258 results about "Heuristic function" patented technology

The invention discloses a path planning heuristic function generation platform and method based on collaborative optimization of a large-scale language model and evolutionary computation. According to the technology, the large-scale language model (LLM) and evolutionary computation (EC) work cooperatively. The platform generates or mutates a heuristic function expressed as an executable code through LLM based on a structured prompt containing an environment context and performance feedback; and an EC framework (such as genetic programming) is combined with performance evaluation feedback to perform selection and iterative optimization on a heuristic code population, and population diversity is maintained. The method aims at overcoming the limitation that a traditional heuristic design is difficult and poor in adaptability, a high-quality heuristic function adapting to a complex and dynamic environment is automatically generated, and therefore the efficiency of a path planning algorithm and path quality are remarkably improved.

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 discloses an intelligent storage robot path planning method, and belongs to the technical field of intelligent robots, and the method comprises the steps: improving a bidirectional A-Star algorithm, and combining a Time Elasticic-Band (TEB) obstacle avoidance algorithm, and providing a new intelligent storage path planning method. The improved A-Star algorithm adopts bidirectional search to replace unidirectional search of a traditional A-Star algorithm; 5-neighborhood search is adopted to replace 8-neighborhood search; introducing a self-adaptive dynamic search factor to improve a heuristic function; and smoothing the path by adopting a redundant node removal and linear interpolation method. The improved bidirectional A-Star algorithm searches to obtain a global optimal path, and the TEB algorithm optimizes and adjusts a local path according to the global path to complete dynamic obstacle avoidance. According to the method, the speed and precision of path planning are effectively improved, the local path can be adjusted in real time to avoid dynamic obstacles, and the requirement for path planning of the intelligent storage robot is met.

The invention discloses a path planning method based on an improved Hybrid A * algorithm and a DWA algorithm. The path planning method specifically comprises the following steps: establishing a grid map for a working environment and setting task starting and ending points; improving a heuristic function in the A * algorithm; the cost function of the Hybrid A * algorithm is improved, the path cost in the improved A * algorithm is used as the complete heuristic constraint cost with obstacles, the extension distance of the Dubins curve is used as the incomplete constraint heuristic cost without obstacles, and a variable step size search strategy based on the obstacle distance is adopted; smoothing the path searched by the global path planning algorithm; extracting path key nodes; optimizing a DWA local path planning algorithm; and outputting the optimal motion path. The efficiency of path planning of the workshop inspection robot in the unstructured complex environment is effectively improved, the real-time performance and safety are enhanced, and the workshop inspection robot can dynamically avoid obstacles while finding the optimal path in the complex environment.

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 a path planning method based on an improved artificial potential field method, and the method comprises the steps: carrying out the modeling of an environment through a grid method, and constructing a potential field map reflecting the distribution of obstacles; and searching an optimal path on the potential field map by adopting a minimum heap-first queue structure and combining a heuristic function considering the potential field gradient. For a local minimum trap problem, a new intermediate target point is selected near a trap point for re-searching by analyzing the potential field gradient change on a path. For special terrains such as narrow channels, a search strategy is dynamically adjusted to adapt to constraints. And the smoothness of the path is improved by normalizing and smoothing the potential field value of the node. In the path execution process, the potential field map can be dynamically updated according to real-time environment information, local re-planning is carried out on the affected path, and therefore dynamic obstacles can be effectively dealt with. According to the method, the intelligence, the safety and the adaptability of path planning are remarkably improved.

The invention discloses a mechanical arm obstacle avoidance path planning method based on an improved RRT-Connect algorithm, a smearing mechanical arm and a masonry robot. The mechanical arm obstacle avoidance path planning method comprises the steps that firstly, a fixed sampling function is adopted in a three-dimensional space formed by a starting point Xinit and a target point Xgoal of a path to generate a fixed point; and respectively and simultaneously constructing four random trees between the starting point and the fixed point and between the fixed point and the target point to carry out path search, and generating a path from the starting point to the target point. A target guiding sampling method based on a heuristic function can be introduced into the algorithm of the four-random-tree collaborative path searching mechanism, and the sampling probability is adjusted; and a better path planning effect is achieved by setting a self-adaptive step length strategy, a greedy strategy and other technical means. According to the method, the path planning of the mechanical arm is realized by setting the fixed sampling points to perform the four-way RRT-Connect algorithm, so that the rapid planning is ensured, the path time consumption is reduced, the collision with an obstacle is avoided, and the robustness of the path 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 an improved robot motion planning method based on Jump Point Search (JPS), and relates to a robot motion planning method based on JPS. According to the method, the search direction and the path planning process of the JPS algorithm are optimized, the path direction guidance of BFS (breadth-First Search) is combined, and the search direction priorities are sorted, so that the overall search efficiency and the path security are remarkably improved. Specifically, according to the method, a cosine value is used for measuring the similarity degree of each search direction and a BFS path, and the directions are divided into high priorities and secondary priorities. In the hop point searching process, the heuristic function value only evaluates the extended hop points in the high priority direction, and selects the hop point corresponding to the minimum value, so that the extension in the redundant direction is reduced, and the searching efficiency is improved. In addition, the invention also designs a set of path optimization strategy which mainly comprises two parts, namely a'security node updating strategy 'and a'redundant node eliminating strategy'. Aiming at the dangerous boundary path points, a security node updating strategy is utilized, and adjacent nodes are replaced by common security neighbor points, so that paths with potential safety hazards are prevented from being formed; according to the redundant node elimination strategy, the number of control nodes is reduced and the overall path length is shortened by detecting the direct connectivity between continuous nodes.

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 provides a lunar polar region sun synchronization # imgabs0 # path planning method, which can be applied to the technical field of celestial body image processing and path planning. The method comprises the following steps: independently training a plurality of enhanced U-Net models by using training data with time sequence slices and fusion types and a mixed loss function; dividing and extracting the reasoning data with a time sequence slice type and a time sequence fusion type by using a plurality of trained enhanced U-Net models to obtain a lunar polar region friendly illumination region, and preprocessing the reasoning data with the lunar polar region friendly illumination region and gradient data corresponding to the lunar polar region friendly illumination region; and based on the reasoning data after time alignment and the gradient data after space alignment, constructing a heuristic function by using mixed cost, and performing heuristic path planning by using an improved # imgabs1 # path planning algorithm with a time one-dimensional function and an in-situ waiting function to obtain a sun synchronization planning path of the lunar polar region.

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 discloses an unmanned surface vehicle formation path planning method, and relates to the technical field of formation path planning, and the method comprises the steps: carrying out the rasterization processing of a map, adjusting the initial unmanned surface vehicle formation, determining the starting point and terminal point information of a navigator, and determining the position of a dynamic obstacle and the information of a static obstacle. An attenuation coefficient is introduced into a heuristic function of the A-Star algorithm, and path planning is carried out by adopting the improved A-Star algorithm; path smoothing processing is carried out through a Bezier curve of a self-adaptive selection control point; introducing an improved dynamic window method to carry out dynamic local obstacle avoidance; and according to the initial unmanned ship formation pattern and the positions of followers in the formation, combining the global path of the navigator to generate a planned path of the followers, and keeping the followers at the same speed and deflection angle as the navigator. The method ensures the optimality of the path planning of the navigator, also ensures that the follower can adaptively adjust in real time, and effectively improves the efficiency, stability and practicability of the formation path planning.

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 a multi-mode network accessibility calculation method for comprehensive traffic, a calculation device and a storage medium, and the method comprises the steps: constructing a network structure of a comprehensive traffic network which comprises a road network, a railway network and an aviation network; recording longitude and latitude coordinates of a starting point and an ending point; a heuristic function of an A * algorithm is improved; the hierarchical search algorithm comprehensively considers travel paths in the traffic network, including intra-city travel paths of cities where a starting point and a terminal point are located, and inter-city travel paths connecting the cities where the starting point and the terminal point are located; calculating total travel time; and calculating reachability and diagnosing reachability depression. According to the method, the path search algorithm is improved with the aim of improving the efficiency, and the path search algorithm suitable for the multi-mode traffic network is constructed from the two aspects of optimizing the search path and improving the search strategy. And by introducing a hierarchical search strategy, unnecessary repeated search is effectively reduced, and the search efficiency of the algorithm is improved. Meanwhile, the algorithm fusion considers the influence of the transfer times and the train connection relationship on the travel path selection, and the actual travel behavior of the traveler is simulated more accurately.

The invention discloses a multi-layer virtual machine scheduling system based on a large language model, and the system comprises the following modules: an initial heuristic design module which is used for designing an initial heuristic function of a virtual machine scheduling problem; the scheduling scene feature analysis module is used for extracting and analyzing features of the cloud scheduling scene; the scene self-adaptive optimization module is used for performing independent optimization according to different scenes; the multi-scene strategy management module is used for managing cross-scene strategy switching; and the performance evaluation module evaluates the optimization effect in real time. By combining the intelligent reasoning capability of the large language model, the system can provide an efficient, flexible and self-adaptive scheduling optimization scheme, and the utilization efficiency of cloud resources is remarkably improved. Through the advantages of the large language model in the aspect of code generation, a heuristic function adaptive to virtual machine scheduling can be intelligently and efficiently generated, so that resource allocation is optimized, the efficiency and performance of a high-dynamic cloud scheduling problem are remarkably improved, and an innovative solution is provided for cloud resource scheduling.

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 provides a ground path planning method and device, and the method comprises the steps: determining a two-dimensional map based on the ground environment of a target planning region, the two-dimensional map comprising a starting node, a target node and an obstacle; sequentially searching from the starting node by adopting an improved A star algorithm to obtain each intermediate node between the starting node and the target node so as to obtain a planned path, each intermediate node is determined by estimating the cost of each non-obstacle node on the basis of each non-obstacle node adjacent to the previous node of the intermediate node and by adopting a heuristic function based on a dynamic weight; determining whether every three continuous nodes in the planned path in the direction from the starting node to the target node are collinear or not or whether a connecting line between two nodes except the node located in the middle in the three nodes does not penetrate through an obstacle or not to globally optimize the planned path, and obtaining a globally optimized planned path; and carrying out local path optimization on the globally optimized planning path by adopting a dynamic window method.

The invention discloses a concrete mixing plant production management system comprising the following modules: a discrete event modeling module used for constructing a discrete event simulation model; the data acquisition module is used for acquiring a real-time operation data set of the concrete mixing plant and inputting the real-time operation data set as a discrete event simulation model; the simulation execution module is used for executing an event-driven simulation process; the ant colony optimization module is used for constructing a scheduling optimization objective function and a path heuristic function according to the simulation data set, and executing search to generate an optimal material sending scheduling sequence; the scheduling execution module is used for executing a scheduling process according to the optimal material sending scheduling sequence; and the feedback control module is used for monitoring the execution deviation, transmitting the latest operation state back to the discrete event modeling module, triggering scheduling reconstruction, and realizing the production dynamic closed-loop optimization management of the concrete mixing plant. According to the invention, continuity and dynamic optimality of production scheduling are realized.

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 discloses an intelligent sampling robot path planning method, and particularly relates to the field of intelligent robot control, and the method comprises the steps: firstly, collecting data through a laser radar, a binocular RGB-D camera and an ultrasonic sensor, carrying out the time-space synchronization, constructing a first environment text, carrying out the fusion modeling, obtaining a state space and a semantic map, and carrying out the fusion modeling; and then constructing a heuristic function based on the sampling task priority and the like, generating an initial path by using an improved A * algorithm and performing smoothing processing, then judging whether to trigger an obstacle avoidance action according to sensor data, performing direction correction after completion, outputting a control instruction, and finally executing the control instruction. According to the method, environmental perception precision is improved through multi-source sensor fusion, path planning is optimized by using an improved algorithm, a complex environment is coped with in combination with a dynamic adjustment mechanism, sampling task specificity is fully considered, path planning efficiency, accuracy and environmental adaptability of the robot in sampling operation are improved, and the method has high engineering application value.

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.