speed of the drone does not exceed 3m / s. Under these conditions, the maximum acceleration limit of the drone is recorded to be 8m / s. 2 , 12m / s 2 , 16m / s2 and 20m / s 2 Experimental results per hundred times under four conditions.

[0147] like Figure 16 As shown in the figure, when the maximum acceleration limit is small, the number of collision tasks generated by the drone per 100 tasks is high. 2 When the maximum acceleration limit is increased to 16m / s, the obstacle avoidance success rate is slightly lower than 90%. 2 After that, the obstacle avoidance success rate of the drone can exceed 95%.

[0148] The path planning algorithm of the present invention integrates local path planning into the global path planning algorithm. Compared with the original A-star algorithm, the present invention enables the drone to autonomously avoid dynamic obstacles. Compared with the use of local path planning algorithms throughout the entire process, the present invention avoids the common situation of local path planning algorithms that easily fall into local optimal solutions. At the same time, the improved three-dimensional A-star global planning algorithm is used to plan checkpoints in advance before takeoff, thereby improving the overall energy efficiency of the algorithm.

[0149] Those skilled in the art will appreciate that the functional unit / module division in the embodiments of the present invention is for the purpose of convenience of description only, and in actual implementation, the functions may be reallocated to different physical or logical units as needed.

[0150] 1) Flexibility in function implementation: Each unit can be implemented using hardware modules, software modules, or a combination of hardware and software;

[0151] 2) Integration freedom: any functional unit can be implemented independently or integrated with other units;

[0152] 3) Workflow reference: For the specific workflow of each unit, please refer to the corresponding description of the aforementioned method embodiment.

[0153] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. A UAV path planning method for urban low-altitude environments, characterized in that: The following steps are involved: Obtain global map information, perform path planning, and obtain global path data; Processing the global path data, extracting global checkpoints, and obtaining a global checkpoint list; Controlling the UAV to fly along the global checkpoint and obtaining dynamic obstacle information during the flight; Based on the dynamic obstacle information, adjust the speed and direction of the drone to avoid dynamic obstacles; Repeatedly control the drone to fly along the global checkpoint and avoid dynamic obstacles until the drone reaches the flight endpoint.

2. The UAV path planning method for urban low-altitude environment according to claim 1 is characterized in that: Obtain global map information, perform path planning, and obtain global path data, including: Set the current node as the parent node; Based on the parent node, searching for several next nodes through a directional priority search mechanism; Selecting a node with the smallest moving cost from among the plurality of next nodes and setting it as a new parent node; Repeat the search for the next node until the end point is reached to obtain the global path data.

3. The UAV path planning method for urban low-altitude environment according to claim 2 is characterized in that: Based on the parent node, several next nodes are found through a directional priority search mechanism, specifically including: Establishing a coordinate system in the global map and calculating the distance parameters between the current node and the end point in the three axes of X, Y, and Z; If the X-axis distance parameter is greater than the other axial distance parameters, the neighbor nodes are expanded along the X-axis. If there is a traversable node, a path expansion operation is performed and the traversable node is loaded into the global path data. Based on the walkable nodes along the X axis, obtain the walkable nodes along other axes; If no feasible node is detected along the X axis, neighbor nodes along other axes are expanded in descending order according to the three axial distance parameters until the global map scan is completed or a feasible node is found.

4. The method for UAV path planning in urban low-altitude environment according to claim 3, characterized in that: Processing the global path data, extracting global checkpoints, and obtaining a global checkpoint list specifically includes: In the global path data, starting from the current node, three next nodes are successively taken out to determine whether the three nodes are collinear: If they are collinear, continue to move down one position from the current node, continue to take out three next nodes, and repeat the judgment of whether they are collinear until the end point is reached; If they are not collinear, the middle point of the three nodes is used as the global checkpoint; All global path data are traversed, the global checkpoints are extracted, and the global checkpoint list is obtained.

5. The method for UAV path planning in urban low-altitude environment according to claim 4, characterized in that: Based on the dynamic obstacle information, the speed and direction of the drone are adjusted to avoid dynamic obstacles, specifically including: Analyzing the dynamic obstacle information and constructing a speed obstacle area; Determine whether the dynamic obstacle is located within the speed obstacle area, and if so, adjust the speed and direction of the drone.

6. The method for UAV path planning in urban low-altitude environment according to claim 5, characterized in that: Analyzing the dynamic obstacle information and constructing a speed obstacle area specifically includes: Obtaining the dynamic obstacle radius and the drone radius; constructing an expansion sphere for the dynamic obstacle based on the radius of the dynamic obstacle and the radius of the drone; A conical surface tangent to the expanded sphere is constructed with the current position of the UAV as the vertex, and the area within the conical surface is the speed obstacle area.

7. The method for UAV path planning in urban low-altitude environment according to claim 6, characterized in that: The radius of the expanded sphere is the radius of the dynamic obstacle plus the safety radius of the drone.

8. The method for UAV path planning in urban low-altitude environment according to claim 7, characterized in that: Determining whether the dynamic obstacle is located within the speed obstacle area, and if so, adjusting the speed and direction of the drone, specifically including: Get the relative speed between the drone and the dynamic obstacle, and calculate the angle between the relative speed and the axis of the speed obstacle area: Wherein, θ' is the angle between the relative speed and the axis of the speed obstacle area; is the relative speed between the UAV and the dynamic obstacle; OP is the distance from the UAV position to the center of the inflated sphere; Calculate the cone half-apex angle of the speed obstacle area: Where θ is the cone half-apex angle of the speed obstacle area; r veh is the safety radius of the UAV; R ob + is the radius of the dynamic obstacle; If the included angle is smaller than the cone half-apex angle, the relative speed is within the speed obstacle area.

9. The method for UAV path planning in urban low-altitude environment according to claim 8, characterized in that: Determining whether the dynamic obstacle is located within the speed obstacle area, and if so, adjusting the speed and direction of the drone, further comprising: Add the velocity change vector to the drone's velocity to get the new velocity. The speed change vector is the minimum vector when the end point of the relative speed vector reaches the boundary of the speed obstacle area.

10. The method for UAV path planning in urban low-altitude environment according to claim 9, characterized in that: The speed change vector satisfies the following formula: Where, is the speed change vector; τ is the time interval for calculation; is the maximum acceleration of the UAV; The new speed is obtained by the following formula: Where, is the new speed; is the current speed of the drone; is the maximum speed of the drone.

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