328 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 provides a path planning method and system for an automatic driving trolley, and relates to the technical field of path planning, and the method comprises the steps: obtaining the data of an acoustic sensor of an underwater environment, and obtaining the contour of the underwater terrain and the position information of an obstacle; carrying out dynamic environment modeling processing; the Euclidean distance is used as a heuristic function to calculate the dynamic model of the underwater environment, and a second optimized path conforming to kinematics and dynamics constraints of the automatic driving trolley is obtained; carrying out smoothing processing on the second optimization path through a dynamic motion primitive technology; constructing a three-dimensional map for path planning and obstacle avoidance; predicting an action track of the robot in a period of time in the future; and through real-time adjustment processing of the self-adaptive control system, path planning of the automatic driving trolley is completed. According to the method, the dynamic characteristics of the underwater environment and the motion constraint of the automatic driving trolley can be comprehensively considered, the three-dimensional map containing the obstacle and topographic information can be constructed in real time, and the future motion trail of the obstacle can be predicted.

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 discloses an intelligent vehicle path planning method based on an improved Astar algorithm, and the method comprises the following steps: S1, enabling an intelligent vehicle to obtain environment information around a current map, building a grid map, and building a barrier-free state region and a barrier state region in each grid; s2, creating binary heap tables OpenList and CloseList according to the established grating map, defining coordinates of a starting point and an end point, and storing the coordinates of the starting point into the OpenList table; s3, selecting a distance calculation function, improving a heuristic function, introducing an evaluation function weight coefficient, and dynamically adjusting the weight of the heuristic function; s4, improving an Astar algorithm to expand a child node selection rule, and adjusting 8 neighborhood diagonal movement cost; and S5, deleting redundant collinear nodes and turning points on the basis of a Ramer-Douglas-Peucker (Ramer-Douglas-Peucker) algorithm, and obtaining an optimized path. According to the method, the global path planning speed and safety of the Astar algorithm are improved.

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 relates to a mobile robot path planning method and system, and the method comprises the steps: determining a multi-neighborhood mixed search mechanism for the mixed use of eight neighborhoods and new sixteen neighborhoods according to whether there is an obstacle in the eight neighborhoods of a current node; deleting invalid nodes according to the positions of the current node and the target node; a bidirectional search strategy is adopted, a dynamic weight coefficient is added to adjust the heuristic function, and a planned path is obtained; and a redundant inflection point deletion strategy is adopted for the planned path, useless inflection points are removed, and an optimal path is obtained. By improving a neighborhood search mechanism, the search efficiency of a path planning algorithm is improved; through a node optimization strategy, invalid nodes in the algorithm searching process are removed, and the path planning algorithm searching time is shortened; by introducing a two-way search mechanism, the algorithm search efficiency is improved, meanwhile, the dynamic weight factor is added to adjust the algorithm search speed, search of invalid regions is reduced, and therefore the search efficiency is improved.

The invention belongs to the technical field of unmanned aerial vehicles, and particularly provides a transformer substation unmanned aerial vehicle autonomous inspection route generation method based on an optimization A star algorithm. According to the method, a heuristic function is improved, the Euclidean distance between a target point and a current node is comprehensively considered, obstacle avoidance processing is carried out, and a safer and more efficient inspection route is generated. And by calculating the total cost of each node, the node with the minimum total cost is selected for expansion until a target node is found. According to the improved A star algorithm, the unmanned aerial vehicle can be effectively prevented from flying into an obstacle dense area, the obstacle keeps a safe distance, and the flying safety is remarkably improved. The distance and the obstacle density are comprehensively considered, the generated inspection route is smoother and more efficient, and unnecessary flight distance and energy consumption are reduced.

The invention discloses a path planning method for a mine rescue robot, relates to the field of path planning of an intelligent algorithm, plans an optimal path of the mine rescue robot on a rasterized map, and aims to solve the problems of low search efficiency, multiple turning times, path redundancy and easiness in falling into a local optimal solution in the initial stage of iteration. The method comprises the following steps: establishing a grid map environment model; changing a heuristic function of a pseudo-random probability ACS algorithm to obtain an initial path; performing non-uniform distribution on initial pheromones through the initial path, and performing iteration; obtaining a neighborhood search mechanism according to a map environment, and selecting path nodes to form a path; smoothing each path successfully reaching the end point; a pheromone reward and punishment mechanism is added, and arrangement of pheromones is changed; and next iteration is carried out to obtain a final optimal path. The method is suitable for path planning in the fields of Internet of Things, cloud computing and the like, and an innovative solution is provided for path planning of the mine rescue robot.

The embodiment of the invention provides an unmanned aerial vehicle autonomous obstacle avoidance and exploration method and device, electronic equipment and a medium, and the method comprises the steps: completing the pose estimation of an unmanned aerial vehicle and the construction of a three-dimensional point cloud map based on an RGB image and a depth image through employing a VINS-Fusion algorithm for a dense forest scene, and generating an octree map; a heuristic function and a cost function in the Hybrid A * algorithm are improved, and the improved heuristic function and the improved cost function are obtained; initializing Fast-planner algorithm parameters, based on the octree map, through an improved heuristic function, calculating obstacle cost, updating obstacle three-dimensional coordinates, calculating a flight safety distance, through an improved cost function, traversing local point clouds in a neighborhood range of the unmanned aerial vehicle, searching a path with the minimum cost, extracting an optimal path course, and constructing a safety corridor; and initializing parameters, and completing autonomous obstacle avoidance and exploration in the constructed safe corridor according to the optimal path course.

The invention provides an AGV robot path planning method and system. The method comprises the steps that a map node graph, robot parameters, a starting node and a target node are acquired; introducing a robot constraint condition, and calculating an evaluation cost value of the robot from a current node to a target node based on a heuristic function; according to the evaluation cost value of the robot from the current node to the target node, based on an A * search algorithm, searching an optimal path of the robot from the starting node to the target node in map nodes; and generating a path instruction from the optimal path, and sending the path instruction to the robot for execution. Based on an advanced path planning algorithm, the path of the AGV robot is automatically planned, the optimal planning path is searched, and high efficiency of task execution of the AGV robot is achieved.

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.

A method for selecting one or more Regions of Interest (RoIs) for human annotations includes obtaining sensor data generated by one or more sensors of a vehicle; applying at least one class-agnostic heuristic function to the sensor data to determine a presence and an approximate position of one or more objects in an RoI of the sensor data; selecting one or more RoIs having proposed annotations for the one or more objects for refinement by an annotator; and outputting the one or more selected RoIs.

The invention provides a deep reinforcement learning kernel emergency robot path planning method introducing a dose factor, and relates to the technical field of robot path planning, the method takes a deep reinforcement learning algorithm as a basis, utilizes an A * algorithm heuristic function, introduces an accumulated radiation dose operator and a distance operator, and obtains the path planning result of the deep reinforcement learning kernel emergency robot. Training to obtain a target network applied to radiation environment path planning; and by using the target network, when the radiation dose and the obstacle are dynamically changed, completing adaptive adjustment of the path of the nuclear emergency robot. According to the method, the interaction capability of the nuclear emergency robot with the environment in the operation process is enhanced, a collision-free self-adaptive operation path with small radiation risk is provided for the nuclear emergency robot, and support is provided for emergency decision making of the nuclear emergency robot.

The invention belongs to the field of robot path planning. The invention provides an improved A * path planning method based on driver guidance and dynamic map segmentation, and aims to solve the problems that an existing path planning method is high in calculation complexity and insufficient in practicability in complex terrains and large-scale maps. A Bezier curve is adopted to fit a driver guiding path, and a dynamic map cutting method is designed based on the path, so that the calculation burden of useless areas is effectively reduced. And comprehensively considering the cost of the path length, the energy consumption and the path risk, optimizing the actual cost function and the heuristic function of the A * algorithm, and smoothing the planned path by adopting a cubic spline curve. The method can improve the efficiency and practicability of path planning.

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 relates to the technical field of robots, and discloses a mechanical arm obstacle avoidance path planning based on an improved bidirectional RRT algorithm, which comprises the following steps: defining a mechanical arm and a working space: determining a kinetic model and kinematics constraint of the mechanical arm and obstacle distribution in the working space, by reasonably setting a starting point, a target point and algorithm parameters, it can be ensured that the algorithm has high exploration efficiency and convergence speed in the initial stage, a heuristic search strategy, such as a heuristic function in an A * algorithm, is introduced on a node expansion strategy, nodes can be guided to expand in a more desirable direction, and therefore the path planning quality is improved, and the path planning efficiency is improved. The position and the motion state of an obstacle are detected in real time, the node expansion direction is dynamically adjusted according to detected information, the mechanical arm can be effectively prevented from colliding with the obstacle, and the function is achieved generally through sensor data such as a laser radar and a camera.

The invention discloses a path planning method for an unmanned surface vehicle, which relates to the technical field of ocean engineering and comprises the following steps: determining starting point and terminal point information, dynamic obstacle information and static obstacle information of the unmanned surface vehicle in a rasterized map; improving a heuristic function of the A * algorithm; an OPEN table and a CLOSE table are established for the starting point, searching is started from the starting point through an improved A * algorithm searching strategy, and child nodes too close to an obstacle are deleted; judging a global evaluation function of child nodes of the OPEN table, taking a point with the lowest global evaluation function as a new father node, adding the newly generated father node into the CLOSE table, improving a node array, reducing path nodes, and forming a global path; performing path smoothing on the global path; and after the unmanned ship is far away from the dynamic obstacle, the unmanned ship takes the point of the original global path as a sub-target point and continues to return to the original path. According to the invention, the path planning length is reduced, and the safety of the unmanned ship is effectively improved.

The invention discloses a TTPA-LSTM soft measurement method based on multiple sampling rates, and belongs to the field of soft measurement modeling and application in the modern industrial process. The method comprises the step of accurately predicting the concentration of butane at the bottom of the debutanizer in the industrial process by constructing a TTPA-LSTM model. According to the invention, through a non-incremental heuristic function related to a time interval in the time sensing module, short-term memory is weighted, more information is reserved while a down-sampling rate method is simulated, and the problem of difficult feature extraction caused by high and low sampling rates is solved; on the other hand, a time mode attention module is introduced, cross-time-step integration is achieved on obtained features through a two-dimensional convolution filter, the problem of multivariate time sequence time delay is solved, meanwhile, the prediction capacity of the model is improved, and the problem existing in actual industrial production is solved.

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 provides an unmanned ship path planning method based on an improved RRT algorithm, and the method comprises the following steps: collecting environment information and depth information around an unmanned ship in real time through a sensor, and fusing the environment information and the depth information, thereby obtaining a planning map; calculating the boundary information of the obstacle in the planning map based on an edge detection algorithm to obtain the boundary information of the obstacle; constructing a heuristic function according to the obstacle boundary information and the path target point of the unmanned ship, and guiding an RRT algorithm to sample through the heuristic function to obtain a first global path; pruning and smoothing the first global path to obtain a second global path; performing local planning according to the second global path and the environment information to obtain a local planning path; and the unmanned ship travels according to the local planning path. Compared with the prior art, the unmanned ship path planning efficiency and quality are effectively improved.

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 relates to the technical field of unmanned aerial vehicle flight path planning, and discloses an unmanned aerial vehicle cluster four-dimensional flight path planning method for a high-density operation scene, and the method comprises the steps: constructing an urban airspace four-dimensional grid model, and completing the four-dimensional spatio-temporal data reconstruction of an unmanned aerial vehicle; the urban airspace four-dimensional grid model comprises a fixed four-dimensional body model, an unmanned aerial vehicle four-dimensional point model and a track four-dimensional line model; constructing a four-dimensional flight path planning model of the unmanned aerial vehicle; improving an adaptive heuristic function based on a 4D-A * search algorithm; and obtaining the optimal flight path of the unmanned aerial vehicle based on the operation cost and the operation risk obtained by the improved adaptive heuristic function. According to the method, high-density operation conflict-free real-time four-dimensional flight path planning of the urban unmanned aerial vehicle cluster can be effectively completed, the operation efficiency is improved, and the operation risk is reduced.