An automatic route planning method for civil delivery drones to avoid obstacles

Abstract

The present invention discloses an automatic route planning method for a civilian delivery drone for bypassing obstacles, including: obtaining the delivery requirements of the civilian delivery drone; determining the requirements for range, load, mount, and speed, and matching the drone based on the determined requirements; according to the no-fly zone and restricted-fly zone, using an improved route planning method based on an adaptive variable weight A* algorithm, planning the shortest path and the safest path for the drone to complete the task, and performing a comprehensive score in combination with the environment; taking the path with the highest comprehensive score as the final planned path. The present invention takes solving the civilian unmanned delivery task as the starting point, comprehensively considers the real-time changes of the external environment, uses real-time obstacle detection combined with historical obstacle avoidance information, improves the safety planning capability of the drone, and enables the drone to achieve safe and autonomous path planning in different environments such as downtown, mountainous areas, and plateaus.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicle (UAV) route planning, and in particular to an automatic route planning method for a civilian delivery UAV for bypassing obstacles. Background Art

[0002] Drones have the advantages of three-dimensional maneuverability, speed and flexibility. In urban environments with complex ground environments, numerous buildings and many obstacles, using drones to transport key materials such as small equipment, medicines, accessories, etc. has obvious advantages in solving the problem of the last 5 kilometers of supply delivery.

[0003] Most of the volume of urban material delivery drones is used to accommodate goods. Their routes do not conflict with ground traffic. They are equipped with sensing and obstacle avoidance devices, and can autonomously complete delivery tasks under the remote monitoring of professional pilots, greatly improving the drone's transportation capacity and solving problems such as many ground obstacles, slow speed, and poor reliability in urban areas. However, urban airspace is complex, mainly manifested in the many airspace control restrictions and the threat of high obstacles at medium and low altitudes. On the premise of ensuring the safety of material delivery, it is also necessary to take into account the delivery efficiency under high-time-sensitive tasks to meet the urban transportation needs for high-value, small-volume, and time-sensitive material delivery. Therefore, it is of great significance to study an automatic route planning method for civil delivery drones that bypass obstacles, and it has important application value. Summary of the invention

[0004] The purpose of the present invention is to provide a method for automatically planning the route of a civilian delivery drone for bypassing obstacles in order to address the problems existing in the above-mentioned prior art.

[0005] The technical solution to achieve the purpose of the present invention is: a method for automatically planning a route for a civilian delivery drone to avoid obstacles, the method comprising the following steps:

[0006] Step 1, obtaining the delivery requirements of the civil delivery drone, including the starting point location information, the end point location information, the transportation distance and the material information; the starting point location information includes the starting point longitude and latitude, altitude, the end point longitude and altitude, the end point location information includes the end point longitude and latitude, altitude, and the material information includes the material type, quantity and weight;

[0007] Step 2, determining the requirements of range, load, load, and speed according to the transportation distance and material information, and matching the drone based on the determined requirements;

[0008] Step 3: According to the restricted flight zone, the improved route planning algorithm based on the adaptive variable weight A* algorithm is used to plan the shortest path for the UAV to complete the task; according to the no-fly zone and restricted flight zone, the improved route planning method based on the adaptive variable weight A* algorithm is used to plan the safest path for the UAV to complete the task, and the shortest path and the safest path are scored respectively;

[0009] Step 4, scoring the environment of the area through which the shortest path and the safest path pass;

[0010] Step 5, taking a weighted average of the scoring results of step 3 and step 4, and obtaining comprehensive scores of the shortest path and the safest path respectively;

[0011] Step 6: The path with the highest comprehensive score is taken as the final planned path.

[0012] Furthermore, the matching of the drones based on the determined requirements in step 2 specifically includes:

[0013] Step 2-1, select the drone that meets the following conditions as the matching drone;

[0014] The four attributes of range, load, payload, and speed all meet the requirements of the closest correspondence;

[0015] Step 2-2, if there is no drone that meets the above conditions, then:

[0016] Prioritize the four attributes of range, load, payload, and speed;

[0017] For the attribute with the highest priority, the drone that is closest to the corresponding requirement is selected as the matching drone.

[0018] Furthermore, step 2 also includes:

[0019] Calculate and record the drone matching score.

[0020] Furthermore, the calculation method of the drone matching score is:

[0021] Calculate the degree of proximity of the four attribute values ​​of the drone, namely, range, load, mount, and speed, to the corresponding requirements;

[0022] Arrange the calculated proximity values ​​in descending order;

[0023] The highest proximity value is taken as the drone matching score.

[0024] Furthermore, the improved route planning algorithm based on the adaptive variable weight A* algorithm described in step 3 specifically includes:

[0025] For the heuristic function and cost function in the A* algorithm, Manhattan distance is used for calculation;

[0026] The path generated by the adaptive variable weight A* algorithm is globally optimized. The optimization process is as follows: the path point set is derivatized twice to generate an inflection point set. Starting from the starting position, it is determined whether there are obstacles between any consecutive points A, B, and C in the inflection point set. If there are no obstacles, the A and C inflection points are directly connected. Then, it is determined whether there are obstacles between A, C, and D in the inflection point set. If there are no obstacles, the redundant node C is removed. If there are obstacles, the inflection point C is retained. From the starting point to the target point, it is determined in turn whether the inflection points can be deleted, and the inflection point set is generated as the final path.

[0027] Furthermore, the improved route planning algorithm based on the adaptive variable weight A* algorithm specifically includes the following steps:

[0028] Step 3-1, read the input map to be planned;

[0029] Step 3-2, initialize the algorithm parameters and determine the initial position and target position of the drone;

[0030] Step 3-3, create a path set list, a plannable path set OPEN list and a closed set CLOSE list respectively, and initialize the plannable path set OPEN list to only have the initial position of the drone, and the closed set CLOSE list to be empty;

[0031] Step 3-4, check the nodes stored in the OPEN list of the plannable path set. When there is only the initial position in the OPEN list of the plannable path set, it means that the drone has no feasible path; otherwise, use the cost function to select the minimum node in the OPEN list of the plannable path set;

[0032] Step 3-5, delete the minimum node from the OPEN list of the plannable path set and add it to the CLOSE list of the closed set, and determine whether the point is the same as the target position. If the point is the same as the target position, it means that the iteration has reached the target point and the iteration process ends; if the current node is not the same as the target position, the current node is used as the current starting position, and the OPEN list of the plannable path set is continued to be expanded, and the feasible path point of the drone is added to the CLOSE list of the closed set, and then step 3-6 is executed;

[0033] Step 3-6, output the planned path set in the closed set CLOSE list, read the obstacle database, calculate whether there is an obstacle on the current path according to the latitude, longitude and altitude coordinates, and query whether there is a corresponding historical obstacle avoidance route according to the obstacle coordinates. If there is a historical obstacle avoidance route, add the historical obstacle avoidance route to the corresponding segment of the planned path, otherwise execute step 3-7;

[0034] Step 3-7, continue to detect the path collection list, if there is no obstacle in the current path connection, delete the redundant turning points, and save the redundant node set after removal, and then perform path smoothing.

[0035] Step 3-8, output the final planned path.

[0036] Furthermore, the method further includes, before step 3, executing: constructing a route planning model, specifically including:

[0037] Delineate the latitude, longitude and altitude ranges of the planned airspace;

[0038] Convert geodetic coordinates into spatial coordinates;

[0039] Normalize the coordinates;

[0040] Path planning conditions are set based on existing route obstacles and flight restrictions in the airspace;

[0041] Randomly generate a starting point that is not within the obstacle;

[0042] Generate a series of safe flight routes and output them as waypoint data.

[0043] Furthermore, the scoring of the shortest path and the safest path in step 3 specifically includes:

[0044] For the safest path described, the score is full marks;

[0045] For the shortest path, the proportion of the path in the restricted flight zone to the entire path is calculated, and the proportion value is used as the score value.

[0046] Furthermore, the step 4 of scoring the environment of the area through which the shortest path and the safest path pass specifically includes:

[0047] Identify the environmental factors that affect the score;

[0048] Different levels are divided according to each environmental factor;

[0049] Establish a scoring table, including the one-to-one correspondence between the different levels of each environmental factor and the scoring value;

[0050] The environment is scored according to the scoring table.

[0051] Furthermore, the method further comprises:

[0052] Step 7, during the flight of the UAV, the UAV collects and analyzes overhead obstacles on the path in real time through the camera, sets conditions in combination with airspace route obstacles and flight restriction constraints, and calculates the obstacle detour route according to the route planning algorithm based on the adaptive variable weight A* algorithm to perform temporary obstacle avoidance; after the UAV returns, the obstacle avoidance process on the route is updated to the obstacle library and obstacle avoidance path library, and the historical obstacle avoidance route is stored.

[0053] Compared with the prior art, the present invention has the following significant advantages:

[0054] (1) Taking into account the safety and timeliness issues faced by route planning, an automatic route planning method for civil delivery drones with the goal of avoiding obstacles was designed, realizing the entire process from item demand to route planning to precise obstacle avoidance.

[0055] (2) Taking the solution of civil unmanned delivery missions as the starting point, the real-time changes of the external environment are comprehensively considered, and the real-time obstacle detection is combined with historical obstacle avoidance information to improve the safety planning capability of UAVs, so that UAVs can achieve safe and autonomous path planning in different environments such as downtown, mountainous areas, and plateaus.

[0056] (3) Guided by the task of unmanned transportation of high-value materials in urban environments, a route planning algorithm that balances safety and timeliness is proposed.

[0057] (4) A method for constructing and updating an obstacle library based on actual measurements and flights is proposed. The real-time path planning and obstacle information of obstacle avoidance flight are recorded to form historical data, which provides data support for the dynamic update of subsequent data and route planning.

[0058] (5) Based on the use of the adaptive variable weight A* algorithm, the present invention adds a historical obstacle avoidance data adaptation method to integrate historical obstacle avoidance information into path planning, thereby improving the obstacle avoidance capability of the A* algorithm.

[0059] The present invention is further described in detail below in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 Schematic diagram of the principle of automatic route planning method for civil delivery UAV to avoid obstacles.

[0061] Figure 2 A flowchart for constructing a route planning model in one embodiment.

[0062] Figure 3 A schematic diagram of a process for deleting redundant nodes in an embodiment.

[0063] Figure 4 The figure is a flow chart of a route planning algorithm based on an adaptive variable weight A* algorithm in one embodiment. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0065] It should be noted that if the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back...), the directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0066] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0067] In one embodiment, in combination Figure 1 , a method for automatic route planning of a civilian delivery UAV for obstacle avoidance is proposed, the method comprising the following steps:

[0068] Step 1, obtaining the delivery requirements of the civil delivery drone, including the starting point location information, the end point location information, the transportation distance and the material information; the starting point location information includes the starting point longitude and latitude, altitude, the end point longitude and altitude, the end point location information includes the end point longitude and latitude, altitude, and the material information includes the material type, quantity and weight;

[0069] Step 2, determining the requirements of range, load, load, and speed according to the transportation distance and material information, and matching the drone based on the determined requirements;

[0070] Step 3: According to the restricted flight zone, the improved route planning algorithm based on the adaptive variable weight A* algorithm is used to plan the shortest path for the UAV to complete the task; according to the no-fly zone and restricted flight zone, the improved route planning method based on the adaptive variable weight A* algorithm is used to plan the safest path for the UAV to complete the task, and the shortest path and the safest path are scored respectively;

[0071] Step 4, scoring the environment of the area through which the shortest path and the safest path pass;

[0072] Step 5, taking a weighted average of the scoring results of step 3 and step 4, and obtaining comprehensive scores of the shortest path and the safest path respectively;

[0073] Step 6: The path with the highest comprehensive score is taken as the final planned path.

[0074] Further, in one embodiment, the matching of the drone based on the determined requirements in step 2 specifically includes:

[0075] Step 2-1, select the drone that meets the following conditions as the matching drone;

[0076] The four attributes of range, load, payload, and speed all meet the requirements of the closest correspondence;

[0077] Step 2-2, if there is no drone that meets the above conditions, then:

[0078] Prioritize the four attributes of range, load, payload, and speed;

[0079] For the attribute with the highest priority, the drone that is closest to the corresponding requirement is selected as the matching drone.

[0080] Here preferably, the priorities are arranged in descending order as follows: load, range, payload, and speed.

[0081] Furthermore, in one embodiment, step 2 further includes:

[0082] Calculate and record the drone matching score.

[0083] Here, in some embodiments, the drone matching score is calculated as follows:

[0084] Calculate the degree of proximity of the four attribute values ​​of the drone, namely, range, load, mount, and speed, to the corresponding requirements;

[0085] Arrange the calculated proximity values ​​in descending order;

[0086] The highest proximity value is taken as the drone matching score.

[0087] Further, in one of the embodiments, the improved route planning algorithm based on the adaptive variable weight A* algorithm described in step 3 specifically includes:

[0088] (1) Manhattan distance is used to calculate the heuristic function and cost function in the A* algorithm;

[0089] (2) Perform global secondary optimization on the path generated by the adaptive variable weight A* algorithm. The optimization process is as follows: generate an inflection point set after secondary derivation of the path point set. Starting from the starting position, determine whether there is an obstacle between any consecutive points A, B, and C in the inflection point set. If there is no obstacle, directly connect the inflection points A and C. Then determine whether there is an obstacle between A, C, and D in the inflection point set. If there is no obstacle, remove the redundant node C (such as Figure 3 As shown in the figure), if there is an obstacle, the turning point C is retained, and the turning points that can be deleted are judged in sequence from the starting point to the target point, and a turning point set is generated as the final path.

[0090] Here, the Manhattan distance can be used to reduce the computational cost, thereby increasing the computational speed of the heuristic function and the cost function, thereby improving the computational efficiency of the A* algorithm.

[0091] Here, global quadratic optimization can effectively solve the problem that the adaptive variable weight A* algorithm has too many redundant nodes compared to the A* algorithm due to the different weights of the heuristic function and the cost function in the adaptive variable weight algorithm.

[0092] Preferably, in some embodiments, combined Figure 4 The improved route planning algorithm based on the adaptive variable weight A* algorithm specifically includes the following steps:

[0093] Step 3-1, read the input map to be planned;

[0094] Step 3-2, initialize the algorithm parameters and determine the initial position and target position of the drone;

[0095] Step 3-3, create a path set list, a plannable path set OPEN list and a closed set CLOSE list respectively, and initialize the plannable path set OPEN list to only have the initial position of the drone, and the closed set CLOSE list to be empty;

[0096] Step 3-4, check the nodes stored in the OPEN list of the plannable path set. When there is only the initial position in the OPEN list of the plannable path set, it means that the drone has no feasible path; otherwise, use the cost function to select the minimum node in the OPEN list of the plannable path set;

[0097] Step 3-5, delete the minimum node from the OPEN list of the plannable path set and add it to the CLOSE list of the closed set, and determine whether the point is the same as the target position. If the point is the same as the target position, it means that the iteration has reached the target point and the iteration process ends; if the current node is not the same as the target position, the current node is used as the current starting position, and the OPEN list of the plannable path set is continued to be expanded, and the feasible path point of the drone is added to the CLOSE list of the closed set, and then step 3-6 is executed;

[0098] Step 3-6, output the planned path set in the closed set CLOSE list, read the obstacle database, calculate whether there is an obstacle on the current path according to the latitude, longitude and altitude coordinates, and query whether there is a corresponding historical obstacle avoidance route according to the obstacle coordinates. If there is a historical obstacle avoidance route, add the historical obstacle avoidance route to the corresponding segment of the planned path, otherwise execute step 3-7;

[0099] Step 3-7, continue to detect the path collection list, if there is no obstacle in the current path connection, delete the redundant turning points, and save the redundant node set after removal, and then perform path smoothing.

[0100] Step 3-8, output the final planned path.

[0101] Furthermore, in one embodiment, the method further includes, before step 3, executing: constructing a route planning model, and step 3 performs path planning based on the model; combining Figure 2 ,Constructing the route planning model specifically includes:

[0102] Delineate the latitude, longitude and altitude ranges of the planned airspace;

[0103] Convert geodetic coordinates into spatial coordinates;

[0104] Normalize the coordinates; here, since the rectangular coordinates have large values, they will affect the convergence speed and accuracy of the model, and the storage space will double accordingly, so normalization is performed;

[0105] Path planning conditions are set based on existing route obstacles and flight restrictions in the airspace;

[0106] Randomly generate a starting point that is not within the obstacle;

[0107] Generate a series of safe flight routes and output them as waypoint data.

[0108] Further, in one embodiment, the scoring of the shortest path and the safest path in step 3 specifically includes:

[0109] For the safest path described, the score is full marks;

[0110] For the shortest path, the proportion of the path in the restricted flight zone to the entire path is calculated, and the proportion value is used as the score value.

[0111] Furthermore, in one embodiment, the step 4 of scoring the environment of the area through which the shortest path and the safest path pass specifically includes:

[0112] Identify environmental factors that affect the score, such as wind speed, rainfall, visibility, etc.;

[0113] Different levels are divided according to various environmental factors, such as visibility level 1, 2, 3, etc.;

[0114] Establish a scoring table, including the one-to-one correspondence between the different levels of each environmental factor and the scoring value;

[0115] The environment is scored according to the scoring table.

[0116] Furthermore, in one embodiment, the method further comprises:

[0117] Step 7, during the flight of the UAV, the UAV collects and analyzes overhead obstacles on the path in real time through the camera, sets conditions in combination with airspace route obstacles and flight restriction constraints, and calculates the obstacle detour route according to the route planning algorithm based on the adaptive variable weight A* algorithm to perform temporary obstacle avoidance; after the UAV returns, the obstacle avoidance process on the route is updated to the obstacle library and obstacle avoidance path library, and the historical obstacle avoidance route is stored.

[0118] In one embodiment, a system for automatically planning a route for a civilian delivery drone for avoiding obstacles is provided, the system comprising the following steps:

[0119] The first module is used to obtain the delivery requirements of the civil delivery drone, including the starting point location information, the end point location information, the transportation distance and the material information; the starting point location information includes the starting point longitude and latitude, altitude, the end point longitude and altitude, the end point location information includes the end point longitude and latitude, altitude, and the material information includes the material type, quantity and weight;

[0120] The second module is used to determine the requirements of range, load, load, and speed according to the transportation distance and material information, and match the drone based on the determined requirements;

[0121] The third module is used to plan the shortest path for the UAV to complete the task according to the restricted flight zone using the improved route planning algorithm based on the adaptive variable weight A* algorithm; according to the no-fly zone and restricted flight zone, the improved route planning method based on the adaptive variable weight A* algorithm is used to plan the safest path for the UAV to complete the task, and score the shortest path and the safest path respectively;

[0122] The fourth module is used to score the environment of the area through which the shortest path and the safest path pass;

[0123] The fifth module is used to perform weighted averaging on the scoring results of the third module and the fourth module to obtain comprehensive scores of the shortest path and the safest path respectively;

[0124] The sixth module is used to select the path with the highest comprehensive score as the final planned path.

[0125] Furthermore, in one embodiment, the system further comprises:

[0126] The seventh module is used to collect and analyze overhead obstacles on the path in real time through the camera during the flight of the UAV, set conditions in combination with airspace route obstacles and flight restriction constraints, and calculate the obstacle detour route according to the route planning algorithm based on the adaptive variable weight A* algorithm to perform temporary obstacle avoidance; after the UAV returns, the obstacle avoidance process on the route is updated to the obstacle library and obstacle avoidance path library, and the historical obstacle avoidance route is stored.

[0127] For the specific limitations of the automatic route planning system for civil delivery drones for bypassing obstacles, please refer to the limitations of the automatic route planning method for civil delivery drones for bypassing obstacles above, which will not be repeated here. Each module in the above-mentioned automatic route planning system for civil delivery drones for bypassing obstacles can be implemented in whole or in part through software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0128] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the following is achieved:

[0129] Step 1, obtaining the delivery requirements of the civil delivery drone, including the starting point location information, the end point location information, the transportation distance and the material information; the starting point location information includes the starting point longitude and latitude, altitude, the end point longitude and altitude, the end point location information includes the end point longitude and latitude, altitude, and the material information includes the material type, quantity and weight;

[0130] Step 2, determining the requirements of range, load, load, and speed according to the transportation distance and material information, and matching the drone based on the determined requirements;

[0131] Step 3: According to the restricted flight zone, the improved route planning algorithm based on the adaptive variable weight A* algorithm is used to plan the shortest path for the UAV to complete the task; according to the no-fly zone and restricted flight zone, the improved route planning method based on the adaptive variable weight A* algorithm is used to plan the safest path for the UAV to complete the task, and the shortest path and the safest path are scored respectively;

[0132] Step 4, scoring the environment of the area through which the shortest path and the safest path pass;

[0133] Step 5, taking a weighted average of the scoring results of step 3 and step 4, and obtaining comprehensive scores of the shortest path and the safest path respectively;

[0134] Step 6: The path with the highest comprehensive score is taken as the final planning path;

[0135] Step 7, during the flight of the UAV, the camera is used to collect and analyze overhead obstacles on the path in real time, and conditions are set in combination with airspace route obstacles and flight restriction constraints, and the obstacle detour route is calculated according to the route planning algorithm based on the adaptive variable weight A* algorithm to perform temporary obstacle avoidance; after the UAV returns, the obstacle avoidance process on the route is updated to the obstacle library and obstacle avoidance path library, and the historical obstacle avoidance route is stored.

[0136] For the specific limitations of each step, please refer to the limitations of the automatic route planning method for civil delivery drones to avoid obstacles mentioned above, which will not be repeated here.

[0137] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the computer program implements:

[0138] Step 1, obtaining the delivery requirements of the civil delivery drone, including the starting point location information, the end point location information, the transportation distance and the material information; the starting point location information includes the starting point longitude and latitude, altitude, the end point longitude and altitude, the end point location information includes the end point longitude and latitude, altitude, and the material information includes the material type, quantity and weight;

[0139] Step 2, determining the requirements of range, load, load, and speed according to the transportation distance and material information, and matching the drone based on the determined requirements;

[0140] Step 3: According to the restricted flight zone, the improved route planning algorithm based on the adaptive variable weight A* algorithm is used to plan the shortest path for the UAV to complete the task; according to the no-fly zone and restricted flight zone, the improved route planning method based on the adaptive variable weight A* algorithm is used to plan the safest path for the UAV to complete the task, and the shortest path and the safest path are scored respectively;

[0141] Step 4, scoring the environment of the area through which the shortest path and the safest path pass;

[0142] Step 5, taking a weighted average of the scoring results of step 3 and step 4, and obtaining comprehensive scores of the shortest path and the safest path respectively;

[0143] Step 6: The path with the highest comprehensive score is taken as the final planning path;

[0144] Step 7, during the flight of the UAV, the camera is used to collect and analyze overhead obstacles on the path in real time, and conditions are set in combination with airspace route obstacles and flight restriction constraints, and the obstacle detour route is calculated according to the route planning algorithm based on the adaptive variable weight A* algorithm to perform temporary obstacle avoidance; after the UAV returns, the obstacle avoidance process on the route is updated to the obstacle library and obstacle avoidance path library, and the historical obstacle avoidance route is stored.

[0145] For the specific limitations of each step, please refer to the limitations of the automatic route planning method for civil delivery drones to avoid obstacles mentioned above, which will not be repeated here.

[0146] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for automatically planning a route for a civilian delivery drone to avoid obstacles, characterized in that: The method comprises the following steps: Step 1, obtaining the delivery requirements of the civil delivery drone, including the starting point location information, the end point location information, the transportation distance and the material information; the starting point location information includes the starting point longitude and latitude, and the altitude; the end point location information includes the end point longitude and latitude, and the altitude; the material information includes the material type, quantity, and weight; Step 2, determining the requirements of range, load, load, and speed according to the transportation distance and material information, and matching the drone based on the determined requirements; Step 3: According to the no-fly zone, the improved route planning algorithm based on the adaptive variable weight A* algorithm is used to plan the shortest path for the UAV to complete the task; according to the no-fly zone and the restricted flight zone, the improved route planning method based on the adaptive variable weight A* algorithm is used to plan the safest path for the UAV to complete the task, and the shortest path and the safest path are scored respectively; Step 4, scoring the environment of the area through which the shortest path and the safest path pass; Step 5, taking a weighted average of the scoring results of step 3 and step 4, and obtaining comprehensive scores of the shortest path and the safest path respectively; Step 6: The path with the highest comprehensive score is taken as the final planned path.

2. The method for automatically planning a route for a civilian delivery drone to avoid obstacles according to claim 1 is characterized in that: Step 2, matching the drone based on the determined requirements, specifically includes: Step 2-1, select the drone that meets the following conditions as the matching drone; The four attributes of range, load, payload, and speed all meet the most corresponding requirements; Step 2-2, if there is no drone that meets the above conditions, then: Prioritize the four attributes of range, load, payload, and speed; For the attribute with the highest priority, the drone that is closest to the corresponding requirement is selected as the matching drone.

3. The method for automatically planning a route for a civilian delivery drone to avoid obstacles according to claim 1 is characterized in that: Step 2 also includes: Calculate and record the drone matching score.

4. The method for automatically planning a route for a civilian delivery drone to avoid obstacles according to claim 3 is characterized in that: The calculation method of the drone matching score is: Calculate the degree of proximity of the four attribute values ​​of the drone, namely, range, load, mount, and speed, to the corresponding requirements; Arrange the calculated proximity values ​​in descending order; The highest proximity value is taken as the drone matching score.

5. The method for automatically planning a route for a civilian delivery drone to avoid obstacles according to claim 1 is characterized in that: The improved route planning algorithm based on the adaptive variable weight A* algorithm described in step 3 specifically includes: For the heuristic function and cost function in the A* algorithm, Manhattan distance is used for calculation; The path generated by the adaptive variable weight A* algorithm is globally optimized. The optimization process is as follows: the path point set is derivatized twice to generate an inflection point set. Starting from the starting position, it is determined whether there are obstacles between any consecutive points A, B, and C in the inflection point set. If there are no obstacles, the A and C inflection points are directly connected. Then, it is determined whether there are obstacles between A, C, and D in the inflection point set. If there are no obstacles, the redundant node C is removed. If there are obstacles, the inflection point C is retained. From the starting point to the target point, it is determined in turn whether the inflection points can be deleted, and the inflection point set is generated as the final path.

6. The method for automatically planning a route for a civilian delivery drone to avoid obstacles according to claim 5 is characterized in that: The improved route planning algorithm based on the adaptive variable weight A* algorithm specifically includes the following steps: Step 3-1, read the input map to be planned; Step 3-2, initialize the algorithm parameters and determine the initial position and target position of the drone; Step 3-3, create a path set list, a plannable path set OPEN list and a closed set CLOSE list respectively, and initialize the plannable path set OPEN list to only have the initial position of the drone, and the closed set CLOSE list to be empty; Step 3-4, check the nodes stored in the OPEN list of the plannable path set. When there is only the initial position in the OPEN list of the plannable path set, it means that the drone has no feasible path; otherwise, use the cost function to select the minimum node in the OPEN list of the plannable path set; Step 3-5, delete the minimum node from the OPEN list of the plannable path set and add it to the CLOSE list of the closed set, and determine whether the point is the same as the target position. If the point is the same as the target position, it means that the iteration has reached the target point and the iteration process ends; if the current node is not the same as the target position, the current node is used as the current starting position, and the OPEN list of the plannable path set is continued to be expanded, and the feasible path point of the drone is added to the CLOSE list of the closed set, and then step 3-6 is executed; Step 3-6, output the planned path set in the closed set CLOSE list, read the obstacle database, calculate whether there is an obstacle on the current path according to the latitude, longitude and altitude coordinates, and query whether there is a corresponding historical obstacle avoidance route according to the obstacle coordinates. If there is a historical obstacle avoidance route, add the historical obstacle avoidance route to the corresponding segment of the planned path, otherwise execute step 3-7; Step 3-7, continue to detect the path collection list, if there is no obstacle on the current path connection, delete the redundant turning points, save the redundant node set after removal, and then perform path smoothing; Step 3-8, output the final planned path.

7. The method for automatically planning a route for a civilian delivery drone to avoid obstacles according to claim 5 is characterized in that: The method further includes, before step 3, executing: constructing a route planning model, specifically including: Delineate the latitude, longitude and altitude ranges of the planned airspace; Convert geodetic coordinates into spatial coordinates; Normalize the coordinates; Path planning conditions are set based on existing route obstacles and flight restrictions in the airspace; Randomly generate a starting point that is not within the obstacle; Generate a series of safe flight routes and output them as waypoint data.

8. The method for automatically planning a route for a civilian delivery drone to avoid obstacles according to claim 1 is characterized in that: The shortest path and the safest path are scored respectively in step 3, specifically including: For the safest path described, the score is full marks; For the shortest path, the proportion of the path in the restricted flight zone to the entire path is calculated, and the proportion value is used as the score value.

9. The method for automatically planning a route for a civilian delivery drone to avoid obstacles according to claim 1, characterized in that: Step 4 of scoring the environment of the area through which the shortest path and the safest path pass includes: Identify the environmental factors that affect the score; Different levels are divided according to each environmental factor; Establish a scoring table, including the one-to-one correspondence between the different levels of each environmental factor and the scoring value; The environment is scored according to the scoring table.

10. The method for automatically planning a route for a civilian delivery drone to avoid obstacles according to claim 1, characterized in that: The method further comprises: Step 7, during the flight of the UAV, the UAV collects and analyzes overhead obstacles on the path in real time through the camera, sets conditions in combination with airspace route obstacles and flight restriction constraints, and calculates the obstacle detour route according to the route planning algorithm based on the adaptive variable weight A* algorithm to perform temporary obstacle avoidance; after the UAV returns, the obstacle avoidance process on the route is updated to the obstacle library and obstacle avoidance path library, and the historical obstacle avoidance route is stored.

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