214 results about "Global planning" patented technology

The invention relates to the technical field of automatic workpiece grabbing and visual servo control, and discloses a workpiece grabbing method and system based on visual feedback, and the method comprises the steps: obtaining a real-time image through a front-view camera, a side-view camera and a top-view camera, and extracting surface features through a feature fusion network; inputting the features into a pose estimation model based on a particle filtering framework, and iteratively updating the pose in combination with a multi-dimensional observation likelihood function to generate prediction parameters; constructing a multi-target path planning model based on the parameters, and optimizing the path by adopting a dynamic planning algorithm with the shortest path and the minimum joint movement as targets; a hierarchical control model is established, a strategy layer performs global planning, an adjustment layer corrects a local path, and an execution layer realizes trajectory tracking through visual servo control and outputs a control instruction to complete grabbing. Through multi-view perception, robust pose estimation, global optimization path planning and hierarchical control, the precision, efficiency and stability of workpiece grabbing in a complex environment are improved.

The present disclosure belongs to a field of UAVs exploration technology, discloses a high-efficient autonomous exploration method, system, and terminal for UAVs, comprising: S1, heuristic waypoint generation: setting an exploration scope and waypoint spacing, and generating waypoints through waypoint generation algorithms; S2, global path planning: after generating heuristic waypoints, an A * algorithm is used to generate the global planning path; S3, real-time positioning and mapping: using point clouds for real-time positioning and mapping; S4, local B-spline trajectory generation: using B-spline parameterization method to generate local trajectories; S5, real-time obstacle avoidance and dynamic feasibility constraints: optimizing the trajectories to achieve fast convergence, generating smooth, collision-free, and dynamically feasible trajectories; S6, local real-time replanning: using a time sliding window for local replanning; S7, flight control: Using UAV control algorithms for controlling of UAVs robustly.

The invention discloses an inspection robot navigation method, system and device based on Beidou and binocular vision fusion and a storage medium, and relates to the field of robot navigation and obstacle avoidance, and the method comprises the following steps: carrying out multi-modal data fusion processing based on an initial coordinate of an inspection robot and an image captured by a binocular vision system to obtain an environment sensing result; carrying out global planning and local optimization based on an environment perception result, and carrying out cooperation to generate a trajectory planning scheme; based on the environment sensing result and the trajectory planning scheme, intelligent navigation cooperative regulation and control are carried out, and an inspection robot motion control strategy is obtained; by improving the environmental perception accuracy, optimizing the path planning and performing intelligent navigation regulation and control, the navigation problem of the inspection robot in complex environments such as a transformer substation can be effectively solved, the inspection safety and efficiency are improved, and the method has remarkable practical application value.

The invention discloses a hierarchical multi-agent robot cluster control method and system, and aims to solve the problems of partial observability and environment non-stability of a multi-agent system in a complex environment. According to the method, a three-layer layered reinforcement learning architecture is constructed, a high-layer strategy is responsible for global task decomposition and role allocation, a middle-layer strategy converts tactical intention into a cooperative behavior mode, and a low-layer strategy executes accurate motion control; a graph neural network is adopted for cluster modeling, global graph representation and local neighborhood features are extracted in parallel through graph convolution and an attention mechanism, and hierarchical decision making is supported; a centralized graph enhancement evaluation network is designed to be combined with an MAPPO algorithm for collaborative optimization, and dynamic adversarial training is introduced to improve strategy robustness. According to the method, effective decoupling of global planning and local control is realized, and the cluster cooperation efficiency, the strategy interpretability and the adaptive capacity in a dynamic environment are improved.

The invention discloses a robot dog inspection path intelligent planning and dynamic adjustment method, system and device and a medium, and belongs to the technical field of robot path planning, and the method comprises the steps: calculating a task emergency degree index according to a weight coefficient of an inspection point and a time constraint, and determining a task priority; constructing a hierarchical electronic map containing the terrain difficulty coefficient and the moving cost; performing global path planning through a comprehensive cost function by adopting an A star algorithm; environment information is collected in real time through multiple sensors, and the dynamic obstacle layer is updated; performing real-time trajectory optimization by adopting a local path planning mode; selecting local path correction or global path re-planning according to the path execution state score; energy needed for completing the remaining tasks is predicted, and a charging path is planned when necessary. According to the invention, multi-target adaptive optimization of path planning is realized, a coordination mechanism of global planning and local adjustment is established, and the execution efficiency and reliability of inspection tasks are improved.

The invention provides an agent illusion correction method and device. The method comprises the following steps: firstly, analyzing and globally planning a problem input by a user, generating an execution plan comprising at least one subtask, performing reasoning circulation and retrieval enhancement based on the execution plan, generating an enhanced context evidence set, then performing screening and context optimization processing on the enhanced context evidence set, generating an optimized context, and finally, performing context optimization processing on the optimized context. And then, generating an initial answer based on the optimized context and the question, carrying out factuality and logic consistency closed-loop verification on the initial answer to detect whether illusion exists or not, if the illusion is detected, correcting the initial answer, generating a corrected answer, and recording the illusion event and the correction process to the reflection knowledge base. According to the method, self-correction and continuous optimization are realized through a closed-loop mechanism of problem decomposition, global planning, reasoning circulation, retrieval enhancement and optimization and correction reflection, so that the reliability and interpretability of agent answering are improved.

The invention discloses an automatic guided vehicle path optimization method and system based on fusion planning, and relates to the field of robot technology, computer science and automation control, and the method comprises the steps: carrying out the global path optimization based on an improved A star algorithm according to a two-dimensional grid map, and obtaining a global optimal path and a key path point set; performing local path planning based on an improved dynamic window algorithm according to the global optimal path and the key path point set, and obtaining a local obstacle avoidance track and an optimal speed control instruction; and based on the optimal speed control instruction and in combination with the local obstacle avoidance trajectory, obtaining an automatic guided vehicle path optimization result. According to the method, on the global planning level, the path length and the number of redundant nodes are remarkably reduced, the steering frequency is effectively reduced, and the path smoothness and the performability are improved; in a local planning level, efficient obstacle avoidance and real-time trajectory optimization in a dynamic obstacle environment are realized.

The invention provides an intelligent pipeline welding method and system based on data decision, and relates to the technical field of intelligent pipeline welding.The intelligent pipeline welding method comprises the steps that a welding request is responded, a groove image of a to-be-welded pipeline is obtained through an active visual sensor, groove morphology characteristic parameters of the to-be-welded pipeline are determined based on the groove image, and the to-be-welded pipeline is welded according to the groove morphology characteristic parameters. Based on the groove morphology characteristic parameters, corresponding basic layer channel arrangement rules and reference process parameters are determined from a preset layer channel arrangement database, a welding process scheme matched with the current groove is generated in combination with the groove morphology characteristic parameters, and the current groove is welded on the basis of the welding process scheme. And the welding operation is executed in the groove of the to-be-welded pipeline, the technical problems that in the prior art, the global planning capacity is deficient, the deviation correction strategy is locally lagged, and the whole-process accurate control is insufficient are solved, and the effect of improving the stability and reliability of the welding quality of the pipeline is achieved.

The invention provides a task processing method and device, an intelligent agent, electronic equipment and a readable storage medium, and relates to the technical field of artificial intelligence such as large models, intelligent agents and visualization. The method comprises the steps that a to-be-processed problem input by a user is disassembled through a preset large model, and a global planning list which is output by the large model and composed of at least two main tasks arranged according to an execution time sequence is obtained; presenting the global planning list to the user in a graphical interface mode; according to a task execution instruction issued by a user to the current global planning list, corresponding tasks are executed in sequence through the large model according to the task execution time sequence recorded in the current global planning list, and corresponding execution state identifiers are presented for the corresponding tasks in the current global planning list according to the actual execution states of the tasks. According to the method, the global planning list of the to-be-processed task is fed back to the user before the task processing is started, so that the efficiency of obtaining the task execution result meeting the expectation of the user is improved.

The invention proposes a robot path planning algorithm based on a particle swarm optimization algorithm and a dynamic window method, and relates to the field of control theories and electronic information, and the method comprises the steps: introducing an inertia weight updating strategy based on a state factor, setting adaptive parameters and crossover and mutation operators to improve the global search capability, increase the population diversity, and improve the robot path planning precision. On-line self-adaptive updating of inertia weight and learning factors is realized on line in combination with Q-learning, gene combination modes are enriched through crossover operators, convergence and exploratory performance of the algorithm are improved, an obstacle avoidance strategy of a traditional DWA algorithm is improved, weight parameters of a dynamic window evaluation function are dynamically adjusted according to real-time information of a target and an obstacle, and an obstacle avoidance algorithm is established. According to the method, the global planning is adopted, the path points generated through global planning are adopted as temporary targets, fusion of MOQLCOPSO and the improved DWA algorithm is achieved, local optimum is effectively avoided, the planning efficiency and path safety are improved, and the method is suitable for mobile robot navigation under the complex three-dimensional terrain.

The embodiment of the invention relates to the technical field of intelligent driving, and discloses a motorcade passing control method, device and equipment based on global planning, and the method comprises the steps: obtaining the driving related information of vehicles in a motorcade; the motorcade comprises a plurality of vehicles, and the driving related information comprises at least one of road condition information, vehicle driving information and map navigation information; according to the driving related information of at least one vehicle in the motorcade, performing global planning by taking the vehicles in the motorcade not to fall behind or the fallen vehicles to get up to the motorcade as a control target, and determining a target state of a target vehicle in the motorcade; the target state indicates driving information needing to be executed by a target vehicle, and the target vehicle is any vehicle in a motorcade; and sending the target state of the target vehicle to the target vehicle to control the target vehicle to be switched from the current state to the target state. By applying the technical scheme of the invention, the current problems that the formation vehicles are easy to fall behind and are difficult to recover because of being cut off in the complex urban road can be solved.

The invention provides a three-dimensional data generation method and device, and the method comprises the steps: carrying out the planning of a text prompt before generation, optimizing the three-dimensional data in stages, introducing a reflection and correction mechanism in the generation process, recognizing and correcting defects through a multi-modal large model, and introducing a cross-view alignment constraint. And global planning and local fine adjustment of the three-dimensional generation process are realized. According to the method, a complex generation task is decomposed into a closed-loop process of planning-execution-reflection-correction, and multi-view consistency is forced from a feature level, so that geometric consistency and semantic accuracy of generated three-dimensional data can be effectively improved, the problems of structural artifacts and Janus which are easy to occur in the prior art are avoided, and the generation efficiency of the three-dimensional data is improved. And finally, high-quality three-dimensional data with consistent views are generated.

The invention discloses a robot dynamic path planning method and system fused with lightweight LLM. The robot dynamic path planning method comprises the following steps of dynamically mapping a grid map, constructing a semantic analysis model, performing global path planning by adopting an improved A * algorithm, and performing local path optimization by adopting an improved DWA algorithm. Specifically, according to the method, the high-frequency capture and real-time updating of the dynamic obstacle are realized by introducing the fusion of the event flow of the event camera and the depth information of the laser radar; dynamic matching of semantic instructions and planning parameters is achieved by introducing lightweight LLM, meanwhile, sight distance penetration rate optimization path connectivity is fused in global planning, local obstacle avoidance adopts a semantic adaptive weight adjustment mechanism, the high-precision perception advantage of multi-sensor fusion is guaranteed, task adaptability and scene pertinence are improved, and the method is suitable for large-scale popularization and application. And the complex requirements of logistics scenes are met.

The invention provides a long-range visual question and answer and multi-modal reasoning task agent construction method. For input images and natural language questions, generating answers through orderly calling of an external tool set; the construction process of the intelligent agent is divided into three stages: a global planning stage: analyzing user requirements through a global planning navigator, and screening tool subsets; in the tool autonomous execution stage, visual details are supplemented, background knowledge is retrieved and accurate operation is carried out in the multi-step process by surrounding three types of tools through a tool autonomous executor, and a returned tool result is used for assisting the next step of decision making; in the response synthesis stage, the reasoning process is combed through a model response synthesizer, and user-friendly and clear-format responses are sorted; and finally, the model response synthesis module extracts and obtains answers from the reasoning track and outputs the answers. The problems of global planning insufficiency and visual context forgetting generally existing in complex task reasoning of an existing multi-mode large language model are solved.

The invention discloses a robot inspection method, device and system for a polycrystalline silicon production park. The method comprises the steps that firstly, a robot inspection path is planned according to a dynamic risk map, a global planning path is obtained, and the dynamic risk map is constructed based on multi-dimensional environment data; according to the global planning path, controlling the robot to inspect in the polycrystalline silicon production park; performing local adjustment on the global planning path based on the regional risk in the robot inspection process to obtain an adjusted global planning path; and finally, according to the adjusted global planning path, controlling the robot of the polycrystalline silicon production park to continue to inspect. According to the routing inspection method, the dynamic risk map is combined, and real-time dynamic optimization of the routing inspection path can be realized through cooperation of global planning and local adjustment, so that emergencies can be quickly responded.

The invention discloses a vehicle path planning interaction method based on vehicle-road cloud collaboration, and the main conception is that a communication link is decomposed into multiple paths of signals, the signals are dynamically switched according to real-time signal quality, seamless degradation is realized, and based on a three-stage elastic architecture of cloud global planning, marginal layer region collaboration and vehicle end local execution, the vehicle path planning interaction method based on the marginal layer region collaboration is realized. A communication degradation control and computing power resource dynamic allocation strategy is utilized to avoid contradiction generated by communication delay and computing power allocation; besides, based on a vehicle-road cloud collaborative architecture and in combination with a space-time consistency guarantee scheme, on the premise of ensuring that the vehicle end can autonomously change a lane during communication interruption, the safety problems that the deviation between the vehicle end autonomous decision and a global path is too large and rapid correction is difficult after communication recovery are solved. According to the invention, aiming at the specific working condition that the communication link is unstable and the computing power resource is limited, the cost benefit of the automatic driving algorithm is obviously optimized, and the driving safety is effectively improved.

The invention relates to a robot global path planning and local obstacle avoidance method and device and a mobile robot. The robot global path planning and local obstacle avoidance method comprises the following steps: S1, constructing an immunoaffinity function; s2, establishing a two-dimensional plane environment model; s3, global path optimization based on an individual body type top-level food grazing strategy; s4, carrying out local obstacle avoidance detection; s5, obstacle grid evaluation based on a pseudo-virus diffusion algorithm; s6, building an obstacle protection area and controlling fitting motion; and S7, continuously monitoring the state of the obstacle in the obstacle avoidance process, and controlling the robot to adjust the trajectory to return to the original global planning path when detecting that the obstacle is separated from the obstacle avoidance area. According to the invention, global shortest path planning is realized through local area path optimization, and the traffic efficiency is improved.

The invention relates to a steel production-oriented multi-base multi-production-line multi-process global plan optimization method and system. The method comprises the following steps of: receiving and standardizing order demand data; executing hierarchical processing and distribution of multi-base orders, and outputting a base-level order set; deducting and marking the net production demand quantity of the base-level orders; executing multi-production-line order allocation in a single base, and generating a production line level order set; performing cross-process collaborative batch grouping on the production line level order set based on steel making-continuous casting-rolling-post-processing process constraints to generate a cross-process batch plan; constructing a global evaluation model, performing comprehensive evaluation on an intermediate result, and generating a parameter correction instruction based on an evaluation result for adjusting parameters, constraints or distribution strategies of subsequent modules; and finally, generating a production plan scheme with consistent cross-base, cross-production-line and cross-process, and triggering rolling optimization update when disturbance occurs. According to the invention, unified coordination and global optimization of multi-module plan results are realized.

The invention provides a vehicle path planning method fusing improved RRT and Trajectory Mamb-DWA, and the method is suitable for a dynamic traffic environment. In the global planning stage, the search efficiency of the RRT algorithm is optimized through KD-Tree, the random sampling range is limited in combination with a safe sampling area, the directivity of the path is enhanced by utilizing adaptive target gravitation bias, and a global optimal path is generated by adopting Bezier curve smoothing. In the local obstacle avoidance stage, a Trajectory Mama trajectory prediction model is embedded in a dynamic window method, the movement trends of surrounding vehicles and pedestrians are predicted, the speed of the vehicle is restrained through time sequence collision detection, and an obstacle avoidance trajectory with both real-time performance and foresight is generated. And aiming at the path connection problem after obstacle avoidance, designing a dynamic re-planning mechanism: according to the current environment state, comparing expected time for returning to an original path or generating a new path with a collision risk, and adaptively selecting an optimal scheme. The method provides key technical support for efficient navigation of the autonomous vehicle, and is of great significance to development of a driving assistance system.

The invention provides a double-layer intelligent agent optimization method, device and equipment for multiple mechanical arms and a storage medium. An RRT-Connect algorithm and B spline track smooth optimization are introduced into a high layer, and global path planning is conducted on environment information of a carrying task; the smoothed centroid trajectory is converted into a reference trajectory sequence at the tail end of each mechanical arm by using a kinematics chain model, then the reference trajectory is used as a bottom layer distributed real-time control guide signal driven by an MAPPO algorithm, and comprehensive feedback is performed on task completion degree, motion smoothness and coordination in combination with a multi-layer reward function; and all the mechanical arm intelligent bodies are guided to cooperatively adjust joint actions in the tracking process, so that seamless connection of global planning and local control is achieved, and the smoothness and cooperative efficiency of carrying motion are remarkably improved.

The invention discloses an urban environmental sanitation operation optimization method based on big data, and relates to the technical field of intelligent environmental sanitation optimization, and the method comprises the steps: collecting urban environmental sanitation data, building a pollution region model, abstracting a detection process of mobile detection equipment into a Poisson random process, and carrying out the optimization of the Poisson random process; the method comprises the following steps: deducing a probability distribution function detected in a polluted area, then calculating the lowest detection frequency of each area, calculating the optimal detection frequency of each area by adopting a simulated annealing algorithm, and carrying out global planning and scheduling on a sanitation operation path by using an improved NSGA-III multi-objective optimization algorithm. Through combination of a Poisson random process model and a simulated annealing algorithm, scientific quantification and optimal distribution of detection frequency are realized, the purpose of accurate detection is achieved, the utilization rate of detection equipment is effectively improved, the regional detection completion degree is improved by using the improved NSGA-III, and meanwhile, resource consumption of invalid operation is reduced. And the environmental sanitation task execution is more efficient.

The invention relates to the technical field of automatic driving global path planning, and particularly discloses a global path planning and homeward voyage method based on a satellite map, and a global path planning module in an unmanned vehicle automatic driving system, and the global path planning module comprises the steps of constructing a road network according to the satellite map, performing global planning according to task points, performing re-planning, and performing homeward voyage. The global path planning module is provided with an I / O data interface and can receive vehicle position information and send a global planning result. For the satellite map, the drivable road sections are manually distinguished, and the road sections are manually marked through related software; according to a breadth-first algorithm, the optimal driving route is solved according to the occurrence sequence of the task points until the vehicle smoothly arrives at the task end point; after the vehicle arrives at the task end point, the program allows to select two return flight modes, i.e., return flight on the original path and return flight on the nearest path; the driving road network is drawn based on the satellite map, global path planning and homeward voyage are achieved, and the method is high in adaptability, low in operation difficulty and low in hardware requirement.

The invention discloses a mobile robot path planning method based on deep reinforcement learning, and relates to the technical field of robot path planning. According to the method, a mobile robot path is divided into global planning and local obstacle avoidance, and local obstacle avoidance is realized through a BiLSTM-D3QN model; analyzing the first sensing data through a decision branch of the BiLSTM model to obtain a decision parameter, and comparing the decision parameter with a set parameter threshold to judge whether local obstacle avoidance is performed or not; when it is determined that local obstacle avoidance needs to be carried out, the BiLSTM model outputs obstacle avoidance parameters, then a D3QN model is used for generating a local obstacle avoidance path, and autonomous obstacle avoidance of the mobile robot is achieved; according to the invention, the BiLSTM model bidirectionally captures the context dependency relationship of the time sequence data, can simultaneously fuse forward and backward time sequence features, judges whether local obstacle avoidance is needed or not by using multi-dimensional data, defines the access opportunity of local obstacle avoidance, and improves the accuracy and reliability of local obstacle avoidance.

The invention relates to an AGV adaptive path planning and reciprocal obstacle avoidance method in a complex environment, which comprises a global path planning layer, a local obstacle avoidance planning layer and a path adaptive correction layer, and is characterized in that the global path planning layer generates a global planning path position vector by improving A * and combining a JPS jump point algorithm; the local obstacle avoidance planning layer uses an improved RVO model based on a dynamic threat function to solve a local planning path position vector of the AGV through a minimum cost function; and the path adaptive correction layer ensures the coordination and consistency of global and local strategies, triggers the global path planning layer to regenerate a task global path based on the deviation between the local planning path position vector and the global planning path position vector and the obstacle density, and realizes adaptive adjustment to improve the planning efficiency and the obstacle avoidance performance. Compared with the prior art, the AGV obstacle avoidance method has the advantages of being small in model calculation amount, good in AGV obstacle avoidance real-time performance, capable of effectively dealing with high-speed dynamic obstacles and avoiding multiple AGVs from one another and the like.

The invention discloses a multi-vehicle collaborative navigation and obstacle avoidance method and system, a vehicle and electronic equipment, and relates to the technical field of automatic driving. According to the method, a first target vehicle is controlled to run according to a global planning path determined based on urban road network topology and real-time traffic flow data, and environment data and running state data collected by the first target vehicle and a second target vehicle are acquired, so that the vehicle distance and the vehicle speed of the first target vehicle and the second target vehicle are controlled; the second target vehicles are all vehicles which are dynamically networked with the first target vehicle in the communication coverage range of the first target vehicle, and when the first target vehicle travels to a specified scene, an obstacle avoidance decision is generated and executed based on the environment data, the traveling state data and a traveling demand limited in the global planning path. Therefore, the problems that information interaction based on a single road section is only suitable for synergy of vehicles in the same direction and in the same lane, data transmission content only contains basic parameters such as vehicle speed and position, and complex and changeable traffic environments are difficult to deal with are solved.

The invention discloses a path planning method and system cooperating with global planning and local obstacle avoidance, and relates to the technical field of unmanned aerial vehicle routing inspection path planing.The method comprises the steps that when an unmanned aerial vehicle flies according to a global path, when it is detected that an obstacle exists and conflicts with a current air route, local obstacle avoidance planning is switched to be executed to bypass the obstacle; after obstacle avoidance is completed, switching back to global path following; when the deviation between the flight path and the original global path exceeds a threshold value due to local obstacle avoidance detour, triggering re-planning and updating of the global path; global planning and local obstacle avoidance of the unmanned aerial vehicle in the whole voyage are mutually coordinated and seamlessly switched, so that calculation overhead caused by frequent re-planning is avoided, and smooth transition and coherent optimization of the flight path in a dynamic environment are guaranteed.

The embodiment of the invention provides an agricultural machine obstacle avoidance control method and device, equipment and a storage medium, and relates to the technical field of machine learning. The method comprises the following steps: splicing grid features corresponding to an aerial view image of an agricultural machine working environment, the speed of the agricultural machine at the current moment and pose data to generate a global state, inputting the global state into an action decision network for global planning, predicting an execution action at the next moment in an action space, and converting the execution action into an execution control signal for agricultural machine control. When the distance between the position of the agricultural machine and at least one obstacle is smaller than a preset value, a local state is generated according to the obstacle parameters and the global state, the local state is input into an action decision network for local planning, the obstacle avoidance action at the next moment is predicted in the action space, and the obstacle avoidance action is converted into an obstacle avoidance control signal for agricultural machine control. Global planning is utilized to ensure that the agricultural machinery travels along an efficient operation path, local planning is utilized to respond to obstacles in real time, and the accuracy of agricultural machinery obstacle avoidance path planning is improved.

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 discloses an unmanned aerial vehicle route global planning method based on key feature points, and relates to the field of unmanned aerial vehicle route planning, and the method comprises the steps: carrying out the hierarchical division of a three-dimensional elevation map according to the performance of an unmanned aerial vehicle; according to obstacle pixel proportions of different layers, an optimal layer is screened out; on the basis of the segmentation maps of the optimal layer and the adjacent layer and in combination with the three-dimensional geographic features, obtaining corrected key feature points; utilizing a heuristic weight calculation formula to calculate heuristic weight values of the corrected key feature points; according to a sorting result of the heuristic weight values, a plurality of corrected key feature points of the unmanned aerial vehicle route are obtained according to a connection barrier-free principle and in combination with a three-dimensional path algorithm until a target point is selected; and based on the initial point, the target point and the plurality of corrected key feature points of the unmanned aerial vehicle route, generating an optimized unmanned aerial vehicle flight path after corner optimization. According to the method, the three-dimensional feasible flight route can be efficiently generated, and the calculation time is short.

The invention discloses an intelligent logistics system based on a tilt-rotor unmanned aerial vehicle, and relates to the technical field of tilt-rotor unmanned aerial vehicle control, the system comprises an unmanned aerial vehicle end, a client and a cloud control server, and the unmanned aerial vehicle end is internally provided with an information acquisition unit and a real-time obstacle avoidance unit; an information receiving and transmitting module, a path generation module and a cluster cooperation module are arranged in the cloud monitoring server, through mutual cooperation of all the modules and units, an intelligent logistics system from the cloud to the airborne and from global planning to local obstacle avoidance is formed, and efficient, safe and stable unmanned aerial vehicle distribution can be achieved in congested cities and remote areas.