131 results about "Heuristic function" patented technology

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 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 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 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 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.

The invention relates to a dynamic self-adaptive obstacle avoidance path planning method for a hoisting operation area, and belongs to the technical field of path planning in hoisting operation. The method comprises the following steps: scanning point cloud data by using three-dimensional laser, and establishing a static three-dimensional working environment model with a known environment; dividing a space by using a grid method, and dispersing a continuous space into a limited three-dimensional point set; setting a safe distance d; redefining a heuristic function eta; defining a volatilization coefficient; constructing a self-adaptive adjustment function considering the path length, the hoisting height and the safety factor; and updating pheromones, carrying out iteration, stopping iteration after an optimal effect is achieved, and obtaining a three-dimensional path. The method is improved on the basis of the ant colony algorithm, and efficient and safe path planning of the hoisting equipment in a complex environment is realized by combining strategies such as environment precise modeling, dynamic visual area definition, three-dimensional space rasterization processing and algorithm core optimization.

The invention relates to a storage AGV path planning method based on a hierarchical collaborative ant colony algorithm, belongs to the field of storage robot path planning, and aims to solve the problem of realizing better path planning in a complex storage environment. The method comprises the following steps: constructing an environment model of intelligent storage based on a grid method; an ant colony algorithm is improved on the basis of a hierarchical cooperative system, an ant colony is divided into scout ants responsible for exploration and foraging ants responsible for optimization, a heuristic function comprehensively considering the distance, the corner and a new exploration area is constructed, an improved pheromone updating strategy based on ant layering and the pheromone concentration on a self-adaptive volatilization factor updating path are adopted, and a new exploration area is obtained. The transfer strategy fuses three modes of random selection, probability selection and determinacy selection, and finally an improved ant colony algorithm is obtained; an improved algorithm is utilized to plan a global path for each AGV, and secondary optimization is performed in a targeted manner, so that the path length is shorter, and the smoothness is higher. And an effective solution is provided for AGV path planning in a complex storage environment.

The invention discloses a path planning method and system for a mobile robot, and a medium. The method comprises the following steps: acquiring starting and ending coordinates of the mobile robot, environment information and related parameters of an artificial potential energy method; performing path planning based on fusion of an improved algorithm and an improved artificial potential field method according to the starting and ending coordinates of the mobile robot, the environment information and related parameters of the artificial potential method to obtain a calculation path or failure information; wherein based on the fusion of the improved algorithm and the improved artificial potential field method, the cost value of each search node is improved on the basis of the improved algorithm, so that the cost value not only comprises the cost from the starting point to the node and the total value of the improved heuristic function, but also comprises the gravitation value and the repulsive force value calculated by the improved artificial potential field method. According to the invention, the obstacle avoidance function is realized to a certain extent, and the calculated path is short in length and smooth.

This invention relates to a path planning method for homotopy classes in planar mobile robots, based on sunlight sampling. The method includes dividing a two-dimensional planar map to be searched into a topological map composed of obstacle modules and free convex polygon modules using convex decomposition. Simultaneously, it records the relationships between convex polygon modules and their vertices, as well as the datasets of convex polygon modules and their adjacent convex polygon modules. A heuristic function is set to expand from the starting block, continuously recording the blocks traversed by the path and excluding parent and ancestor blocks to prevent path looping when advancing an adjacent block. Upon reaching the target block, the method stops and records a set of non-repeating path blocks as a path homotopy class. Sunlight sampling is performed on each path homotopy class to obtain the intra-class shortest path. This invention can quickly search for path homotopy classes in the topological map and uses the sunlight principle to search for intra-class shortest paths, achieving rapid partitioning of path homotopy classes and significantly improving the search efficiency for intra-class shortest paths.

The application provides an assembled building electromechanical pipeline automatic wiring method and system, constructs a grid space by relying on a building information model, extracts forbidden area and pipeline port coordinates, and gives grid medium conflict weight according to pipeline type; generates a comprehensive sorting key by combining pipe diameter, pressure grade and space bottleneck quantity, performs descending order sorting on the pipeline, removes isolated grids based on connectivity, and takes bottleneck cross-sectional area reciprocal as congestion penalty; expands the direction according to the turning constraint weight, matches the threshold to limit pipeline turning, fuses Manhattan distance and joint cost to construct a heuristic function, adjusts node cache screening multiple according to cache occupancy rate, completes grid space wiring by using A-star algorithm, and adjusts and regulates the soft forbidden area according to high-priority pipeline parameters; when the subsequent pipeline has no solution, backtrack the previous suboptimal node and rewire, and if the same pipeline has no solution for multiple times, reduce the conflict weight and modify the soft forbidden area thickness, and write the wiring path meeting the constraint to the building information model.

This invention provides an intelligent optimization and collision avoidance method for powder coating lifting fixture paths, relating to the field of powder coating production automation technology, including the following steps: S1: Constructing an environmental map of the powder coating production line, generating an initial lifting fixture path based on an improved A* algorithm, the improved A* algorithm optimizing the heuristic function by introducing a direction cost factor; S2: Iteratively optimizing the initial path generated in S1 using an adaptive inertia weight PSO algorithm to obtain a multi-objective optimal path; This invention introduces a direction cost factor into the heuristic function of the A* algorithm, fully considering the matching degree between the lifting fixture's movement direction and the target direction, resulting in an initial path that better conforms to the lifting fixture's movement characteristics, reducing redundant segments and unreasonable turns; Combined with the adaptive inertia weight PSO algorithm, the inertia weight is dynamically adjusted according to the iteration process, which can accurately adapt to the path optimization needs of different production line scenarios, achieving multi-objective optimization of lifting fixture running time, path length, and workpiece sway amplitude.

The invention discloses a path planning method based on a city engine and an unreal engine. The method comprises the following steps: completing basic data preparation, rule definition, model generation and visual presentation and analysis, and establishing a city model; secondly, constructing a vehicle motion model in an unreal engine, and simulating weather; the three-dimensional static model and a dynamic model in an unreal engine are fused to generate an urban environment; carrying out geographical gridding on the urban area; introducing a trafficability map and physical restrictions of the vehicle, and setting the trafficability map of the vehicle; an improved A * algorithm based on geographic gridding is introduced, path planning is conducted, the improved A * algorithm is used, and path optimization is achieved through a heuristic function and a penalty function; and outputting the generated path plan.

The invention belongs to the technical field of task planning of unmanned aerial vehicles, and provides an unmanned aerial vehicle route planning method fusing a collision probability model and an A star algorithm in order to solve the problem that the route safety of the unmanned aerial vehicle cannot be guaranteed, and the method comprises the steps: dispersing a flight region of the unmanned aerial vehicle into grids, marking obstacles, and determining a starting point and a target point, an A star algorithm model is initialized, and the collision probability is determined based on the unstable item and the positions of the unmanned aerial vehicle and the obstacle; determining an adaptive environment heuristic factor according to the collision probability, and weighting a heuristic function of the A star algorithm model; determining a global environment correction factor based on the shortest distance and the second shortest distance from the unmanned aerial vehicle to the obstacle, and improving a weighted heuristic function; improving a cost function based on the obtained new heuristic function; and planning a safety path of the unmanned aerial vehicle based on the improved cost function. According to the invention, the problems of wall-attached flight, high collision risk and insufficient environmental adaptability of an existing A star algorithm path are solved, and the safety of path planning is improved.