190 results about "Heuristic function" patented technology

The invention relates to the technical field of path planning, and discloses a robot path planning method, which comprises the following steps that a hybrid cost map is constructed, and the hybrid cost map comprises a static obstacle field, a dynamic obstacle probability prediction field and a terrain energy consumption field; constructing an anisotropic heuristic function, and searching on the mixed cost map to obtain an initial path; parameterizing the initial path into a group of piecewise polynomial curves, constructing a joint optimization target which takes the total curvature, the piecewise polynomial curves and the space-time overlapping integral of a dynamic obstacle probability prediction field, and performing trajectory optimization under the condition of meeting the kinematics constraint of the robot to obtain a smooth space-time trajectory; when the task is executed, the information divergence is continuously calculated, and when the information divergence exceeds a preset threshold value, the current state of the robot serves as a new starting point, and the complete path planning process is executed again. According to the method, the future collision risk is prospectively avoided, the energy consumption of the robot is considered, and the comprehensive quality of the path is improved from the source.

The invention relates to the technical field of educational resource recommendation, and discloses an educational resource intelligent recommendation method and system based on big data driving, and the system comprises a data fusion processing module, a portrait modeling module, a resource feature engine module, an intelligent recommendation core module and a closed-loop feedback module. By fusing knowledge state, cognitive ability and interest preference three-dimensional portraits, capturing user learning ability evolution in real time, dynamically updating knowledge mastery by adopting a knowledge tracking model, and perceiving interest migration in combination with an attention mechanism, the problem of learning cold start in a new field is solved, recommendation coverage range and accuracy are improved, and user experience is improved. The cognitive load sensitive ant colony optimization algorithm is designed, the learning path continuity is guaranteed through a heuristic function, the learning path structure reasonability is optimized, the cognitive burden of a user is reduced, a personalized knowledge attenuation model is constructed by fusing an Ebbinghaus forgetting curve, the knowledge long-term retention rate is increased, and the review resource release accuracy is enhanced.

The invention discloses an outdoor robot path planning method based on improved bidirectional A * and DWA algorithms. The method comprises the following steps: step 1, A * algorithm improvement; step 2, DWA algorithm improvement; step 3, algorithm fusion; step 4, path planning; the dynamic weight DOMW strategy is introduced into the A * algorithm to optimize a heuristic function, multiple factors are comprehensively considered, path planning is more reasonable, the algorithm calculation amount and search time are reduced by removing redundant nodes, the algorithm efficiency is improved, a bidirectional search mechanism is adopted, and the algorithm efficiency is improved. The search efficiency is improved, the search space is reduced, a local optimal solution is avoided, the robustness of the algorithm is enhanced, the path is smoothed by adopting a Bezier curve optimization strategy, so that the path meets the actual motion requirement of the robot, the damage to hardware is reduced, and the attractiveness and readability of the path are improved; by optimizing a DWA algorithm trajectory evaluation function and speed space constraints, the collision risk is reduced, and local obstacle avoidance and operation stability are improved.

The invention discloses a path planning method and system based on an improved A * algorithm and an improved DWA algorithm, and relates to the technical field of path planning, and the method comprises the steps: outputting a dynamically optimized heuristic function weight; generating a complete path of the current navigation task in combination with the heuristic function weight; executing self-adaptive smoothing processing, and outputting a smoothed global path; performing dynamic trajectory prediction according to the dynamic window parameters to generate a plurality of motion trajectories; performing comprehensive evaluation on each motion track, generating an evaluation value of each motion track, and selecting the motion track with the optimal evaluation value as a final track; and inputting the path coefficient recorded by the navigation task to the path coefficient optimization network based on the hierarchical attention mechanism. According to the method, the planning time of the A * algorithm in global path planning is shortened, the search efficiency of the A * algorithm in a large-scale scene is improved by optimizing the algorithm structure, and the problem that the DWA algorithm is prone to falling into local optimum is greatly solved.

The invention provides a comprehensive scene map path planning method based on an improved A * algorithm, and the method comprises the steps: 1, carrying out environment modeling for a comprehensive scene map: segmenting a two-dimensional plane scene into more than two square grids, and carrying out the assignment of a single grid in a binarization manner to represent the occupation condition of the grids; constructing a protection area formed by a single-layer grid around the obstacle, and filling the protection area into gray; step 2, planning a path by adopting an improved A * algorithm: step 2-1, establishing an improved bidirectional search strategy; step 2-2, establishing a heuristic function with a barrier linear density weight; step 2-3, adopting a variable neighborhood node expansion mode to complete node expansion; and step 2-4, carrying out path secondary optimization. According to the method, the planning efficiency and the path performability in a complex environment are remarkably improved, and an effective solution is provided for navigation systems in the fields of service robots, intelligent warehousing and the like.

The invention discloses a complex terrain-oriented tracked vehicle energy efficiency optimization path planning method and system, and the method comprises the steps: firstly constructing a joint energy consumption model on a rasterized three-dimensional terrain, and superposing the straight rolling resistance, gradient and steering equivalent resistance moment energy consumption to obtain node cost; then, under the framework of a weighted A * function, the product of Euclidean distance and energy consumption rate is used as a heuristic function, and rapid rough search is achieved; halton sampling is carried out on the coarse path nodes, an environment vector is calculated based on the distance between a sample and the center and the gradient difference, offsets are carried out, impassable samples are removed, secondary search is carried out after a probability road network is reconstructed, and the local path quality is improved; and finally, smoothing the broken line by using a three-dimensional fourth-order B spline, eliminating the conflict with the impassable area by densifying nodes, and outputting an executable track with continuous curvature. Simulation shows that compared with A *, Dijkstra and PRM algorithms in complex terrains, the method saves energy by 5.2%-12% on average, and the energy saving performance of the tracked vehicle is achieved.

The invention belongs to the technical field of robot path planning, and provides a path planning method for an electric power inspection mobile robot, which comprises the following steps of: firstly, constructing an environment grid map, establishing a corner directional expansion safety boundary, and performing safety expansion on an outer right angle of an obstacle; secondly, establishing an intelligent neighborhood selection mechanism based on fuzzy control, and performing adaptive adjustment neighborhood search; then, a multi-target heuristic function fusing the local obstacle density, the safety distance and the traffic difficulty is established, path search is carried out, and an initial path is obtained; then, constructing a path optimization strategy, and carrying out redundant point deletion and B spline curve smoothing processing on the initial path until a target executable path is obtained; and finally, optimizing an executable track in the target executable path in a time-space domain through an improved time-domain elastic band algorithm to obtain an optimal path. According to the invention, accurate obstacle avoidance of complex geometric obstacles can be realized.

The invention discloses a mobile robot path planning method based on an improved A star algorithm, which comprises the following steps of: firstly, acquiring environment information of a current map through a sensor, establishing a grid map, and simultaneously determining a starting point coordinate, an end point coordinate and an environment obstacle coordinate of a robot; an OPENLIST list and a CLOSEDLIST list are created, and a starting point is put into the OPENLIST list; selecting a new distance calculation function, and setting the Euclidean distance as a median value of the Euclidean distance and the Manhattan distance; improving a heuristic function, and introducing an adaptive estimation function; the total distance cost of the nodes is calculated from the starting point according to the node expansion mode, the optimal path nodes are placed in the CLOSEDLIST till the end point is traversed, and the optimal path is generated; and deleting redundant nodes in the path based on a collinear principle to obtain a final optimized path. The search time of the algorithm is shortened, the algorithm can better adapt to different environments and scenes, the applicability of the algorithm is enhanced, and the planning efficiency of the algorithm is effectively improved.

The invention provides a multi-document key phrase extraction method based on graph structure node influence, and relates to the technical field of natural language processing and text mining. Firstly, a candidate phrase set is generated through noun phrase extraction and standardization; secondly, a semantic relation between phrases is captured through local subgraph construction and a sliding window mechanism, and the semantic relation is integrated into a global phrase co-occurrence graph; thirdly, dynamically dividing theme communities based on two-dimensional structure entropy minimization and a potential game model, and identifying phrase groups with high semantic aggregation; then, cross-topic nodes are processed through a structure entropy heuristic function, and flexibility of topic division is enhanced; and finally, in combination with node influence sorting, extracting key phrases with theme representativeness and propagation capability. The method does not need to label data, is suitable for multiple fields of academic literatures, news texts and the like, has high efficiency, accuracy and universality, and provides an innovative solution for multi-document key phrase extraction.

The invention relates to the technical field of machine path planning, and discloses an island ant colony path planning method for an unmanned trolley, which comprises the following steps: after generating a grid map, decomposing global path search into path exploration between adjacent islands; non-uniform pheromone initialization is adopted between adjacent islands, local paths between the adjacent islands are searched respectively, and the position of a next grid is selected in the searching process by combining an improved node transition probability with a heuristic function and pheromone concentration; and when global pheromone distribution is updated, an optimal path is rewarded, and a worst path is punished. And combining the local paths of the island pairs to obtain global paths, and selecting the global path with the shortest length as the optimal driving path. According to the method, a segmented island strategy is adopted, the complexity of single search is reduced, the early random probing process is shortened through non-uniform initialization pheromones, the convergence speed is increased, the number of deadlock ants is effectively reduced, and the precision, convergence and stability of path planning are improved.

The invention relates to the technical field of automatic driving, and discloses a tramcar path planning method, device and equipment based on a reference line and a storage medium. According to the method, an improved A * search algorithm is combined with reference line discretization and smoothing processing, the node expansion number is reduced, the path smoothness is optimized, and the tramcar path planning efficiency is improved. Meanwhile, a double-factor heuristic function and dynamic constraint verification are utilized to ensure that the path is safe and feasible, and the method has the advantages of improving path planning efficiency, reducing path fluctuation, meeting special terrain and dynamic constraint requirements of a mining area and ensuring that the mine car stably travels along a predetermined reference line.

The invention relates to the field of positioning, in particular to a laser radar wheeled robot positioning mapping method and system based on ROS. The method comprises the following steps: generating Gaussian proposal distribution by fusing laser radar and odometer data through an improved RBPF Gmapping algorithm, dynamically balancing particle degradation and computing resources in combination with an adaptive resampling threshold Neff, and reducing mapping errors and memory occupation; the optimization A * algorithm adopts heuristic function weight segmentation adjustment, redundant search directions are eliminated, and path nodes are smoothed in combination with a Bezier curve; an improved DWA algorithm introduces an obstacle trajectory prediction factor, and a speed window is dynamically adjusted; iMU and laser radar weights are dynamically distributed based on EKF multi-sensor fusion positioning, pose errors are corrected and fed back to the mapping and obstacle avoidance module, and closed-loop control is formed. According to the method, the mapping precision is improved, the path planning efficiency is improved, the positioning error is reduced, and the problems of mapping accumulative error, path redundancy and insufficient dynamic obstacle avoidance capability in a traditional scheme are effectively solved.

The invention discloses a mobile robot path planning method combining kinematics constraint and variable density sampling, which comprises the following steps: setting a starting point and a target point, adding the starting point into a vertex set, adding the target point into a sampling set, initializing a node priority queue and an edge priority queue, and establishing kinematics constraint and environment parameters at the same time; searching a main circulation path; if the vertex cost is better, performing vertex expansion and generating candidate edges, and if the edge cost is better, performing feasibility verification and accessing the path tree; when the search is stagnated or trapped in local optimum, an escape mechanism is triggered, and the exploration capability is enhanced through radius expansion and supplementary sampling; an improved heuristic function and a local cost function are established, search convergence is guided, and track smoothness and performability are improved; and when a termination condition is reached, outputting a path planning result. According to the method, the sampling efficiency and the searching speed are improved while the path feasibility is ensured, so that the navigation requirement of the mobile robot in a complex dynamic environment is met.

The invention relates to a path planning method and device based on hierarchical reinforcement learning, equipment and a medium, and the method comprises the steps: converting any planar map into rasterization, taking the shortest path between a starting point position and an end point position as an optimization target under the constraint of avoiding the collision of an obstacle, and starting from the starting point position, selecting a plurality of sub-target positions, the method comprises the following steps of: establishing a path planning problem between a starting point position and an end point position until the end point position is reached, converting the path planning problem between the starting point position and the end point position into a plurality of local sub-path planning problems, constructing a learning heuristic network by adopting an artificial neural network as a heuristic function in an A * algorithm, and training the learning heuristic network by utilizing reinforcement learning to obtain a learning heuristic network; and solving each local sub-path planning problem by using the trained learning heuristic network to obtain an optimal solution of each local sub-path, and then obtaining a bounded suboptimal path planning result. By adopting the method, path planning can be efficiently carried out on any plane map.

The invention discloses a low-altitude path planning method and system based on a dynamic adaptive graph neural network, and belongs to the technical field of artificial intelligence and robot autonomous navigation. Aiming at the problems of large calculation amount of a fixed grid map, slow dynamic response of a static octree, failure of a traditional heuristic function on a non-uniform graph and the like, the invention provides the following scheme: constructing a resolution self-adaptive non-uniform graph by using the octree; a Morton code is adopted to realize rapid positioning of a dynamic obstacle and millisecond-level updating of a map; an attention mechanism is introduced into a graph attention network (GAT), weights are adaptively allocated to different neighbor nodes, features are aggregated, and a high-precision heuristic cost estimation value is output; and finally, integrating the estimated value into an algorithm to perform efficient path search. According to the method, the memory occupation and the calculation complexity are remarkably reduced, and the planning efficiency and the path safety in a dynamic environment are improved.

The invention discloses a multi-target path planning method and device, equipment and a storage medium, and relates to the technical field of traffic path planning, and the method comprises the steps: obtaining solar radiation data and original map data, carrying out the multi-source data fusion based on the solar radiation data and the original map data, and obtaining the radiation road network data; based on the radiation road network data, a total cost function of the path length is constructed through an accumulated cost function of the path length and a heuristic function of the estimated cost, and candidate paths are generated according to the total cost function; on the basis of the radiation road network data, calculating the path average radiation intensity of the candidate path, and further obtaining the energy recovery amount of the candidate path; constructing a dual-target cost function based on the total cost function and the energy recovery amount, adjusting node priorities and screening and updating a non-dominated solution set according to the dual-target cost function, and generating a target candidate path set according to the non-dominated solution set; and determining an optimal balance path in the target candidate path set through multi-dimensional index quantitative evaluation and balance coefficient sorting.

The invention belongs to the related technical field of underwater gravity matching navigation, and discloses a submersible vehicle route planning method, application and equipment based on an improved ant colony algorithm, and the method comprises the steps: (1) constructing an adaptability index matrix based on gravity adaptability characteristics, and carrying out the normalization and information entropy weighting to obtain comprehensive adaptability characteristics; (2) constructing an environment grid model according to the comprehensive adaptability characteristics; (3) taking a free space as a search area, carrying out path search based on an environment grating model and an improved ant colony algorithm, and further obtaining a track node sequence meeting the adaptability constraint, the path continuity and the shortest path target to complete track planning; wherein the improved ant colony algorithm is obtained by introducing an elite ant strategy to enhance the globally optimal path pheromone of the ant colony algorithm and adaptively adjusting a pheromone concentration factor, a heuristic function factor and a pheromone volatilization coefficient in the ant colony algorithm by using a particle swarm optimization algorithm. The search efficiency and the automation degree are improved.

The invention belongs to the technical field of unmanned aerial vehicle path planning. Aiming at the problems of two-dimensional limitation, low search efficiency, high probability of local optimum and the like of a traditional ant colony algorithm during unmanned aerial vehicle path planning, Q-Learning is integrated into the ant colony algorithm based on DEM modeling, a heuristic function and a pheromone strategy are optimized, and the unmanned aerial vehicle path planning method and system suitable for the three-dimensional terrain are provided. And the optimal path is fused and smoothed, so that the efficiency and safety of path planning under the complex terrain are improved, and the method is suitable for mountainous area inspection, agricultural plant protection and other scenes.

The invention discloses an unmanned aerial vehicle path planning method based on an intelligent optimization algorithm. The method aims to overcome the defects that a traditional ant colony algorithm is low in convergence speed, prone to falling into local optimum and prone to falling into blind search in the initial stage of the algorithm. The unmanned aerial vehicle path planning method suitable for the complex environment is provided from the characteristics that a fitness function and a heuristic function of an ant colony algorithm can be customized to meet actual requirements, a search strategy can be customized to adapt to a specific environment, and the ant colony algorithm is easily combined with other intelligent optimization algorithms. Based on a search mode of an unmanned aerial vehicle, three-dimensional grid topographic data and a basic unmanned aerial vehicle kinematics constraint and path evaluation principle, after environment modeling and topographic data preparation are carried out on a three-dimensional mountainous area scene based on a grid method, a classical ant colony algorithm is improved; comprising the steps of introducing an improved heuristic function, a self-adaptive step length search strategy based on a visual area and a pheromone updating strategy comprehensively considering path smoothness.

The invention discloses a method for planning a road between an upper reservoir and a lower reservoir of a pumped storage power station based on an improved A * algorithm, and the method comprises the steps: S1, carrying out the rasterization of three-dimensional terrain point cloud data of a target region of the pumped storage power station, and generating a digital elevation model (DEM); s2, identifying a natural obstacle area in the target area, and generating an impassable area matrix map; s3, constructing a heuristic function comprehensively considering distance and gradient, an enhanced cost function and a multilayer neighborhood search strategy, performing path search based on an improved A * algorithm, and constructing an initial path; s4, performing optimization processing on the initial path to obtain a final path; and S5, superposing the optimized final path to an original terrain grid map for visualization, calculating and displaying key design indexes of the path, and assisting a designer to perform scheme comparison and selection and engineering decision. The method is high in intelligent degree, high in engineering adaptability and suitable for rapid line selection and optimization design of the upper and lower reservoir roads of the pumped storage power station in the complex mountainous area.

The invention discloses a load power balance regulation and control method and device based on knowledge graph and random sequence optimization, a medium and a product, and relates to the field of power system dispatching, and the method comprises the steps: constructing a power grid topology knowledge graph according to power grid physical equipment and corresponding topology connection and operation constraints; performing probability distribution fitting on the power output of the renewable energy according to the output data to obtain the output of the renewable energy; obtaining a plurality of typical output scenes; taking the state of the power grid physical equipment after fault isolation as an initial state, and constructing a random Markov decision process; searching in a state space by using an improved heuristic search algorithm by taking an initial state as a starting point, minimizing an accumulated cost function as a target, taking a hard constraint as a constraint condition and taking a topological distance as a heuristic function to obtain an optimal operation sequence; verifying the optimal operation sequence to obtain a final operation sequence; the power of the power grid is regulated and controlled; according to the invention, the fault response speed can be improved, and rapid adaptive decision in a complex scene can be realized.

The invention discloses a global-local fusion path planning method for unmanned equipment in a strip mine, and relates to the technical field of self-service navigation of unmanned equipment. The method comprises the steps of constructing a grid map with a protection area based on a laser radar, planning a global path by adopting an improved A algorithm, fusing an FMM arrival time graph and multi-factor environmental cost to design a heuristic function, and replacing traditional g-score with accumulated movement cost to generate a smooth optimization path. Key nodes of U-shaped curves, narrow road sections, obstacle avoidance and the like are extracted, and local navigation targets are dynamically switched. And in combination with a self-adaptive hybrid MPPI algorithm, a local control sequence meeting vehicle dynamics constraints is generated in real time, and safe obstacle avoidance and accurate tracking are realized. According to the method, the safety and smoothness of the path and the adaptive capacity of the path in a complex strip mine environment are remarkably improved.

The invention discloses a cluster robot collaborative operation method and system based on hierarchical path planning, and the method comprises the steps: carrying out the space partitioning of an environment map of cluster robot operation, and obtaining a plurality of barrier-free cells; an improved RRT * algorithm is adopted to carry out path preliminary planning on the cluster robot, and a heuristic function is combined to guide a tree structure to expand to a target cell boundary; coordinating path conflicts among the plurality of measurement robots by using a CBS algorithm to form a collision-free path set; a CBS algorithm is adopted to uniformly plan paths for one or more tracking robots; path smoothing processing is carried out on the paths of all the measuring robots and the paths of all the tracking robots, and finally planned collision-free paths are obtained. According to the method, bounded unit division is carried out on the working environment, and sampling is carried out on the cell boundary in combination with the improved RRT * algorithm, so that the path planning efficiency and the target guidance quality can be remarkably improved.

The invention discloses an intersection dynamic path planning method and system based on vehicle-road cooperation, and the method comprises the steps: obtaining vehicle trajectory data, carrying out the preprocessing of the vehicle trajectory data, carrying out the trajectory prediction based on the preprocessed trajectory data, carrying out the global path planning based on the predicted trajectory, and carrying out the conflict analysis and S-T diagram construction based on the determined global path planning. And then carrying out local trajectory planning, trajectory evaluation and optimal trajectory selection. According to the scheme, trajectory smoothing and lane matching are integrated to improve the accuracy of trajectory prediction, trajectory level analysis is carried out on potential conflicts between vehicles to enhance the refinement and pertinence of conflict detection, and in the global path planning stage, the path planning efficiency is improved. By improving a heuristic function of a Hybrid A * algorithm, a generated path is smoother and has fewer nodes while meeting vehicle kinematics constraints; in the local trajectory planning stage, a Lattice trajectory sampling method is fused, it is ensured that a fine-grained feasible trajectory can still be generated in a complex dynamic environment, and high calculation efficiency is maintained.

The invention discloses a robot path planning method and device, equipment, a medium and a product, and relates to the field of robot obstacle avoidance, and the method comprises the steps: constructing a grid map of a target environment; according to the grid map, adopting an improved A * algorithm to obtain a global planning path; the improved A * algorithm constructs a dynamic heuristic function based on geometric distance and dynamic risk parameters on the basis of the A * algorithm; according to the global planning path, adopting a path decomposition method and an improved dynamic window algorithm to obtain a plurality of local planning paths; the improved dynamic window algorithm constructs an evaluation function based on a cross-modal fusion method and a collision time method on the basis of a dynamic window algorithm; and obtaining a robot path planning result according to all the local planning paths. According to the invention, the global path and local path planning of the robot is realized based on the improved A * algorithm and the improved dynamic window algorithm, the path planning efficiency and safety are improved, and the obstacle avoidance capability of the robot in a complex environment is enhanced.

The invention relates to the technical field of path planning, in particular to a cable path planning method based on deep learning and multi-objective optimization, and the method comprises the following steps: based on remote sensing image data, employing a multi-scale semantic segmentation deep learning model, combining with an auxiliary accessibility tagging sample, and recognizing the type of each ground feature; constructing a ground feature accessible confidence field with continuous spatial distribution; constructing a confidence coupling path cost function, endowing a penalty weight to an area with low ground feature confidence, endowing a passage reward weight to an area with high confidence, and generating a confidence coupling path cost function for path search; and constructing an improved heuristic function, and executing path search by adopting a multi-objective optimization algorithm to obtain a cable path scheme. According to the method, the historical total cost of the current node is considered, and the average confidence risk from the current node to the target direction is predicted, so that prospective avoidance is realized.

The invention relates to the technical field of intelligent warehouse logistics and industrial automation, in particular to a multi-AGV path planning method based on regional impedance perception and space-time dynamic coordination, and the method comprises the steps: obtaining grid map information and task information of each AGV based on a warehouse environment; constructing a local observation window taking the AGV as a central node, analyzing nodes covered by corresponding grid map information, determining a region passing impedance index, and constructing an obstacle distance penalty term; dynamically adjusting a heuristic function weight in an A * algorithm by adopting a regional passage impedance index, establishing an evaluation function in combination with an obstacle distance penalty term, and generating a single-machine global path; based on the task information of the AGV, generating a time window for each node in the global path, and judging the path conflict of the current storage environment through the time window; and a task priority strategy is preset, path conflicts are eliminated, and a cooperative scheduling instruction is generated to regulate and control the operation path of the AGV. And self-adaptive adjustment is performed by sensing the environment congestion degree, so that the search efficiency is improved.

The invention discloses an unmanned aerial vehicle path planning method based on comprehensive risk cost, which can generate a low-risk unmanned aerial vehicle flight path. The method comprises the steps that firstly, an unmanned aerial vehicle path planning model containing comprehensive risks and operation cost is constructed, the model comprises a target function and constraint conditions, and the target function is composed of airspace risk cost and unmanned aerial vehicle transportation cost; then, setting three-dimensional constraint conditions including a voyage constraint, a turning angle constraint and a maximum takeoff weight constraint, and constructing a feasible solution space conforming to physical characteristics of the unmanned aerial vehicle; then, performing initial path planning based on a heuristic search weight-A * algorithm with weight, and generating an initial feasible path meeting a constraint condition by dynamically adjusting a heuristic function weight balance path length and risk cost; and finally, based on a parallelogram rule, optimizing the turning point optimization of the initial planning path. According to the invention, a path planning solution with safety and economical efficiency is provided for unmanned aerial vehicle transportation in a complex low-altitude environment.

The invention relates to the technical field of deep-sea mining vehicle path planning, and discloses a deep-sea mining vehicle full-coverage path planning method based on an improved bio-activation neural network, and the method comprises the following steps: (1) analyzing the climbing dynamics of a deep-sea mining vehicle, and determining the passing time of each deep-sea mining region; (2) establishing a deep-sea mining operation environment model based on the determined passing time of each region; (3) establishing a grid heuristic function for deep-sea mining coverage operation requirements; and (4) in the deep-sea mining operation process, improving an escape path planning algorithm for dead zone escape demands, optimizing a target search mechanism and a fitness function, and performing point-to-point intelligent path planning on the mining vehicle. According to the method, an improved biological activation neural network algorithm and an escape algorithm are combined, deep-sea mining operation can be autonomously, accurately and efficiently completed under the condition that various seabed obstacles exist, and safety, energy conservation and mining efficiency are guaranteed.

The invention discloses a multi-sensor data fusion path planning method and system for a pipeline robot, and the method comprises the steps: collecting laser radar and inertial measurement data through an environment perception module, and executing time-space calibration in a semantic map construction module to generate a three-dimensional semantic map containing obstacle attributes; the global path planning module adopts an algorithm to dynamically adjust a heuristic function for a straight pipe, a bent pipe and a branch pipe to generate an initial path; and the local dynamic adjustment module establishes a rolling window based on the advancing speed, and replans the dynamic obstacle by using a fast expansion random tree algorithm. And the path execution conversion module performs smooth optimization on the path through quintic polynomial fitting and curvature change rate constraint and generates a bottom layer control instruction. Through the multi-source fusion and hierarchical planning strategy, the problems of pipeline environment positioning drifting and dynamic obstacle avoidance are effectively solved, and the stability and safety of robot advancing are remarkably improved.