13results about How to "Fast planning" patented technology

The invention discloses a wall-climbing robot cylindrical tank space surface welding seam path planning method. The method comprises the steps that 1, a 2D map of a three-dimensional cylindrical tank welding seam is constructed; step 2, constructing an undirected connected graph; 3, judging whether any cross point meets the requirements of an Euler loop or not; 4, constructing a shortest path Euler loop by using a virtual edge adding method; and 5, an improved Fluery algorithm is utilized to plan a path through which the robot can traverse the welding seam at a time. According to the method, the three-dimensional cylindrical tank welding seam is mapped to the 2D plane, the 2D map of the welding seam path is accurately constructed, path planning is optimized through the undirected connected graph and the virtual edge method, and it is ensured that the cylindrical tank wall-climbing robot can traverse all welding seams on the shortest path. According to the method, an improved Fluery algorithm is combined, shortest path planning of an Euler loop is achieved, the efficiency and accuracy of robot weld traversal are optimized, an efficient path control scheme is provided for a robot, and the working capacity and the autonomous navigation level of the robot in a complex environment are improved.

The invention discloses a UUV path planning method based on an improved fast search random tree. The method is suitable for the fields of autonomous navigation, task execution, obstacle avoidance, safety guarantee and the like of UUVs. The method comprises the following steps: S1, determining an initial step length; dynamically adjusting the initial step length according to the scale of the task space and the complexity of the environment; and S2, carrying out target bias sampling. In the sampling process, according to a set bias threshold value, an algorithm biases a sampling point to a target area according to a certain probability, so that the target guidance quality of search is enhanced; and S3, removing redundant points. A Dijkstra algorithm is adopted to simplify a path, unnecessary turning points are removed, and more nodes are inserted into the path to improve the intensity of the path, so that the optimization space is increased. Then, redundant point removal is executed again, so that the path is further simplified; and S4, smoothing the path. Through the maximum curvature constraint, it is ensured that the turning angle of the path does not exceed the preset limit, meanwhile, in combination with a polynomial fitting method, abrupt turning in the path is reduced, and the smoothness of the path is improved.

The present invention discloses a method and device for path planning and defect identification in the process of unmanned aerial vehicle inspection, the method comprising: collecting oblique photography image data of the inspection area; generating point cloud data of the inspection area according to the oblique photography image data; reconstructing the point cloud data based on the average curvature smoothing and triangulation gridding method to obtain the real scene reconstruction result of the inspection area; gridding the real scene reconstruction result, dividing the area according to the divided grid, performing path planning based on the area division result, and optimizing the planned path to obtain the optimal inspection path; controlling the unmanned aerial vehicle to inspect according to the optimal inspection path, collecting target image data during the inspection process, and inputting the target image data into a pre-established defect detection model for defect identification. The method can effectively improve the inspection efficiency of unmanned aerial vehicles.

The invention discloses an optimal path planning method of a pulse coupling neural network based on double constraints. The method comprises the following steps: mapping a path planning environment to a DC-PCNN network; a DC-PCNN neural network model is constructed, and all neurons are initialized; activating a target neuron, and recording the current neuron as a father node; calculating an exponential decay function of the torque deviation, and multiplying the calculated value as a penalty factor by an update item of the internal activity item; calculating a gravitational function value of the flow field constraint, and using the calculated value to update a current neuron dynamic threshold value; comparing the internal activity item with a dynamic threshold; when gt; if yes, activating the neuron, and recording the current neuron as a father node; repeating the steps S4-S6 until the initial neuron is activated; and backtracking all activated nodes, and planning an optimal path. According to the method, the search efficiency is remarkably improved while the path optimality is ensured.

This invention discloses a collaborative operation system and method for a drone and a robotic dog in a mining scenario. The system includes a drone, a robotic dog, and a wireless communication module. The drone and the robotic dog transmit data through the wireless communication module. The drone includes a laser terrain scanner, and the robotic dog is equipped with a high-definition camera and an infrared sensor. The laser terrain scanner is used to perform a global terrain scan of the mining blasting area to obtain laser point cloud data. The high-definition camera is used to identify obstacle elevation values, and the external sensors are used to collect road slope data. This invention utilizes a bidirectional BFS search algorithm combined with adjacent grid selection weights and priorities to generate multiple planned paths. The drone sends the planned paths to the robotic dog in advance, and the robotic dog only needs to select the optimal path based on the actual road conditions. The lightweight MobileNet model improves the robotic dog's computation speed and time, and the selection of the optimal path based on actual road conditions requires low hardware computing power.

The present invention belongs to the field of collaborative path planning, and provides a method and system for collaborative path planning of a drone-unmanned vehicle merging. The present invention is based on a bidirectional A* algorithm, and regards the forward search of the bidirectional A* algorithm starting from the starting point as the path search process of the drone, and the reverse search process starting from the end point as the path search process of the unmanned vehicle. The meeting position of the drone and the unmanned vehicle is determined by screening suitable meeting points, thereby realizing collaborative path planning of the drone and the unmanned vehicle. The present invention realizes cross-dimensional fusion of path search by placing the drone and the unmanned vehicle in a three-dimensional space and limiting the node expansion of the unmanned vehicle to a two-dimensional expansion. While ensuring the feasibility of the unmanned vehicle path, the optimal meeting point is selected based on the nodes obtained by the two-dimensional expansion, thereby reducing the number of candidate nodes and speeding up the path planning speed.

The application provides a kind of mechanical arm motion planning method and system based on deep neural network.The method includes processing obstacle space point cloud data collected by depth camera to obtain processed point cloud data;Convert the processed point cloud data into octomap chart and import into moveit mechanical arm workspace;In moveit mechanical arm workspace, the complete collision-free path from the initial configuration state to the target configuration state is output by the mechanical arm motion planning algorithm based on deep neural network;Optimize the complete collision-free path to generate the optimal complete collision-free path;Control the mechanical arm to move according to the generated optimal complete collision-free path.The application proposes that the deep neural network predicts the configuration state of the mechanical arm at a certain time, reduces the calculation amount in the process of mechanical arm obstacle avoidance planning through collision detection and bidirectional iterative exploration, greatly speeds up the path planning speed, shortens the path planning time, and at the same time ensures that the planned path is close to the optimal path.

The present invention relates to an image recognition method based on deep learning, comprising: S1, obtaining corner point information and character information of a floor plan to be recognized, and inputting the corner point information into a quadratic programming model to obtain connection information of wall lines and doors and windows; the quadratic programming model pre-establishes a model including a plurality of corresponding corner point connection rules and constraint conditions of the positional relationship between wall lines and doors and windows; S2, inputting the connection information of wall lines and doors and windows into a graph theory optimization model to obtain regional composition information of each room area in the floor plan; the graph theory optimization model is a pre-established Graham algorithm model improved based on graph theory knowledge; S3, matching the character information with the regional composition information to obtain a recognition result. The method solves the problems in the prior art that the floor plan recognition result is not accurate, the recognition process is complex in calculation and has poor scalability.

The invention discloses a path planning method based on Informed-RRT *, and relates to the technical field of path planning. The invention aims to solve the problem that an existing path planning method cannot efficiently acquire a global path when passing through a narrow-distance area. The method comprises the following steps: identifying all narrow-distance areas in a research area by using a research area cost map, and generating offset points; establishing a forward and reverse bidirectional tree by using the coordinates of the starting node, the starting auxiliary node, the target node and the target auxiliary node, generating a new node to update the forward and reverse bidirectional tree, judging whether the updated forward and reverse bidirectional tree has a connection path, and obtaining a planned path of the mobile robot if the connection path exists; if not, judging whether the number of sampling points is greater than the total number of iterations; if the number of the sampling points is greater than the total number of iterations, determining that the output path planning fails; if the number of the sampling points is smaller than or equal to the total number of iterations, the bias nodes are used for guiding the node growth direction, and then the gravitation and repulsive force of the bias points are eliminated. The method is used for path planning.

The present invention relates to a formation method of a hovering drone formation based on intersection trajectory replanning, comprising: S1, initialization setting; S2, all non-leader drones send their respective current positions to the leader drone; S3, the leader drone determines whether the current positions sent by all non-leader drones are received within a first preset time period; if so, an initial expected point is allocated to each drone, a corresponding initial maneuvering trajectory is generated, and the initial maneuvering trajectory is replanned to obtain a corresponding optimized trajectory; S4, the leader drone sends the optimized trajectory to the corresponding drone; S5, each drone maneuvers according to the optimized trajectory, and informs the leader drone of the completion of the maneuver after reaching the corresponding target expected point; S6, the leader drone determines whether all drones complete the maneuver within a second preset time period. The present invention can ensure that no collision occurs during maneuvering.

The present disclosure provides a path planning method, a path planning model, and a training method for a selection model, and relates to the field of computer technology, and in particular to the fields of artificial intelligence, intelligent transportation, and deep learning technology. Among them, the specific implementation scheme of the path planning method is: divide the first path set corresponding to the first path planning information to obtain multiple second path sets; re-plan the path according to the second path planning information corresponding to the target path set to obtain an optimized third path set; and update the first path set using the third path set. According to the scheme of the embodiment of the present disclosure, a subset with a large room for improvement in path cost is selected from the multiple subsets contained in the first path set, and the corresponding local path planning problem is re-solved, thereby optimizing the current path set, improving the quality of the path set, and increasing the path planning speed.

The invention discloses a water jet cutter path automatic planning method and system suitable for vegetable processing, and belongs to the technical field of food processing. The method comprises the following steps: firstly, carrying out image acquisition and analysis on vegetable slices, and identifying a vegetable slice area and a wood area of each vegetable slice from an image; in each vegetable slice, performing expansion treatment on the vegetable slice area and the wooden area, and subtracting the wooden area from the vegetable slice area to obtain a meat quality area; then discrete nodes are extracted from the contour line of the meat quality area, the number of the discrete nodes is reduced through fitting, and the fitted discrete nodes are recorded in a clockwise or anticlockwise sequence; and finally, generating a water jet cutter path of each vegetable slice according to the record sequence of the discrete nodes. The water jet cutter path automatic planning scheme is based on image recognition, planning time consumption is short, computing resource consumption is extremely low, and the efficient requirement of the vegetable processing industry can be met.

The present invention discloses a multi-route planning method, comprising the following steps: S1, initializing the planning space; S2, constructing a two-dimensional simulated ocean current field model; S3, setting a virtual obstacle wall in the planning space, dividing the planning space into p+q sub-planning spaces; S4, using the level set method to plan the route for the entire planning space to obtain the optimal route; planning the route for all sub-spaces to obtain p+q sub-optimal routes; S5, merging the same routes; S6, sorting the p+q+1 optimized routes obtained by planning from small to large according to the value of the navigation time, and completing the route planning. The present invention realizes the efficient use of ocean currents to plan reliable and reference-worthy optimized routes with large discrimination for commanders to choose from, and solves the problem of no effective alternative routes for commanders to choose in actual underwater route planning. The spatial subdivision level set method based on the commander's intention can well implement the commander's intention.