A Feasibility Verification Method and System for UAV Waypoint Paths Based on Octree Space Partitioning

Abstract

This invention relates to the field of unmanned aerial vehicle (UAV) technology and discloses a method and system for verifying the feasibility of UAV waypoint paths based on octree spatial partitioning. This method addresses the technical problem of insufficient efficiency in existing methods for verifying the feasibility of UAV waypoint paths, thereby improving the efficiency of automatic path planning for UAVs. The method includes: obtaining the planned waypoint path to be verified; partitioning the path space involved in the waypoint path to be verified using the octree spatial partitioning method to obtain multiple spatial nodes under multiple partitioning levels, encoding each spatial node, and obtaining the node information of each spatial node; determining spatial connectivity based on the node information of the target spatial nodes involved in any two adjacent planned waypoints, using a preset connectivity threshold; and determining that if any two adjacent planned waypoints are not spatially connected, the waypoint path to be verified is an infeasible path.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) technology, and in particular to a method, system, electronic device, computer storage medium, and computer program product for verifying the feasibility of UAV waypoint paths based on octree spatial partitioning. Background Technology

[0002] Drone inspection refers to the use of drones to conduct regular or irregular inspections of specific areas or facilities. Compared with traditional manual inspections, drones can be equipped with various devices such as high-definition cameras and infrared thermal imagers to conduct detailed observations and data collection on the ground, buildings, and infrastructure.

[0003] In recent years, drone inspections have been widely applied in various fields such as power, petroleum, and transportation due to their high efficiency, safety, wide coverage, and accurate detection. However, regardless of the inspection scenario, improving automation efficiency and reducing risks during the inspection process are crucial aspects of drone technology development. Specifically, before executing a flight mission, a flight path is typically planned manually or automatically, and its feasibility is verified to enable efficient path evaluation or optimization. Especially in the scenario of automatic drone path planning, because a large amount of calculation and optimization needs to be completed within a certain time, the efficiency and accuracy of flight path feasibility calculations have a significant impact on the path planning effect, which is even more pronounced when the equipment to be inspected is complex.

[0004] Therefore, there is an urgent need for a method to verify the feasibility of waypoint paths for UAVs in complex scenarios, so as to quickly detect the flight safety of UAV paths, thereby reducing the time complexity of path planning algorithms and improving the execution efficiency of automatic planning algorithms in automatic path planning scenarios. Summary of the Invention

[0005] The main objective of this invention is to solve the technical problem of insufficient efficiency in existing methods for verifying the feasibility of waypoint paths for unmanned aerial vehicles (UAVs).

[0006] The first aspect of this invention provides a method for verifying the feasibility of UAV waypoint paths based on octree spatial partitioning, comprising: Obtain the waypoint paths to be verified generated by the drone during path planning; Based on the octree space partitioning method, the path space involved in the waypoint path to be verified is partitioned to obtain multiple spatial nodes under multiple partitioning levels, and each spatial node is encoded based on the node information of each spatial node. Based on the connectivity judgment threshold and the node codes of the target spatial nodes involved in any two adjacent planned waypoints in the waypoint path to be verified, the spatial connectivity between the planned waypoints is judged. If any two adjacent planned waypoints are not spatially connected, then the path to the waypoint to be verified is an infeasible path. If any two adjacent planned waypoints of the waypoint path to be verified are spatially connected, then the waypoint path to be verified is a feasible path.

[0007] Optionally, in a first implementation of the first aspect of the present invention, determining the spatial connectivity between planned waypoints based on a connectivity judgment threshold and the node codes of target spatial nodes involved in any two adjacent planned waypoints in the path to be verified includes: Starting from the initial level, for each pair of adjacent planned waypoints in the waypoint path to be verified, obtain the node code of the target space node involved in the adjacent planned waypoint, wherein the number of the target space node is at least one; For each target spatial node, the straight-line distance between the target spatial node and the adjacent planned waypoint is calculated based on the node information contained in the node code and the waypoint coordinates of the adjacent planned waypoints, and the relationship between the straight-line distance and the connectivity judgment threshold is determined; wherein, the connectivity judgment threshold includes a first judgment threshold and a second judgment threshold; If the straight-line distance is less than the first judgment threshold, then the space between the adjacent planned waypoints is not connected; If the straight-line distance is greater than the second judgment threshold, then the next target spatial node is obtained and the spatial connectivity judgment is continued until all target spatial nodes have been traversed. If none of the target spatial nodes trigger the disconnection judgment, then the adjacent planned waypoints are spatially connected.

[0008] Optionally, in a second implementation of the first aspect of the present invention, the step of determining the spatial connectivity between planned waypoints based on a connectivity judgment threshold and the node codes of target spatial nodes involved in any two adjacent planned waypoints in the path to be verified further includes: If the straight-line distance is between the first judgment threshold and the second judgment threshold, then the next level non-empty child node corresponding to the target space node is obtained, and all the non-empty child nodes of the next level are taken as the newly added target space node.

[0009] Optionally, in a third implementation of the first aspect of the present invention, the first judgment threshold is: The second judgment threshold is ; in, Indicates the safe flight radius of the drone. This represents the node scale value of the current partitioning level.

[0010] Optionally, in a fourth implementation of the first aspect of the present invention, the encoding of each spatial node based on the node information of each spatial node includes: The coding rules are determined based on the scale and information accuracy requirements of the path space involved in the waypoint path to be verified, and each spatial node is encoded based on the coding rules, and the node information of each spatial node is saved in the form of encoding. The node information includes: occupancy flag, number of non-empty child nodes, node scale value, and node center coordinates.

[0011] Optionally, in a fifth implementation of the first aspect of the present invention, the calculation of the straight-line distance between the target spatial node and the adjacent planned waypoints, using the node information included in the node encoding and the waypoint coordinates of the adjacent planned waypoints, includes: Extract the three-dimensional coordinates of the center point of the target spatial node based on the node encoding; The straight-line distance between the target spatial node and the adjacent planned waypoint is calculated based on the three-dimensional coordinates of the center point and the waypoint coordinates of the adjacent planned waypoints.

[0012] A second aspect of the present invention provides a system for verifying the feasibility of UAV waypoint paths based on octree spatial partitioning, comprising: The path acquisition module is used to acquire the waypoint paths to be verified generated by the UAV during path planning; The spatial partitioning module is used to partition the path space involved in the waypoint path to be verified based on the octree spatial partitioning method, obtain multiple spatial nodes under multiple partitioning levels, and encode each spatial node based on the node information of each spatial node. The verification execution module is used to determine the spatial connectivity between planned waypoints based on a connectivity judgment threshold and the node codes of target spatial nodes involved in any two adjacent planned waypoints in the path to be verified. If any two adjacent planned waypoints are not spatially connected, the path to be verified is an infeasible path. If any two adjacent planned waypoints in the path to be verified are spatially connected, the path to be verified is a feasible path.

[0013] A third aspect of the present invention provides a device for verifying the feasibility of UAV waypoint paths based on octree spatial partitioning, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor invokes the instructions in the memory to cause the device for verifying the feasibility of UAV waypoint paths based on octree spatial partitioning to perform the steps of the above-described method for verifying the feasibility of UAV waypoint paths based on octree spatial partitioning.

[0014] A fourth aspect of the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the steps of the above-described method for verifying the feasibility of UAV waypoint paths based on octree spatial partitioning.

[0015] The fifth aspect of the present invention provides a computer program product, including a computer program / instruction that, when executed by a processor, implements the steps of the above-described method for verifying the feasibility of UAV waypoint paths based on octree space partitioning.

[0016] The technical solution provided by this invention involves obtaining the waypoint path to be verified generated by the UAV during path planning; dividing the path space involved in the waypoint path to be verified based on the octree spatial partitioning method to obtain multiple spatial nodes under multiple partitioning levels, and encoding each spatial node based on the node information of each spatial node; judging the spatial connectivity between the planned waypoints based on the connectivity judgment threshold and the node codes of the target spatial nodes involved in any two adjacent planned waypoints in the waypoint path to be verified; if any two adjacent planned waypoints are not spatially connected, the waypoint path to be verified is an infeasible path; if any two adjacent planned waypoints in the waypoint path to be verified are spatially connected, the waypoint path to be verified is a feasible path. The method provided by this invention can efficiently and automatically verify the feasibility of UAV waypoint paths, thereby improving the UAV path planning effect and inspection safety.

[0017] Furthermore, the system, electronic device, computer-readable storage medium, and computer program product provided by this invention also solve the corresponding technical problems. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a schematic diagram of the verification process of the first embodiment of the UAV waypoint path feasibility verification method based on octree space partitioning in this invention. Figure 2This is a schematic diagram of spatial partitioning in the first embodiment of the UAV waypoint path feasibility verification method based on octree spatial partitioning in this invention. Figure 3 This is a schematic diagram of the connectivity judgment process for a single flight segment in the first embodiment of the UAV waypoint path feasibility verification method based on octree spatial partitioning in this invention. Figure 4 This is a schematic diagram of the first step of the second embodiment of the method for verifying the feasibility of UAV waypoint paths based on octree spatial partitioning in this invention. Figure 5 This is a schematic diagram of the second process of the second embodiment of the method for verifying the feasibility of UAV waypoint paths based on octree space partitioning in this invention. Figure 6 This is a schematic diagram of an embodiment of the UAV waypoint path feasibility verification system based on octree space partitioning in this invention. Figure 7 This is a schematic diagram of an embodiment of the UAV waypoint path feasibility verification device based on octree spatial partitioning in this invention. Figure 8 This is a schematic diagram illustrating the principle of a computer-readable medium according to an embodiment of the present invention. Detailed Implementation

[0019] Exemplary embodiments of the invention will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limiting the invention to the embodiments set forth herein. Rather, these exemplary embodiments are provided to make the invention more comprehensive and complete, and to facilitate a full communication of the inventive concept to those skilled in the art. The same reference numerals in the drawings denote the same or similar elements, components, or parts, and therefore repeated descriptions of them will be omitted.

[0020] Subject to the technical concept of this invention, the features, structures, characteristics or other details described in a particular embodiment may be combined in one or more other embodiments in a suitable manner.

[0021] In the description of specific embodiments, the features, structures, characteristics, or other details described in this invention are intended to enable those skilled in the art to fully understand the embodiments. However, it is not excluded that those skilled in the art can practice the technical solutions of this invention without one or more of the specific features, structures, characteristics, or other details.

[0022] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0023] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0024] The terms “and / or” or “and / or” include all combinations of any one or more of the listed items.

[0025] See Figures 1-3 The first embodiment of the UAV waypoint path feasibility verification method based on octree space partitioning in this invention includes: The process of this invention can be used in UAV inspection path planning scenarios. In the automatic iterative planning process of the UAV path, the feasibility of the generated UAV waypoint path is determined. The waypoint path includes multiple waypoints arranged in flight sequence. The line connecting each adjacent waypoint constitutes a flight segment, which is used for collision detection and safe distance judgment in feasibility verification. If any flight segment in the planned path of the UAV is not connected (e.g., due to obstacles or the path space does not meet the safe distance requirement), the waypoint path during flight needs to be adjusted or replanned.

[0026] It is understood that the executing entity of this invention can be a UAV waypoint path feasibility verification system based on octree space partitioning, or it can be a terminal or a server; the specific implementation is not limited here. This embodiment of the invention will be described using a server as an example.

[0027] S101. Obtain the waypoint path to be verified generated by the UAV during path planning; The waypoint path described in this embodiment refers to an ordered set of spatial points traversed by the UAV from its starting point to its destination. The waypoint path information includes at least the three-dimensional spatial position information of each waypoint, and sometimes additional information such as attitude, speed, and arrival time. A line connecting any two adjacent waypoints constitutes a flight segment.

[0028] In practical path planning, waypoint paths include, but are not limited to, geometric paths and parametric trajectories. A geometric path consists only of a sequence of spatial points, with the line connecting any two adjacent points forming a segment. A parametric trajectory adds a time dimension to the geometric path, including information such as velocity, acceleration, and yaw angle for subsequent flight control. Regardless of the waypoint path format, it is typically checked first whether each segment of the geometric path meets safety constraints.

[0029] In a preferred embodiment, the server monitors the path planning process and automatically reads and caches a new waypoint path when it is generated, and converts it into a unified format of validation input data structure.

[0030] S102. Based on the octree space partitioning method, the path space involved in the waypoint path to be verified is partitioned to obtain multiple spatial nodes under multiple partitioning levels, and each spatial node is encoded based on the node information of each spatial node. In this step, the scope of the path space involved in the waypoint path to be verified is first determined. The scope of the path space can be directly selected as the entire space of the scene to be inspected, and a spatial bounding box of the path space is generated; alternatively, a minimum bounding box can be generated based on the three-dimensional coordinates of all waypoints in the waypoint path to be verified, and a preset safety distance is calculated according to a preset expansion coefficient. The boundary of the minimum bounding box is then expanded outward according to the preset safety distance to obtain the spatial bounding box of the path space.

[0031] After obtaining the path spatial extent described in the form of a bounding box, as shown in the appendix. Figure 2 As shown, the path space is recursively partitioned using an octree spatial partitioning method. Specifically, a cube space corresponding to the root node is generated based on the path space range, and it is recursively divided into eight equal-sized subspaces according to the octree structure until a preset maximum partitioning depth is reached or the geometric complexity of obstacles contained in the subspace is lower than a preset threshold, thereby generating spatial nodes with multiple partitioning levels. Each spatial node corresponds to a subspace region, and each spatial node contains the association information between its parent node and child nodes. This method can discretize the continuous path space into a multi-level, structured set of nodes, providing an efficient spatial indexing foundation for subsequent collision detection of flight segments and obstacles and safe distance determination.

[0032] In one specific implementation, within the created octree nodes, nodes at different levels are encoded according to a preset encoding rule based on their scale level. This ensures that each node stores its information in encoded form. The node information includes, but is not limited to: an occupancy flag indicating whether the current node is empty, the number of non-empty child nodes at the next level, the node scale value corresponding to the current node's level, and the coordinates of the node's center position. The preset encoding rule includes, but is not limited to, the encoding order and the number of bits specified. Storing node information through encoding reduces query time during subsequent queries and further increases the efficiency of path feasibility assessment.

[0033] The occupancy marker is used to indicate whether there are obstacles within a node. If a node is not occupied, it is an empty node. Empty nodes can be skipped directly in the subsequent connectivity determination to reduce unnecessary distance calculations.

[0034] S103. Based on the connectivity judgment threshold and the node codes of the target spatial nodes involved in any two adjacent planned waypoints in the path to be verified, determine the spatial connectivity between the planned waypoints. After partitioning the space using an octree structure, the connectivity of flight segments between any two adjacent planned waypoints can be determined. In this case, any two points in three-dimensional space that are reachable by a straight line under constraints are considered connected; if there are obstacles between the lines connecting the two points, or if the constraints are not met, they are considered disconnected. (See also...) Figure 3 In step S103, for the current flight segment, following the direction from the root node of the octree to the leaf node, the following hierarchical judgment steps, such as S301-S307, are executed: S301. Obtain all target space nodes involved in the current flight segment at the current level; S302. For each target space node, filter according to the occupancy flag in its node information: if the node is empty, skip the node and continue to process the next node; if the node is not empty, proceed to S303. S303. Calculate the distance from the center point of the target spatial node to the straight line of the current flight segment, and determine the connectivity judgment threshold based on the safe flight radius of the UAV and the current node scale value, and compare the relationship between the distance and the threshold. S304. If the distance is less than the first judgment threshold, the current flight segment is determined to be disconnected, and the process proceeds directly to S104. S305. If the distance is greater than the second judgment threshold, it is determined that the current flight segment is connected at this node, and the next target space node of the current level is processed. S306. If the distance is between the first judgment threshold and the second judgment threshold, or equal to the first judgment threshold or the second judgment threshold, then obtain the next level non-empty child node of the target space node as the new target space node, and enter the next level to repeat S302 to S303. S307. Determine whether all target space nodes in the current flight segment at the current level are considered connected and that there is no need to enter the next level. If yes, the current flight segment is connected, and the process can proceed to the next flight segment. If no, the next target node at the current level is obtained, and the process continues.

[0035] S104. If any two adjacent planned waypoints are not spatially connected, then the path to the waypoint to be verified is an infeasible path. Based on the judgment in S103, if there are two adjacent planned waypoints corresponding to any flight segment that are not connected in space, then the current path to the waypoint to be verified has at least one different flight segment, and is directly marked as an infeasible path.

[0036] S105. If any two adjacent planned waypoints of the waypoint path to be verified are spatially connected, then the waypoint path to be verified is a feasible path.

[0037] Based on the judgment in S103, if all flight segments (i.e., between any two adjacent planned waypoints of the waypoint path to be verified) are spatially connected, then the current waypoint path to be verified is a feasible path.

[0038] In addition, it also includes: when all target spatial nodes involved between any two adjacent planned waypoints have been traversed and no disconnection determination has been triggered, then the adjacent planned waypoints are spatially connected.

[0039] The method provided in this embodiment of the invention can efficiently and automatically verify the feasibility of UAV waypoint paths. By setting thresholds and using a hierarchical judgment method, the path judgment can be directly interrupted when disconnected flight segments are found, which can save the algorithm's running time, improve the efficiency of searching and judgment, and thus improve the UAV path planning effect and inspection safety.

[0040] See Figures 4-5 The second embodiment of the UAV waypoint path feasibility verification method based on octree space partitioning in this invention includes: S401. Obtain the waypoint path to be verified generated by the UAV during path planning; In this embodiment, the path to be verified generated by the UAV during path planning is first obtained. The specific method can be referred to the content of S101 in the first embodiment mentioned above, and will not be repeated here.

[0041] S402. Based on the octree space partitioning method, the path space involved in the waypoint path to be verified is partitioned to obtain multiple spatial nodes under multiple partitioning levels. After obtaining the waypoint path to be verified, when partitioning the octree space, in addition to referring to the content of the first embodiment S102 above, the following content is also included: In this embodiment, when constructing the octree, the length of the cube space corresponding to the level of each node from the root node to the final leaf node is fixed. This length can be automatically divided or implemented according to the division level set manually.

[0042] For example, if the length of the 3D scene containing the entire waypoint path to be verified is (200, 100, 80), then when creating the octree, the root node scale is set to 200, the next level can be set to 100, and so on; the unit of scale is meters. Furthermore, because the area occupied by different inspection ranges and the density of objects in the scene vary in different usage scenarios, the scales of the root node and leaf nodes also differ. For example, in a substation scenario, the drone flight path must guarantee an accuracy of at least 10 centimeters; in this case, the scale of the highest-level leaf node should be less than 0.1.

[0043] S403. Determine the coding rules based on the scale and information accuracy requirements of the path space involved in the waypoint path to be verified, encode each spatial node based on the coding rules, and save the node information of each spatial node in the form of coding. In the created octree nodes, nodes at different levels are encoded according to a preset encoding rule based on their scale, so that each node stores the node information of each spatial node in encoded form. Preferably, the encoding is unique only within the current level, and different levels are distinguished using different encoding lists. In this embodiment, the node information includes, but is not limited to: Occupancy flag is used to indicate whether the current node is empty; The number of non-empty child nodes is used to record the number of non-empty child nodes in the next level of the current node. Node scale value, used to record the scale value corresponding to the level of the current node; The coordinates of the node's center position are used to record the position information of the current node's center point, represented in coordinate form, for example ( , , ).

[0044] In one specific implementation, the specific encoding rules are as follows: Complete encoding = Hierarchical scale encoding + Coordinate encoding + Coordinate encoding + Coordinate encoding; The encoding length is set based on the root node scale value and the maximum level scale value. For example, if the root node scale value is 200 and the maximum level scale value is 0.1, the encoding needs to be able to represent binary values ​​from 0 to 1999. If there are 20 scale levels, at least 5 bits are needed to represent the level number, and 11 bits are needed to represent the coordinate encoding. The specific encoding rules are as follows: Complete encoding = 5-bit hierarchical encoding + 11-bit x-coordinate encoding + 11-bit y-coordinate encoding + 11-bit z-coordinate encoding; The hierarchical encoding is performed using the hierarchical sequence number. When the hierarchical sequence number is converted to binary, it is padded with 0 to 11 binary bits at the beginning. The x-coordinate encoding is based on the number of segments within the range of the x-coordinate of the node's center point, such as the x-coordinate of the center point. The scale value of the node's level is The root node center is The root node hierarchy scale is ; x-encoding number = round up ( ); The final encoding of x is the binary code, which is padded with 0 to 11 binary bits before the encoding number. The encoding methods for y-coordinates and z-coordinates are the same as above, and will not be repeated here.

[0045] When querying any subsequent node, the node code is converted according to the node center, and then the node parameter information is directly matched with the corresponding node based on the encoding dictionary. The encoding dictionary is already established when the octree is built, and it is recorded using a key-value structure, where the key represents the node code and the value is the node parameter information.

[0046] S404. Based on the connectivity judgment threshold and the node codes of the target spatial nodes involved in any two adjacent planned waypoints in the path to be verified, determine the spatial connectivity between the planned waypoints. In a preferred embodiment, before performing the specific path connectivity determination, an initial level determination step is included. Taking the determination of connectivity between point A and point B under constraints as an example, firstly, an initial scale is calculated, and then the initial level for determining the spatial connectivity between planned waypoints is determined based on the initial scale. The specific calculation expression is as follows: in, This represents the straight-line Euclidean distance between point A and point B. Since substation planning typically constrains the safe distance for drones, and each point must meet this safe distance requirement, the AB segment is extended at both ends by a predetermined length as... Based on the initial scale and the octree partitioning, the starting level for subsequent connectivity judgments can be determined, which can further improve the judgment efficiency.

[0047] In one specific implementation, ,Right now Take the safe flight radius r of the drone.

[0048] In one specific implementation, The specific expression is given by the method of calculating the Minkowski distance: in, and Take the three-dimensional coordinates in sequence , , That is, Euclidean distance.

[0049] S405. If any two adjacent planned waypoints are not spatially connected, then the path to the waypoint to be verified is an infeasible path. S406. If any two adjacent planned waypoints of the waypoint path to be verified are spatially connected, then the waypoint path to be verified is a feasible path.

[0050] Furthermore, if all target spatial nodes involved between any two adjacent planned waypoints have been traversed and no disconnection determination has been triggered, then the adjacent planned waypoints are spatially connected.

[0051] See Figure 5 In a preferred embodiment, the specific judgment process involved in steps S404-S406 is as follows: S501, Begin; S502. Query the node corresponding to the starting point of the query path at this level; In this step, the starting point of the path is the starting point of the complete waypoint path to be verified. An empty candidate stack is then created based on the initial hierarchy size.

[0052] S503, Push the node onto the stack; Next, following the segment order, the connectivity between any two adjacent planned waypoints on the path to be verified is determined sequentially. For a segment, all nodes corresponding to that segment at the current level are found and pushed onto the stack.

[0053] S504, Pop the top node of the stack; After all nodes corresponding to this level are pushed onto the stack, the top node of the stack is popped out in sequence.

[0054] S505. Determine whether to proceed to the next scale based on the pop-up current node; For the popped node, it is determined whether to proceed to the next scale based on the current node's encoding. If the current node is an empty node, or the distance between the current node's center position and the current flight segment's path is too large (which will not affect connectivity) or too small (which will definitely affect connectivity), then the next scale determination is not required. In other cases, it is necessary to proceed to the next scale level, obtain all child nodes of the next scale level, and return to step S503 to push all child nodes of the next scale level onto the stack.

[0055] In one specific implementation, the determination of whether to proceed to the next scale is made based on the relationship between the distance between the current node's center position and the path of the current flight segment, and a first judgment threshold and a second judgment threshold. If the distance from the node's center point to the straight line of the path is between the first and second judgment thresholds, then proceeding to step S506 is required.

[0056] S506. Enter the next scale level and obtain all child nodes of the next scale level; After obtaining all child nodes of the next scale level, return to step S503 and push all child nodes onto the stack.

[0057] S507. The current node is an empty node; After S507, proceed to S511. In a preferred embodiment, if only non-empty nodes are pushed onto the stack at S503, while empty nodes are not pushed onto the stack, then step S507 can be omitted during the judgment at S505.

[0058] S508. The distance from the center point of the current node to the flight segment path is less than the first judgment threshold. The first judgment threshold is determined based on a preset minimum safe distance. In one specific implementation, using... This represents the distance from the node center point to the flight path, with the safe flight radius r of the UAV taken as the first judgment threshold. ;like Then proceed to S509.

[0059] S509, Path not connected, end; S510, The distance from the center point of the current node to the flight segment path is greater than the second judgment threshold; The second judgment threshold is determined based on a preset maximum safe distance. In one embodiment, the second judgment threshold is calculated based on the following expression. : ; Where r represents the safe flight radius of the UAV, and a represents the node scale value of the current level, which can be extracted based on the corresponding number of bits in the node code. This represents the distance from the center point of the node to a vertex of the node's cube. When At that time, it will enter S511.

[0060] Based on the first judgment threshold described in S508 and S510 Second judgment threshold In one specific embodiment, when determining whether to proceed to the next scale in step S505, when... Then, proceed to S506.

[0061] S511. Determine if the stack is empty; In S511, check if the stack is empty. If there are still nodes in the stack, return to S504 and pop the top node from the stack. If the stack is empty, proceed to S512.

[0062] S512. Determine whether the initial level node that was last popped from the stack is the node where the destination node is located. If the initial level node popped from the stack is the end node of the waypoint path, it means that all nodes involved in the entire waypoint path have been traversed, and proceed to S513; if the initial level node popped from the stack is not the end node of the waypoint path, it means that there are still segments whose connectivity has not been determined, and proceed to S514. S513, The path is connected; S514. Determine whether the neighboring nodes of the last popped initial level node are empty nodes along the direction towards the end. If the neighboring node of the last popped initial level node is an empty node, proceed to S515; if the neighboring node of the last popped initial level node is not an empty node, return to S503.

[0063] S515. Find the next non-empty neighboring node along the direction towards the endpoint; If in S514 it is determined that the neighboring node of the last popped initial level node is an empty node, then continue to search for the next non-empty neighboring node and push the node onto the stack, or until the end of the path. If the next neighboring node searched each time is an empty node, then the path is considered to be connected.

[0064] Furthermore, when all target spatial nodes involved between any two adjacent planned waypoints have been traversed and no disconnection has been determined, the current waypoint path to be verified is a feasible path.

[0065] The method provided in this embodiment of the invention can efficiently and automatically verify the feasibility of UAV waypoint paths. By setting thresholds and using a hierarchical judgment method, it can identify disconnections earlier by continuously querying reference scale values ​​and safety distances at nodes. When a disconnected segment is found, the path judgment is directly interrupted, saving algorithm runtime, improving the efficiency of search and judgment, and thus improving the UAV path planning effect and inspection safety. It is particularly suitable for scenarios such as substations with many devices and complex and messy tower and pipeline lines. The method provided in this embodiment can more effectively improve the efficiency of search and connectivity judgment, thereby reducing the overall algorithm computation time while ensuring the safety of UAV inspection.

[0066] The above describes the method for verifying the feasibility of UAV waypoint paths based on octree spatial partitioning in the embodiments of the present invention. The following describes the system for verifying the feasibility of UAV waypoint paths based on octree spatial partitioning in the embodiments of the present invention. (See reference...) Figure 6 One embodiment of the UAV waypoint path feasibility verification system based on octree space partitioning in this invention includes: The path acquisition module 601 is used to acquire the waypoint path to be verified generated by the UAV during path planning; The spatial partitioning module 602 is used to partition the path space involved in the waypoint path to be verified based on the octree spatial partitioning method, obtain multiple spatial nodes under multiple partitioning levels, and encode each spatial node based on the node information of each spatial node. The verification execution module 603 is used to determine the spatial connectivity between planned waypoints based on a connectivity judgment threshold and the node codes of target spatial nodes involved in any two adjacent planned waypoints in the path to be verified; if any two adjacent planned waypoints are not spatially connected, the path to be verified is an infeasible path; if any two adjacent planned waypoints in the path to be verified are spatially connected, the path to be verified is a feasible path.

[0067] The system provided in this embodiment of the invention can efficiently and automatically verify the feasibility of UAV waypoint paths, save algorithm running time, and improve the efficiency of UAV waypoint path feasibility judgment.

[0068] In another embodiment of this application, the verification execution module 603 is further configured to: Starting from the initial level, for each pair of adjacent planned waypoints in the waypoint path to be verified, obtain the node code of the target space node involved in the adjacent planned waypoint, wherein the number of the target space node is at least one; For each target spatial node, the straight-line distance between the target spatial node and the adjacent planned waypoint is calculated based on the node information contained in the node code and the waypoint coordinates of the adjacent planned waypoints, and the relationship between the straight-line distance and the connectivity judgment threshold is determined; wherein, the connectivity judgment threshold includes a first judgment threshold and a second judgment threshold; If the straight-line distance is less than the first judgment threshold, then the space between the adjacent planned waypoints is not connected; If the straight-line distance is greater than the second judgment threshold, then the next target spatial node is obtained and the spatial connectivity judgment is continued until all target spatial nodes have been traversed. If none of the target spatial nodes trigger the disconnection judgment, then the adjacent planned waypoints are spatially connected.

[0069] In another embodiment of this application, the connectivity determination threshold further includes a third determination threshold; The verification execution module 603 is further configured to: if the straight-line distance is between the first judgment threshold and the second judgment threshold, obtain the next-level non-empty child node corresponding to the target space node, and take all the non-empty child nodes of the next level as the newly added target space node.

[0070] In another embodiment of this application, the first judgment threshold is The second judgment threshold is ; in, Indicates the safe flight radius of the drone. This represents the node scale value of the current partitioning level.

[0071] In another embodiment of this application, the space partitioning module 602 is further configured to: The coding rules are determined based on the scale and information accuracy requirements of the path space involved in the waypoint path to be verified, and each spatial node is encoded based on the coding rules, and the node information of each spatial node is saved in the form of encoding. The node information includes: occupancy flag, number of non-empty child nodes, node scale value, and node center coordinates.

[0072] The system provided in this embodiment of the invention can efficiently and automatically verify the feasibility of UAV waypoint paths. By setting thresholds and using a hierarchical judgment method, it can directly interrupt the path judgment when disconnected flight segments are found, which can save the algorithm's running time, improve the efficiency of searching and judgment, and thus improve the UAV path planning effect and inspection safety.

[0073] Based on the same inventive concept, this specification also provides an electronic device for verifying the feasibility of UAV waypoint paths based on octree spatial partitioning. The following is a detailed description of the electronic device for verifying the feasibility of UAV waypoint paths based on octree spatial partitioning in this embodiment of the invention from the perspective of hardware processing.

[0074] Figure 7 This is a schematic diagram of an electronic device provided as an embodiment of this specification. Refer to the following... Figure 7 To describe the electronic device 700 according to this embodiment of the invention. Figure 7 The electronic device 700 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0075] like Figure 7 As shown, the electronic device 700 is presented in the form of a general-purpose computing device. The components of the electronic device 700 may include, but are not limited to: at least one processing unit 710, at least one storage unit 720, a bus 730 connecting different system components (including storage unit 720 and processing unit 710), a display unit 740, etc.

[0076] The storage unit stores program code that can be executed by the processing unit 710, causing the processing unit 710 to perform the steps described in the processing method section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 710 can perform actions such as... Figure 1 , Figure 3 , Figure 4 or Figure 5 The steps are shown.

[0077] The storage unit 720 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 7201 and / or a cache storage unit 7202, and may further include a read-only memory unit (ROM) 7203.

[0078] The storage unit 720 may also include a program / utility 7204 having a set (at least one) program module 7205, such program module 7205 including but not limited to: an operating system, 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.

[0079] Bus 730 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0080] Electronic device 700 can also communicate with one or more external devices 100 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 700, and / or with any device that enables electronic device 700 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 750. Furthermore, electronic device 700 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) via network adapter 760. Network adapter 760 can communicate with other modules of electronic device 700 via bus 730. It should be understood that, although... Figure 7 As not shown, other hardware and / or software modules may be used in conjunction with the electronic device 700, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0081] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described in this invention can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this invention can be embodied in the form of a software product, which can be stored in a computer-readable storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, or network device, etc.) to execute the method described above according to this invention. When the computer program is executed by a data processing device, it enables the computer-readable medium to implement the method described above, i.e.: as... Figure 1 , Figure 3 , Figure 4 or Figure 5 The method shown.

[0082] Figure 8 This is a schematic diagram of a computer-readable medium provided for embodiments of this specification.

[0083] accomplish Figure 1 , Figure 3 , Figure 4 or Figure 5The computer program of the method shown can be stored on one or more computer-readable media. A computer-readable medium can be a readable signal medium or a readable storage medium. A readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0084] The computer-readable storage medium may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable storage medium may also be any readable medium other than a readable storage medium, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0085] In addition, the present invention also provides a computer program product, including a computer program / instruction that, when executed by a processor, implements the method for verifying the feasibility of UAV waypoint paths based on octree space partitioning as described in any of the above embodiments.

[0086] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0087] In summary, the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that in practice, general-purpose data processing devices such as microprocessors or digital signal processors (DSPs) can be used to implement some or all of the functions of some or all of the components according to the embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such programs implementing the present invention can be stored on a computer-readable medium or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0088] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the present invention is not inherently related to any specific computer, virtual device, or electronic device, and various general-purpose devices can also implement the present invention. The above descriptions are merely specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0089] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0090] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for verifying the feasibility of UAV waypoint paths based on octree spatial partitioning, characterized in that, include: Obtain the waypoint paths to be verified generated by the drone during path planning; Based on the octree space partitioning method, the path space involved in the waypoint path to be verified is partitioned to obtain multiple spatial nodes under multiple partitioning levels, and each spatial node is encoded based on the node information of each spatial node. Based on the connectivity judgment threshold and the node codes of the target spatial nodes involved in any two adjacent planned waypoints in the waypoint path to be verified, the spatial connectivity between the planned waypoints is judged. Starting from the initial level, for each pair of adjacent planned waypoints in the waypoint path to be verified, obtain the node code of the target space node involved in the adjacent planned waypoint, wherein the number of the target space node is at least one; For each target spatial node, the straight-line distance between the target spatial node and the adjacent planned waypoint is calculated based on the node information contained in the node code and the waypoint coordinates of the adjacent planned waypoints, and the relationship between the straight-line distance and the connectivity judgment threshold is determined; wherein, the connectivity judgment threshold includes a first judgment threshold and a second judgment threshold; If the straight-line distance is less than the first judgment threshold, then the space between the adjacent planned waypoints is not connected; If the straight-line distance is between the first judgment threshold and the second judgment threshold, then the next level non-empty child node corresponding to the target space node is obtained, and all the non-empty child nodes of the next level are taken as the newly added target space node; If the straight-line distance is greater than the second judgment threshold, then the next target spatial node is obtained and the spatial connectivity judgment is continued until all target spatial nodes have been traversed. If none of the target spatial nodes trigger the disconnection judgment, then the adjacent planned waypoints are spatially connected. If any two adjacent planned waypoints are not spatially connected, then the path to the waypoint to be verified is an infeasible path. If any two adjacent planned waypoints of the waypoint path to be verified are spatially connected, then the waypoint path to be verified is a feasible path.

2. The method for verifying the feasibility of UAV waypoint paths based on octree spatial partitioning according to claim 1, characterized in that, The first judgment threshold is , and the second judgment threshold is ; wherein, represents the safe flight radius of the UAV, represents the node scale value of the current partition level. 3.The UAV waypoint path feasibility verification method based on octree space partitioning according to claim 1 or 2, characterized in that, The encoding of each spatial node based on the node information of each spatial node includes: The coding rules are determined based on the scale and information accuracy requirements of the path space involved in the waypoint path to be verified, and each spatial node is encoded based on the coding rules, and the node information of each spatial node is saved in the form of encoding. The node information includes: occupancy flag, number of non-empty child nodes, node scale value, and node center coordinates.

4. The UAV waypoint path feasibility verification method based on octree space partitioning according to claim 3, wherein, The node information contained in the node code and the waypoint coordinates of the adjacent planned waypoints are used to calculate the straight-line distance of the path from the target spatial node to the adjacent planned waypoint, including: Extract the three-dimensional coordinates of the center point of the target spatial node based on the node encoding; The straight-line distance between the target spatial node and the adjacent planned waypoint is calculated based on the three-dimensional coordinates of the center point and the waypoint coordinates of the adjacent planned waypoints.

5. An unmanned aerial vehicle waypoint path feasibility verification system based on octree space partitioning, characterized in that, The UAV waypoint path feasibility verification system based on octree spatial partitioning includes: The path acquisition module is used to acquire the waypoint paths to be verified generated by the UAV during path planning; The spatial partitioning module is used to partition the path space involved in the waypoint path to be verified based on the octree spatial partitioning method, obtain multiple spatial nodes under multiple partitioning levels, and encode each spatial node based on the node information of each spatial node. The verification execution module is used to determine the spatial connectivity between planned waypoints based on a connectivity threshold and the node codes of target spatial nodes involved in any two adjacent planned waypoints in the path to be verified. Starting from the initial level, for each pair of adjacent planned waypoints in the path to be verified, the node codes of the target spatial nodes involved in the adjacent planned waypoints are obtained, wherein the number of target spatial nodes is at least one. For each target spatial node, the straight-line distance of the path from the target spatial node to the adjacent planned waypoint is calculated based on the node information contained in the node code and the waypoint coordinates of the adjacent planned waypoints, and the relationship between the straight-line distance and the connectivity threshold is determined. The connectivity threshold includes a first threshold and a second threshold. If the straight-line distance is less than the first threshold, then... If the adjacent planned waypoints are not spatially connected, and the straight-line distance is between the first and second judgment thresholds, then the next-level non-empty child node corresponding to the target spatial node is obtained, and all the non-empty child nodes of the next level are added as new target spatial nodes; if the straight-line distance is greater than the second judgment threshold, then the next target spatial node is obtained and spatial connectivity judgment is continued until all target spatial nodes have been traversed. If none of the target spatial nodes trigger the non-connectivity judgment, then the adjacent planned waypoints are spatially connected; if any two adjacent planned waypoints are not spatially connected, then the waypoint path to be verified is an infeasible path; if any two adjacent planned waypoints of the waypoint path to be verified are spatially connected, then the waypoint path to be verified is a feasible path.

6. An unmanned aerial vehicle waypoint path feasibility verification device based on octree space partitioning, characterized by, The device for verifying the feasibility of UAV waypoint paths based on octree spatial partitioning includes: a memory and at least one processor, wherein the memory stores instructions. The at least one processor invokes the instructions in the memory to cause the UAV waypoint path feasibility verification device based on octree space partitioning to perform the steps of the UAV waypoint path feasibility verification method based on octree space partitioning as described in any one of claims 1-4.

7. A computer-readable storage medium having stored thereon computer programs / instructions, characterized in that, When the program / instruction is executed by the processor, it implements the steps of the UAV waypoint path feasibility verification method based on octree space partitioning as described in any one of claims 1-4.

8. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the steps of the method for verifying the feasibility of UAV waypoint paths based on octree space partitioning as described in any one of claims 1-4.

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