An intelligent inspection bionic robot path planning method and system

Abstract

The present invention relates to the technical field of robots, and specifically discloses a path planning method and system for an intelligent inspection bionic robot. This application first obtains the inspection path fork information and the destination location of the intelligent inspection bionic robot, then obtains multiple fork trajectory information according to the inspection path fork information, and then obtains the dynamic obstacle information and static obstacle information encountered by the intelligent inspection bionic robot. The second driving delay time is obtained according to the dynamic obstacle information, and the third driving delay time is obtained according to the static obstacle information. Finally, the optimal path is obtained according to the second driving delay time and the third driving delay time. In this way, it is possible to comprehensively evaluate the time cost of different path branches when encountering various obstacles, select the path with the minimum total delay time as the optimal path, and thus ensure that the intelligent inspection bionic robot can complete the task in a shorter time or complete the plan closer to the preset inspection time.

Description

Technical Field

[0001] The present invention relates to the technical field of robots, and in particular to a path planning method and system for an intelligent inspection bionic robot. Background Art

[0002] With the continuous progress of technology, intelligent robots have been widely used in the fields of industrial inspection, logistics transportation, security monitoring, etc. Especially in complex or dangerous environments, such as oil and gas pipelines, power equipment, mines and other fields, inspection robots need to be able to complete inspection tasks quickly and accurately.

[0003] The existing path planning methods for inspection robots mainly perform inspections based on pre-set paths. However, the pre-set paths will change due to environmental changes, which also leads to the need to wait again for path setting. This method results in inspection delays for inspection robots, and further causes the inspection robots to be unable to perform inspections according to the preset time. Therefore, an intelligent inspection bionic robot path planning method is needed to solve the above problems. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent inspection bionic robot path planning method and system to solve the technical problems raised in the above background art.

[0005] To achieve the above purpose, the present invention provides the following technical solutions:

[0006] An intelligent inspection bionic robot path planning method, comprising:

[0007] Obtaining the inspection path information of the intelligent inspection bionic robot, wherein the inspection path information includes inspection path fork information and destination location;

[0008] Obtaining multiple path fork node positions according to the inspection path fork information, and obtaining multiple fork trajectory information from the multiple path fork node positions to the destination location;

[0009] Obtaining the obstacle feature information encountered by the intelligent inspection bionic robot, wherein the obstacle feature information includes dynamic obstacle information and static obstacle information;

[0010] Obtaining the moving trajectory information of the dynamic obstacle according to the dynamic obstacle information, and traversing the coincidence points of the moving trajectory information and multiple path fork node positions to obtain multiple first coincidence path fork node positions;

[0011] Obtaining corresponding multiple first travel delay times according to the multiple first coincidence path fork node positions, and screening the multiple first travel delay times according to the minimum time to obtain a second travel delay time;

[0012] Obtain the fixed positions of the static obstacles according to the static obstacle information, and traverse the coincidence points of the fixed positions and the positions of multiple path fork nodes to obtain the second coincidence path fork node positions;

[0013] Obtain the corresponding third driving delay time according to the second coincidence path fork node positions;

[0014] Obtain the optimal path according to the second driving delay time and the third driving delay time, and perform path planning for the intelligent inspection bionic robot according to the optimal path.

[0015] Preferably, the step of obtaining the positions of multiple path fork nodes according to the inspection path fork information includes:

[0016] Obtain the main inspection path information of the intelligent inspection bionic robot according to the inspection path information, and obtain the corresponding continuous path points according to the main inspection path information;

[0017] Obtain multiple branch path node information according to the inspection path fork information, and obtain the corresponding end positions of multiple branch paths according to the multiple branch path node information;

[0018] Obtain the merging positions of the end positions of multiple branch paths into the continuous path points;

[0019] Obtain the corresponding path fork starting points according to the multiple merging positions, and use the multiple path fork starting points as the positions of multiple path fork nodes.

[0020] Preferably, the step of traversing the coincidence points of the movement trajectory information and the positions of multiple path fork nodes to obtain multiple first coincidence path fork node positions includes:

[0021] Obtain the moving speed and moving direction of the dynamic obstacle according to the movement trajectory information, and obtain the movement trajectory curve of the dynamic obstacle within a preset time according to the moving speed and moving direction;

[0022] Map the movement trajectory curve and the positions of multiple path fork nodes to a preset grid map to obtain multiple movement trajectory coordinate points and multiple path fork node coordinate points;

[0023] Calculate multiple Euclidean distances according to each movement trajectory coordinate point and the multiple path fork node coordinate points;

[0024] Successively determine whether the multiple Euclidean distances are less than a preset threshold;

[0025] If the Euclidean distance is less than a preset threshold, the moving trajectory coordinate points corresponding to the Euclidean distance that meet the conditions are used as multiple first coincidence path fork node positions that coincide with the path fork node coordinates.

[0026] Preferably, the step of obtaining corresponding multiple first travel delay times according to the multiple first coincidence path fork node positions includes:

[0027] Obtain the path capacity according to the first coincidence path fork node position, where the path capacity represents the maximum passing volume;

[0028] Based on radar detection, obtain multiple collision volumes of the first coincidence path fork node position;

[0029] Obtain the path flow according to the multiple collision volumes and a preset passing time;

[0030] Calculate the path flow density according to the path capacity and the path flow, where the calculation formula is:

[0031] ;

[0032] Among them, Q(M) represents the path flow density, L(j) represents the path flow, and L(l) represents the path capacity;

[0033] Obtain the first travel delay time according to the path flow density;

[0034] Repeat the steps of obtaining the path capacity according to the first coincidence path fork node position to obtaining the first travel delay time according to the path flow density to traverse all the first coincidence path fork node positions and obtain multiple first travel delay times.

[0035] Preferably, the step of obtaining the preferred path according to the second travel delay time and the third travel delay time includes:

[0036] Obtain the corresponding first preferred fork path according to the second travel delay time;

[0037] Obtain the corresponding second preferred fork path according to the third travel delay time;

[0038] Obtain the preset inspection path of the intelligent inspection bionic machine;

[0039] Map the first preferred fork path, the second preferred fork path and the preset inspection path into a two-dimensional coordinate system for splicing to obtain a spliced path;

[0040] Take the spliced path as the preferred path.

[0041] Preferably, the step of path planning for the intelligent inspection bionic robot according to the preferred path includes:

[0042] Obtain the driving trajectory of the preferred path according to the preferred path;

[0043] Obtain multiple turning angles according to the driving trajectory of the path;

[0044] Determine whether multiple turning angles are less than a preset angle;

[0045] If the turning angle is less than the preset angle, smooth and fit the turning angles less than the preset angle to obtain a first fitting point and a second fitting point;

[0046] Insert smooth nodes between the first fitting point and the second fitting point to generate a smooth turning angle, and optimize the turning angle of the selected path driving trajectory according to the smooth turning angle to obtain a smooth preferred path;

[0047] Perform path planning for the intelligent inspection bionic robot according to the smooth preferred path.

[0048] The present invention also provides an intelligent inspection bionic robot path planning system, including:

[0049] A first acquisition module, configured to acquire the inspection path information of the intelligent inspection bionic robot, where the inspection path information includes inspection path fork information and destination location;

[0050] A second acquisition module, configured to acquire the positions of multiple path fork nodes according to the inspection path fork information, and acquire multiple fork trajectory information from the positions of multiple path fork nodes to the destination location;

[0051] A third acquisition module, configured to acquire the obstacle feature information encountered by the intelligent inspection bionic robot, where the obstacle feature information includes dynamic obstacle information and static obstacle information;

[0052] A fourth acquisition module, configured to acquire the moving trajectory information of the dynamic obstacle according to the dynamic obstacle information, and traverse the coincidence points of the moving trajectory information and the positions of multiple path fork nodes to obtain multiple first coincidence path fork node positions;

[0053] A fifth acquisition module, configured to acquire corresponding multiple first driving delay times according to the multiple first coincidence path fork node positions, and screen the multiple first driving delay times according to the minimum time to obtain a second driving delay time;

[0054] A sixth acquisition module, configured to obtain the fixed positions of static obstacles according to the static obstacle information, perform coincidence point traversal on the fixed positions and the positions of multiple path fork nodes, and obtain the second coincidence path fork node positions;

[0055] A seventh acquisition module, configured to obtain the corresponding third driving delay time according to the second coincidence path fork node positions;

[0056] An eighth acquisition module, configured to obtain an optimal path according to the second driving delay time and the third driving delay time, and perform path planning on the intelligent inspection bionic robot according to the optimal path.

[0057] Preferably, the second acquisition module includes:

[0058] A first replacement unit, configured to obtain the main inspection path information of the intelligent inspection bionic robot according to the inspection path information, and obtain the corresponding path continuous point positions according to the main inspection path information;

[0059] A second replacement unit, configured to obtain multiple branch path node information according to the inspection path fork information, and obtain the corresponding multiple branch path end positions according to the multiple branch path node information;

[0060] A third replacement unit, configured to obtain multiple merging point positions where the multiple branch path end positions are merged into the path continuous point positions;

[0061] A fourth replacement unit, configured to obtain the corresponding path fork start points according to the multiple merging point positions, and use the multiple path fork start points as the multiple path fork node positions.

[0062] This application also provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the above method are implemented.

[0063] This application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0064] The beneficial effects of the present application are as follows: First, the inspection path information of the intelligent inspection bionic robot is obtained, where the inspection path information includes inspection path fork information and destination location. Then, multiple fork trajectory information is obtained according to the inspection path fork information. Next, the obstacle feature information encountered by the intelligent inspection bionic robot is obtained, where the obstacle feature information includes dynamic obstacle information and static obstacle information. The second travel delay time is obtained according to the dynamic obstacle information, and the third travel delay time is obtained according to the static obstacle information. Finally, an optimal path is obtained according to the second travel delay time and the third travel delay time, and path planning is performed on the intelligent inspection bionic robot according to the optimal path. In this way, by combining the second travel delay time caused by dynamic obstacles and the third travel delay time caused by static obstacles, the time cost of different path branches when encountering various obstacles can be comprehensively evaluated, and the path with the minimum total delay time is selected as the optimal path, which can minimize the time loss caused by obstacles during the inspection process of the robot, and at the same time, there is no need to wait for the set path, thus ensuring that the intelligent inspection bionic robot can complete the task in a shorter time or complete the plan closer to the preset inspection time. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is a schematic flowchart of the method according to an embodiment of the present application.

[0066] Figure 2 It is a schematic structural diagram of the system according to an embodiment of the present application.

[0067] Figure 3 It is a schematic internal structure diagram of a computer device according to an embodiment of the present application.

[0068] The realization, functional features and advantages of the purpose of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0069] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0070] As Figures 1-3 shown, the present application provides a method for path planning of an intelligent inspection bionic robot, including:

[0071] S1. Obtain the inspection path information of the intelligent inspection bionic robot, where the inspection path information includes inspection path fork information and destination location;

[0072] S2. Obtain multiple path fork node positions according to the inspection path fork information, and obtain multiple fork trajectory information from the multiple path fork node positions to the destination location;

[0073] S3. Obtain the obstacle feature information encountered by the intelligent inspection bionic robot, where the obstacle feature information includes dynamic obstacle information and static obstacle information;

[0074] S4. Obtain the movement trajectory information of the dynamic obstacle according to the dynamic obstacle information, and traverse the coincidence points between the movement trajectory information and the positions of multiple path fork nodes to obtain multiple first coincidence path fork node positions;

[0075] S5. Obtain the corresponding multiple first travel delay times according to the multiple first coincidence path fork node positions, and screen the multiple first travel delay times according to the minimum time to obtain the second travel delay time;

[0076] S6. Obtain the fixed position of the static obstacle according to the static obstacle information, and traverse the coincidence points between the fixed position and the positions of multiple path fork nodes to obtain the second coincidence path fork node positions;

[0077] S7. Obtain the corresponding third travel delay time according to the second coincidence path fork node positions;

[0078] S8. Obtain the optimal path according to the second travel delay time and the third travel delay time, and perform path planning for the intelligent inspection bionic robot according to the optimal path.

[0079] As described in the above steps S1 - S8, the existing path planning method for inspection robots mainly conducts inspections based on pre - set paths. However, the pre - set paths may change due to environmental changes, which also leads to the need to wait again for path setting. This way results in inspection delays for the inspection robot, and further causes the inspection robot to be unable to conduct inspections according to the preset time. Therefore, the present invention first obtains the inspection path information of the intelligent inspection bionic robot. Among them, the inspection path information includes inspection path fork information and destination location. In this way, the inspection path information can provide a decision - making basis for the subsequent path planning of the inspection robot. Then, according to the inspection path fork information, the positions of multiple path fork nodes are obtained, and multiple fork trajectory information from these multiple path fork node positions to the destination location is obtained. Among them, the fork trajectory information is an alternative path on the pre - set path, and its function is to avoid obstacles. This refines the intermediate link of path planning, transforming the abstract inspection path information into specific and operable path fork nodes and trajectory information. This enables subsequent calculations and decisions to be based on more accurate path data, improving the accuracy and effectiveness of path planning. At the same time, it also provides basic selection conditions for path screening after encountering obstacles. Next, the obstacle feature information of the intelligent inspection bionic robot encountering obstacles is obtained. Among them, the obstacle feature information includes dynamic obstacle information and static obstacle information. In this way, real - time acquisition of obstacle information is the key for the robot to conduct inspections safely and efficiently in a complex environment. The acquisition of dynamic obstacle information and static obstacle information enables the robot to comprehensively understand the obstacle situation in the environment, providing a basis for avoiding obstacles and reducing delays in the future. For example, knowing that there are dynamic pedestrians or vehicles ahead, as well as fixed obstacles such as pillars or walls, the robot can plan a detour route in advance and also provide a basis for path optimization. Since the movement of dynamic obstacles increases the complexity of path planning and it is necessary to predict its impact on the robot's path, the movement trajectory information of the dynamic obstacle is obtained according to the dynamic obstacle information, and the movement trajectory information and the positions of multiple path fork nodes are traversed for coincidence points, obtaining multiple first coincidence path fork node positions. In this way, by analyzing the relationship between the movement trajectory of the dynamic obstacle and the path fork node positions, the fork nodes that may coincide with the dynamic obstacle trajectory are determined. These nodes are potential alternative path nodes. Identifying them in advance helps calculate the subsequent delay time and select the optimal path. For example, if the trajectory of the dynamic obstacle coincides with the position of fork node D, the robot needs to consider the possible delay when passing through point D during path planning. At the same time, through this method of traversing coincidence points, the key nodes affected by the dynamic obstacle can be accurately found, providing targeted data for subsequent delay time calculation and path optimization.If this step is not taken, the robot may blindly select a path when facing dynamic obstacles, resulting in greater delays. Then, based on the positions of multiple first overlapping path fork nodes, the corresponding multiple first travel delay times are obtained, and the multiple first travel delay times are screened according to the minimum time to obtain the second travel delay time. Calculating the travel delay time of each fork node that may be affected by dynamic obstacles reflects the time loss that the robot may encounter at these nodes. Screening out the minimum delay time helps to determine the relatively optimal path branch to reduce the impact of dynamic obstacles on the inspection efficiency. For example, after calculation, the first travel delay time of fork node E is 5 seconds, and that of fork node F is 3 seconds. Then, selecting node F may make the overall inspection time shorter. At the same time, the multiple first travel delay times can quantify the path preference index and provide data support for the robot to complete the inspection on time. Then, based on the static obstacle information, the fixed positions of the static obstacles are obtained, and the fixed positions and the positions of multiple path fork nodes are traversed for coincidence points to obtain the second overlapping path fork node positions. Among them, the processing method of static obstacle information is similar to that of dynamic obstacles. Determining the fork nodes that coincide with the positions of static obstacles, these nodes will also affect the robot's travel and may require detouring or path adjustment, providing a basis for calculating the delay time caused by static obstacles in the future. At the same time, finding the relevant fork nodes through the coincidence point traversal can more specifically evaluate the impact of static obstacles on the path and provide complete data support for comprehensively considering the impacts of dynamic and static obstacles and selecting the final optimal path. If static obstacles are ignored, the robot may directly collide with the obstacles and fail to complete the task. Then, based on the second overlapping path fork node positions, the corresponding third travel delay times are obtained. In this way, the impact of static obstacles on the robot needs to be specifically quantified, and the calculation of the third travel delay time can accurately reflect its impact on the path efficiency. Only by obtaining the respective and comprehensive delay times of dynamic and static obstacles can the optimal path be scientifically selected in the subsequent steps to ensure that the robot can complete the inspection task within the preset time during the inspection process. Finally, based on the second travel delay time and the third travel delay time, the optimal path is obtained, and the path planning of the intelligent inspection bionic robot is carried out according to the optimal path. In this way, by combining the second travel delay time caused by dynamic obstacles and the third travel delay time caused by static obstacles, the time costs of different path branches when encountering various obstacles can be comprehensively evaluated, and the path with the minimum total delay time is selected as the optimal path, which can make the robot minimize the time loss caused by obstacles during the inspection process and does not need to wait for the set path, thereby ensuring that the intelligent inspection bionic robot can complete the task in a shorter time or complete the plan closer to the preset inspection time.

[0080] In one embodiment, step S2 of obtaining the positions of multiple path fork nodes according to the inspection path fork information includes:

[0081] S201. Obtain the main inspection path information of the intelligent inspection bionic robot according to the inspection path information, and obtain the corresponding continuous path points according to the main inspection path information;

[0082] S202. Obtain multiple branch path node information according to the inspection path fork information, and obtain the corresponding end positions of multiple branch paths according to the multiple branch path node information;

[0083] S203. Obtain the end positions of multiple branch paths and incorporate them into the multiple merging points of the continuous path points;

[0084] S204. Obtain the corresponding path fork starting points according to the multiple merging points, and use the multiple path fork starting points as the positions of multiple path fork nodes.

[0085] As described in the above steps S201-S204, the present invention first obtains the main inspection path information of the intelligent inspection bionic robot based on the inspection path information, and obtains the corresponding path continuous points based on the inspection main path information. Obtaining the inspection main path information in this way helps to determine the main travel direction and route framework of the robot in the entire inspection area, which is the basis for building a complete path planning. In a complex inspection environment, first determining the main path is the key to effective path planning. The main path information provides a basic direction guide for the entire path planning, so that the robot can have a main travel direction as a reference when faced with many possible path choices, avoiding confusion and disorder in path planning. At the same time, it is not enough to only know the general direction of the main path. The path continuous points can more accurately portray the actual form of the main path. In the subsequent operations such as calculating the path length and judging the positional relationship with other nodes or obstacles, accurate path description is indispensable, which can improve the accuracy and reliability of path planning. In actual inspection scenarios, the path is often not a single straight line, but has multiple branches. In order to enable the robot to make a reasonable path selection at the fork, multiple branch path node information is obtained according to the inspection path fork information, wherein the branch path node information is the secondary bifurcation path of the inspection path fork information. Only two bifurcation situations are listed in the present invention, but the number of times the path can be bifurcated does not affect the path selection, because the present invention only selects the point position connected to the continuous point position of the path, and other bifurcated paths that are not connected are invalid paths, and the corresponding multiple branch path terminal positions are obtained according to the multiple branch path node information, and obtaining the branch path node information can clarify the possible branch positions on the main path. These nodes are the key decision points that the robot needs to make path selection during the inspection process. At the same time, understanding the position of the branch path node helps the robot prepare in advance to deal with different path selection situations, and obtaining the branch path terminal position provides the robot with a potential target range for each branch path. This allows the robot to have a preliminary understanding of the destination and possible areas of each branch path when considering path selection, so as to better evaluate the impact of different branch paths on the completion of inspection tasks. In order to achieve efficient path planning, the main path and branch paths need to be considered as a whole. By incorporating the merging point positions, a unified model containing all key path nodes can be constructed. Therefore, it is necessary to obtain multiple branch path end positions that are merged into the multiple merging point positions of the path continuous points. In this way, the branch path end positions are merged into the path continuous points, which can organically combine the information of the main path and the branch path to form a complete path network representation.In this way, the robot can perform path planning and decision-making in a unified path system, which improves the integrity and coherence of path planning. Finally, corresponding path fork starting points are obtained according to multiple merging point positions. Among them, the path fork starting points are reversely deduced through the merging point positions, aiming to eliminate invalid branches. And the multiple path fork starting points are used as multiple path fork node positions. The path fork starting point is the position where the robot actually faces path selection during actual inspection. Determining it as the path fork node position enables the robot to clearly know at which specific points it needs to make decisions. These node positions become the core reference points for subsequent calculation of path trajectories, evaluation of the influence of obstacles, and selection of the optimal path.

[0086] In one embodiment, step S4 of traversing the coincidence points of the movement trajectory information and multiple path fork node positions to obtain multiple first coincidence path fork node positions includes:

[0087] S401. Obtain the moving speed and moving direction of the dynamic obstacle according to the movement trajectory information, and obtain the moving trajectory curve of the dynamic obstacle within a preset time according to the moving speed and moving direction;

[0088] S402. Map the moving trajectory curve and multiple path fork node positions to a preset grid map to obtain multiple moving trajectory coordinate points and multiple path fork node coordinate points;

[0089] S403. Calculate multiple Euclidean distances according to each moving trajectory coordinate point and multiple path fork node coordinate points;

[0090] S404. Sequentially determine whether multiple Euclidean distances are less than a preset threshold;

[0091] If the Euclidean distance is less than the preset threshold, the moving trajectory coordinate points corresponding to the Euclidean distances that meet the conditions are used as multiple first coincidence path fork node positions that coincide with the path fork node coordinate points.

[0092] As described in the above steps S401 - S404, the present invention first obtains the moving speed and moving direction of the dynamic obstacle according to the moving trajectory information, and obtains the moving trajectory curve of the dynamic obstacle within a preset time according to the moving speed and moving direction. By calculating the moving trajectory curve through the moving speed and direction, the robot can know in advance the approximate position range of the dynamic obstacle within a certain period in the future. This helps the robot to consider the influence of the dynamic obstacle in advance during path planning, avoid collisions with it, and improve the safety and smoothness of the inspection process. Then, the moving trajectory curve and the positions of multiple path fork nodes are mapped into a preset grid map to obtain multiple moving trajectory coordinate points and multiple path fork node coordinate points. Mapping the moving trajectory curve and the path fork node positions into the preset grid map provides a unified spatial reference system for both. In this unified coordinate system, the robot can conveniently compare and analyze the positions of the dynamic obstacle and the path nodes, facilitating subsequent calculation of the relationships between them. At the same time, obtaining the moving trajectory coordinate points and the path fork node coordinate points enables the positions of the dynamic obstacle and the path nodes to be represented in the map in the form of accurate coordinates. This helps the robot to more accurately judge the relative position relationship between the dynamic obstacle and the path fork nodes, providing an accurate data basis for operations such as calculating the Euclidean distance. Then, multiple Euclidean distances are calculated according to each of the moving trajectory coordinate points and the multiple path fork node coordinate points. Calculating the Euclidean distance can accurately quantify the degree of proximity between each moving trajectory coordinate point and the path fork node coordinate point. Through the distance values, the robot can intuitively understand the distance relationship between the dynamic obstacle and each path fork node during the movement process. Then, it is sequentially determined whether multiple of the Euclidean distances are less than a preset threshold. If the Euclidean distance is less than the preset threshold, the moving trajectory coordinate point corresponding to the Euclidean distance that meets the condition is used as multiple first coincident path fork node positions that coincide with the path fork node coordinate point. By comparing with the preset threshold, it can accurately judge whether the moving trajectory coordinate point and the path fork node coordinate point are close enough to determine whether they coincide. Finding the first coincident path fork node positions means that these nodes are the key positions where the dynamic obstacle may affect the robot's path selection, providing clear target nodes for subsequent operations such as calculating the delay time.

[0093] In one embodiment, step S5 of obtaining corresponding multiple first travel delay times according to the multiple first coincident path fork node positions includes:

[0094] S501. Obtain the path capacity according to the first coincident path fork node positions, where the path capacity represents the maximum passing volume;

[0095] S502. Obtain multiple collision volumes of the first coincident path fork node positions based on radar detection;

[0096] S504. Obtain the path flow based on the multiple collision volumes and a preset elapsed time;

[0097] S505. Calculate the path flow density according to the path capacity and the path flow, where the calculation formula is:

[0098] ;

[0099] where Q(M) represents the path flow density, L(j) represents the path flow, and L(l) represents the path capacity;

[0100] S506. Obtain the first travel delay time according to the path flow density, and repeat the steps from obtaining the path capacity based on the position of the first overlapping path fork node to obtaining the first travel delay time according to the path flow density to traverse all the positions of the first overlapping path fork nodes and obtain multiple first travel delay times.

[0101] As described in the above steps S501 - S506, the present invention first obtains the path capacity according to the position of the first coincidence path fork node, where the path capacity represents the maximum passing volume, and the path capacity is an index for measuring the ability of the path to accommodate the passage of objects in the physical space. Obtaining the path capacity provides basic data for subsequent analysis of the passage situation of the path under the influence of dynamic obstacles, which helps to determine whether the path will become congested or impassable due to the existence of dynamic obstacles. Then, based on radar detection, multiple collision volumes at the position of the first coincidence path fork node are obtained. Since the collision volumes are obtained through radar detection, they can accurately represent the space size occupied by the dynamic obstacles at the position of the first coincidence path fork node. This helps the robot to more intuitively understand the occupancy of the path space by dynamic obstacles and provides key data for evaluating the degree of obstruction to the path passage. Then, the path flow is obtained according to the multiple collision volumes and the preset passing time. Next, the path flow density is calculated according to the path capacity and the path flow. The path flow density comprehensively calculates the path capacity and the path flow to obtain a standardized index for evaluating the passage efficiency of the path under the influence of dynamic obstacles. It can intuitively reflect the congestion degree of the path. The larger the value, the more congested the path is, and the greater the delay that the robot may encounter when passing through. Finally, the first travel delay time is obtained according to the path flow density, and the steps from obtaining the path capacity according to the position of the first coincidence path fork node to obtaining the first travel delay time according to the path flow density are repeated to traverse all the positions of the first coincidence path fork nodes, and multiple first travel delay times are obtained. The first travel delay time is calculated according to the path flow density, which directly reflects the time delay caused by the dynamic obstacles at the position of the first coincidence path fork node to the robot's travel. By obtaining the travel delay times of multiple first coincidence path fork nodes, the robot can comprehensively understand the delay situations at different nodes that may be affected by dynamic obstacles, providing accurate data support for selecting the optimal path. At the same time, repeating the calculation steps for multiple positions of the first coincidence path fork nodes can cover the influence situations of dynamic obstacles that the robot may encounter under different path branch selections. This enables the robot to comprehensively consider various possible scenarios during path planning and select the path with the minimum overall delay, improving the comprehensiveness and adaptability of path planning.

[0102] In one embodiment, step S8 of obtaining the preferred path according to the second travel delay time and the third travel delay time includes:

[0103] S801. Obtain the corresponding first preferred fork path according to the second travel delay time;

[0104] S802. Obtain the corresponding second preferred fork path according to the third travel delay time;

[0105] S803. Obtain the preset inspection path of the intelligent inspection bionic robot;

[0106] S804. Map the first preferred fork path, the second preferred fork path, and the preset inspection path to a two-dimensional coordinate system for splicing to obtain a spliced path;

[0107] Use the spliced path as the preferred path.

[0108] As described in the above steps S801 - S804, due to the uncertainty of the movement of dynamic obstacles, it has a great impact on the path planning of the robot. Determining the preferred fork path solely based on the delay time caused by dynamic obstacles can more specifically handle the challenges brought by dynamic obstacles, providing an optimized path basis considering dynamic situations for subsequent comprehensive consideration of the influence of static obstacles, making the path planning more hierarchical and targeted. On this basis, the present invention first obtains the corresponding first preferred fork path according to the second travel delay time, and the second travel delay time reflects the influence degree of dynamic obstacles on each path fork node. By selecting the first preferred fork path based on this time, the robot can preferentially select the path branch with the minimum delay under the influence of dynamic obstacles, thereby avoiding to a certain extent the greater impact of dynamic obstacles on the inspection efficiency and ensuring that it can travel to the destination more quickly in a dynamic environment. Then, obtain the corresponding second preferred fork path according to the third travel delay time. Different from the first preferred fork path based on dynamic obstacles, the second preferred fork path focuses on static obstacle factors. The combination of the two can provide a more comprehensive path selection basis for the robot, enabling the robot to handle dynamic changes and fully consider static environmental factors during path planning, thus formulating a more reasonable and efficient inspection path. Then, obtain the preset inspection path of the intelligent inspection bionic robot. The preset inspection path is a basic inspection route framework set based on prior knowledge or conventional task requirements. It provides an initial reference direction for the path planning of the robot, enabling the robot to make comprehensive decisions in combination with the preset path while considering the influence of dynamic and static obstacles, avoiding the path planning completely deviating from the expected goal, and ensuring that the inspection task can cover the necessary areas. Then, map the first preferred fork path, the second preferred fork path, and the preset inspection path to a two-dimensional coordinate system for splicing to obtain a spliced path. By splicing the first preferred fork path based on dynamic obstacles, the second preferred fork path based on static obstacles, and the preset inspection path in a two-dimensional coordinate system, comprehensive consideration of the influence of dynamic and static obstacles and preset task requirements is realized. The obtained spliced path can avoid obstacles and reduce the delay time to the greatest extent while following the preset inspection logic, thereby determining an overall optimal inspection path and improving the inspection efficiency and task completion quality of the robot.

[0109] In one embodiment, step S8 of performing path planning on the intelligent inspection bionic robot according to the preferred path includes:

[0110] S805. Obtain the driving trajectory of the preferred path according to the preferred path;

[0111] S806. Obtain multiple turning angles according to the driving trajectory of the path;

[0112] S807. Determine whether multiple turning angles are less than a preset angle;

[0113] If the turning angle is less than the preset angle, smooth-fit the turning angles less than the preset angle to obtain a first fitting point and a second fitting point;

[0114] S808. Insert smooth nodes between the first fitting point and the second fitting point to generate a smooth turning angle, and optimize the turning angle of the selected path driving trajectory according to the smooth turning angle to obtain a smooth preferred path;

[0115] S809. Perform path planning on the intelligent inspection bionic robot according to the smooth preferred path.

[0116] As described in the above steps S805 - S809, since the ultimate goal of path planning is to enable the robot to actually move along the planned path. Obtaining the driving trajectory of the optimal path establishes a connection between path planning and robot motion execution, converting the abstract path selection into a specific sequence of motion instructions that the robot can understand and execute. It is a crucial transitional link from planning to execution in the entire inspection process. Therefore, the present invention first obtains the driving trajectory of the optimal path according to the optimal path, and obtaining the driving trajectory of the optimal path can represent the previously determined optimal path in the form of specific geometric lines, including the starting point, ending point, turning points of the path, and the connection methods of each line segment, etc. This enables the robot to have a more intuitive and accurate understanding of the actual route to be traveled, providing basic data for subsequent analysis of the geometric features of the path (such as turning angles). Then, multiple turning angles are obtained according to the path driving trajectory, and the turning angle is an important indicator for measuring the complexity of the path. By obtaining multiple turning angles, the robot can understand how many turning points exist in the optimal path and the degree of curvature of each turning point. A larger turning angle or more turning points may mean that the path is more complex, and the robot requires more adjustments and controls during driving, which poses higher requirements for the mobility and stability of the robot. At the same time, it reduces the energy consumption, time delay, or even collision risk that may be caused by sharp turns, improving the inspection efficiency and safety. It is judged whether multiple said turning angles are less than a preset angle. If the turning angle is less than the preset angle, the turning angles less than the preset angle are smoothed and fitted to obtain a first fitting point and a second fitting point. Among them, smooth fitting is a mathematical method used to find a smooth curve or function to approximate a given set of data points (in the present invention, the points on the path) so that the curve transitions naturally between the data points without sharp changes or mutations, thereby reducing the tortuosity of the path, enabling the robot to turn more smoothly during driving, reducing energy consumption and steering difficulty, and improving the driving speed and stability. During the smooth fitting process, the fitting point refers to the data point used to construct the smooth curve or function. In the present invention, when it is judged that the turning angle is less than the preset angle, the path points near these turning points are processed, and the points used to determine the shape of the smooth curve are the fitting points. The first fitting point and the second fitting point are the points on both sides of the turning angle. They are the key positions passed by the smooth curve and are used to determine how to insert smooth nodes at the turning points of the original path to generate a smooth turning angle, thereby optimizing the geometric shape of the entire path. The determination and processing of the fitting points are important steps to achieve path smooth optimization, directly affecting the quality of the final smooth optimal path and the driving performance of the robot. The setting of the preset angle provides a standard for judging whether the turning angle needs to be optimized.By comparing with the preset angle, the robot can quickly identify which turning angles are large and may require optimization, and which turning angles are within the acceptable range and do not require additional adjustment. This helps to focus on optimizing the key turning points, improve the efficiency of path optimization. At the same time, by judging whether the turning angle is less than the preset angle, the robot can avoid encountering overly sharp or difficult-to-handle turns during driving, reduce risks such as collisions and instability caused by improper turning, ensure the smooth progress of the inspection task. And by performing smooth fitting and inserting smooth nodes for the turning angles less than the preset angle, the path at the turning points can be made smoother, reducing the tortuosity of the path. This can reduce the steering difficulty and energy consumption of the robot when turning, improve the speed and stability of the robot passing through the turning points, thereby optimizing the geometric shape of the entire path, making the robot's driving smoother. Then, smooth nodes are inserted between the first fitting point and the second fitting point to generate a smooth turning angle, and the turning angle of the selected path driving trajectory is optimized according to the smooth turning angle to obtain a smooth optimal path. The smooth optimal path can make the path at the turning points smoother and reduce the tortuosity of the path. This can reduce the steering difficulty and energy consumption of the robot when turning, improve the speed and stability of the robot passing through the turning points, thereby optimizing the geometric shape of the entire path, making the robot's driving smoother. Finally, according to the smooth optimal path, path planning is performed for the intelligent inspection bionic robot. After a series of calculations and optimizations, the smooth optimal path becomes the final path for the robot to actually perform the inspection task. It comprehensively considers various factors such as obstacle influence and path geometric shape optimization, provides the robot with a driving route most suitable for the current environment and task requirements, and ensures that the robot can efficiently, safely, and accurately complete the inspection task.

[0117] This application also provides a path planning system for an intelligent inspection bionic robot, including:

[0118] A first acquisition module 1, configured to acquire the inspection path information of the intelligent inspection bionic robot, where the inspection path information includes inspection path fork information and destination location;

[0119] A second acquisition module 2, configured to acquire the positions of multiple path fork nodes according to the inspection path fork information, and acquire multiple fork trajectory information from the positions of the multiple path fork nodes to the destination location;

[0120] A third acquisition module 3, configured to acquire the obstacle feature information encountered by the intelligent inspection bionic robot, where the obstacle feature information includes dynamic obstacle information and static obstacle information;

[0121] The fourth acquisition module 4 is configured to obtain the movement trajectory information of the dynamic obstacle according to the dynamic obstacle information, and perform coincidence point traversal on the movement trajectory information and the positions of multiple path fork nodes to obtain multiple first coincidence path fork node positions;

[0122] The fifth acquisition module 5 is configured to obtain corresponding multiple first driving delay times according to the multiple first coincidence path fork node positions, and screen the multiple first driving delay times according to the minimum time to obtain the second driving delay time;

[0123] The sixth acquisition module 6 is configured to obtain the fixed position of the static obstacle according to the static obstacle information, and perform coincidence point traversal on the fixed position and the positions of multiple path fork nodes to obtain the second coincidence path fork node positions;

[0124] The seventh acquisition module 7 is configured to obtain the corresponding third driving delay time according to the second coincidence path fork node positions;

[0125] The eighth acquisition module 8 is configured to obtain an optimal path according to the second driving delay time and the third driving delay time, and perform path planning on the intelligent inspection bionic robot according to the optimal path.

[0126] In one embodiment, the second acquisition module 2 includes:

[0127] The first replacement unit is configured to obtain the main inspection path information of the intelligent inspection bionic robot according to the inspection path information, and obtain the corresponding path continuous point positions according to the main inspection path information;

[0128] The second replacement unit is configured to obtain multiple branch path node information according to the inspection path fork information, and obtain the corresponding multiple branch path end positions according to the multiple branch path node information;

[0129] The third replacement unit is configured to obtain the multiple branch path end positions and incorporate them into the multiple merging point positions of the path continuous point positions;

[0130] The fourth replacement unit is configured to obtain the corresponding path fork start points according to the multiple merging point positions, and use the multiple path fork start points as the multiple path fork node positions.

[0131] The present invention also provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the above PCB component detection method based on deep learning are implemented.

[0132] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned PCB component detection method based on deep learning are implemented.

[0133] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned method embodiments. Among them, any reference to a memory, storage, database, or other medium provided in this application and used in the embodiments can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0134] It should be noted that in this article, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, device, article or method. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, device, article or method including that element.

[0135] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A method for intelligent inspection bionic robot path planning, characterized in that: include: Obtaining inspection path information of the intelligent inspection bionic robot, wherein the inspection path information includes inspection path fork information and destination location; Acquire multiple path fork node positions according to the inspection path fork information, and acquire multiple path fork trajectory information from the multiple path fork node positions to the destination position; Obtaining obstacle feature information of obstacles encountered by the intelligent inspection bionic robot, wherein the obstacle feature information includes dynamic obstacle information and static obstacle information; Acquire the moving trajectory information of the dynamic obstacle according to the dynamic obstacle information, and traverse the coincidence points of the moving trajectory information and multiple path fork node positions to obtain multiple first coincidence path fork node positions; Acquire a plurality of corresponding first travel delay times according to the plurality of first coincident path fork node positions, and filter the plurality of first travel delay times according to the minimum time to obtain a second travel delay time; Acquire a fixed position of a static obstacle according to the static obstacle information, and traverse the coincidence points of the fixed position and a plurality of path fork node positions to obtain a second coincidence path fork node position; Acquire a corresponding third travel delay time according to the fork node position of the second coincident path; A preferred path is acquired according to the second travel delay time and the third travel delay time, and path planning is performed for the intelligent inspection bionic robot according to the preferred path.

2. The intelligent inspection bionic robot path planning method according to claim 1 is characterized in that: The step of acquiring the positions of multiple path fork nodes according to the inspection path fork information comprises: Obtaining the main inspection path information of the intelligent inspection bionic robot according to the inspection path information, and obtaining corresponding path continuous points according to the inspection main path information; Acquire multiple branch path node information according to the inspection path fork information, and acquire corresponding multiple branch path terminal positions according to the multiple branch path node information; Acquire multiple branch path end point positions merged into multiple path continuous point positions; The corresponding path fork starting points are obtained according to the multiple merging point positions, and the multiple path fork starting points are used as multiple path fork node positions.

3. The intelligent inspection bionic robot path planning method according to claim 1 is characterized in that: The step of traversing the coincidence points of the movement trajectory information and the positions of multiple path fork nodes to obtain multiple first coincidence path fork node positions includes: Acquire the moving speed and moving direction of the dynamic obstacle according to the moving trajectory information, and acquire the moving trajectory curve of the dynamic obstacle within a preset time according to the moving speed and moving direction; Mapping the movement trajectory curve and the positions of multiple path fork nodes into a preset grid map to obtain multiple movement trajectory coordinate points and multiple path fork node coordinate points; Calculate multiple Euclidean distances based on each of the moving trajectory coordinate points and multiple path fork node coordinate points; Determine in sequence whether a plurality of the Euclidean distances are less than a preset threshold; If the Euclidean distance is less than a preset threshold, the moving trajectory coordinate points corresponding to the Euclidean distance that meets the condition are used as a plurality of first coincident path fork node positions that coincide with the path fork node coordinate points.

4. The intelligent inspection bionic robot path planning method according to claim 1, characterized in that: The step of acquiring corresponding multiple first travel delay times according to multiple first coincident path fork node positions comprises: Acquire path capacity according to the position of the fork node of the first coincident path, wherein the path capacity represents the maximum passing volume; Acquire multiple collision volumes at the locations of the fork nodes of the first coincident path based on radar detection; Acquire path flow according to the plurality of collision volumes and preset elapsed time; The path flow density is calculated according to the path capacity and the path flow, wherein the calculation formula is: ； Among them, Q(M) represents the path flow density, L(j) represents the path flow, and L(l) represents the path capacity; Acquire a first travel delay time according to the path flow density; Repeat the steps of obtaining the path capacity according to the first overlapping path fork node position to obtaining the first travel delay time according to the path flow density to traverse all the first overlapping path fork node positions to obtain multiple first travel delay times.

5. The intelligent inspection bionic robot path planning method according to claim 1, characterized in that: The step of acquiring the preferred route according to the second travel delay time and the third travel delay time comprises: Acquire a corresponding first preferred fork path according to the second travel delay time; Acquire a corresponding second preferred fork path according to the third travel delay time; Obtain the preset inspection path of the intelligent inspection bionic machine; Mapping the first preferred fork path, the second preferred fork path and the preset inspection path into a two-dimensional coordinate system for splicing to obtain a spliced ​​path; The splicing path is taken as the preferred path.

6. The intelligent inspection bionic robot path planning method according to claim 1, characterized in that: The step of performing path planning for the intelligent inspection bionic robot according to the preferred path comprises: Acquire a preferred path driving trajectory according to the preferred path; Acquire multiple turning angles according to the path driving trajectory; Determining whether the plurality of turning angles are less than a preset angle; If the turning angle is smaller than the preset angle, the turning angle smaller than the preset angle is smoothly fitted to obtain a first fitting point and a second fitting point; Inserting a smooth node between the first fitting point and the second fitting point to generate a smooth turning angle, and optimizing the turning angle of the selected path driving trajectory according to the smooth turning angle to obtain a smooth preferred path; The path planning of the intelligent inspection bionic robot is carried out according to the smooth optimal path.

7. An intelligent inspection bionic robot path planning system, characterized in that: include: A first acquisition module is used to acquire the inspection path information of the intelligent inspection bionic robot, wherein the inspection path information includes the inspection path fork information and the destination location; A second acquisition module is used to acquire multiple path fork node positions according to the inspection path fork information, and acquire multiple fork trajectory information from the multiple path fork node positions to the destination position; A third acquisition module is used to obtain obstacle feature information of obstacles encountered by the intelligent inspection bionic robot, wherein the obstacle feature information includes dynamic obstacle information and static obstacle information; A fourth acquisition module is used to acquire the moving trajectory information of the dynamic obstacle according to the dynamic obstacle information, and traverse the coincidence points of the moving trajectory information and a plurality of path fork node positions to obtain a plurality of first coincidence path fork node positions; A fifth acquisition module, configured to acquire a plurality of corresponding first travel delay times according to a plurality of first coincident path fork node positions, and filter the plurality of first travel delay times according to a minimum time to obtain a second travel delay time; A sixth acquisition module, configured to acquire a fixed position of a static obstacle according to the static obstacle information, and traverse the coincidence points of the fixed position and a plurality of path fork node positions to obtain a second coincidence path fork node position; A seventh acquisition module, configured to acquire a corresponding third travel delay time according to the fork node position of the second coincident path; An eighth acquisition module is used to acquire a preferred path according to the second driving delay time and the third driving delay time, and to perform path planning for the intelligent inspection bionic robot according to the preferred path.

8. The intelligent inspection bionic robot path planning system according to claim 7, characterized in that: The second acquisition module includes: A first replacement unit is used to obtain the main inspection path information of the intelligent inspection bionic robot according to the inspection path information, and obtain the corresponding path continuous points according to the inspection main path information; A second replacement unit is used to obtain a plurality of branch path node information according to the inspection path fork information, and obtain a plurality of corresponding branch path terminal positions according to the plurality of branch path node information; A third replacement unit is used to obtain a plurality of merging point positions where the end points of the branch paths are merged into the continuous points of the path; The fourth replacement unit is used to obtain corresponding path fork starting points according to the multiple merging point positions, and use the multiple path fork starting points as multiple path fork node positions.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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