292 results about "Global planning" patented technology

The invention discloses a robot path planning and obstacle avoidance method combining an artificial potential field and reinforcement learning. The method comprises the following steps: carrying out sensor data acquisition and fusion; environment complexity evaluation is carried out, a multi-scale planning mechanism is established, and global planning and local planning are determined based on switching conditions; carrying out artificial potential field path planning based on global planning; carrying out reinforcement learning path planning based on local planning; and judging whether the target point is reached, and returning to the determined plan until the target point is reached. According to the invention, the data of the infrared sensor and the laser radar are fused, the complementary characteristics of the two sensors are fully utilized, the comprehensive perception of static and dynamic obstacles is realized, and the adaptability of the robot in different environments is improved. According to the method, through reinforcement learning, strategy optimization and uncertainty estimation, the obstacle avoidance capability of the robot in a dynamic environment is enhanced, and the local optimum problem of a traditional artificial potential field method is solved.

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 invention belongs to the technical field of robot obstacle avoidance, and particularly relates to an anti-explosion humanoid double-arm robot obstacle avoidance path planning method which comprises the following steps: acquiring a three-dimensional model of double mechanical arms; the joints, the connecting rods and the environmental obstacles of the double mechanical arms are enveloped by the spheres; the improved Bi-RRT algorithm is adopted to conduct global path planning on the double mechanical arms; whether the double-mechanical-arm global planning path collides or not is judged, if yes, the path is planned again, the master arm and the slave arm are determined, and if not, the global planning path is executed; and whether the distance between the obstacle and the joint or the connecting rod of any mechanical arm is smaller than a set safety threshold value or not is judged, if yes, an artificial potential field local obstacle avoidance mode is started, and if not, the steps are repeatedly executed till the double mechanical arms complete path planning and reach the target point. According to the invention, the obstacle avoidance of static and dynamic obstacles in a working space can be realized, and meanwhile, the collision problem existing in the local obstacle avoidance process between the two arms is solved.

The invention discloses a real-time temperature measurement and optimal scheduling method for a rail-mounted intelligent inspection robot, and the method comprises the steps: obtaining the temperature of equipment along a rail in real time through a temperature sensor on the inspection robot, analyzing the working state of the equipment along the rail, and recognizing a potential overheating risk region; establishing a multi-robot collaborative inspection task scheduling model according to the position information and the emergency degree of the potential overheating risk area; planning an inspection path for the multi-robot collaborative task scheduling data by taking the shortest time as an index, optimizing the inspection path by adopting a fusion improved ant colony algorithm and combining a TEB strategy, and obtaining a global planning path; a trajectory optimization strategy based on Minimum Snap is adopted for the global planning path, a Bezier curve is utilized to optimize the path trajectory, a multi-robot collaborative task scheduling model is combined with an auction algorithm to dynamically allocate tasks, efficient utilization of resources and priority processing of emergency tasks are ensured, the inspection efficiency and safety are improved, and the operation cost is reduced.

The invention belongs to the technical field of path planning, and particularly relates to a robot path planning method based on an RRT improved algorithm, and the method comprises the steps: obtaining environment space information needing path planning, defining parameters, and obtaining the minimum distance between a coordinate point and an obstacle; a bidirectional random tree is constructed and initialized, and sampling points are obtained through a sampling function; finding a nearest neighbor point, selecting a new node, carrying out safety distance judgment, and if not, carrying out sampling again; generating a discretization path; setting a cost function during local node expansion; performing loop iteration to generate discrete point cost, taking a minimum cost value, searching an optimal node and inserting the optimal node into the current tree; an overall path Path after local optimization is obtained; and discretizing the whole Path, setting a cost function of nodes during global planning, selecting the node with the minimum cost for reconnection, and finally obtaining a smooth and effective optimal path. According to the method, the optimality and smoothness of the path are effectively 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 discloses a path planning and trajectory optimization method for a mobile robot in a non-flat terrain, and the method comprises the following steps: S1, the mobile robot employs a laser radar to detect the surrounding environment, and an SLAM module constructs a three-dimensional map according to point cloud data and odometer data collected by the laser radar in real time; and S2, a trafficability analysis module performs terrain trafficability evaluation based on the generated three-dimensional map, and generates a trafficability layer accordingly. And S3, the global planning module performs path search in the trafficability layer to obtain an initial path of the mobile robot from the starting point to the target point. And S4, a trajectory optimization module optimizes the initial path to ensure that the obtained trajectory meets the kinematics constraint of the mobile robot. And S5, vertically mapping the optimized trajectory to the non-flat terrain surface to obtain a final path for direct tracking of the mobile robot. According to the method, it can be ensured that the mobile robot generates a feasible track conforming to kinematics constraints in the non-flat terrain.

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 a multi-agent path planning method, device and equipment, and the method comprises the steps: dividing a map according to first grids of a preset size, carrying out the global planning of all agents on the map, and generating a global path, the global path being a road section passing through a plurality of first grids from a starting point to a target point; on the basis of each path section of the global path, local planning is conducted on all agents, a local path is generated, and each path section is a road section formed by the center points of every two adjacent first grids; real-time data are obtained by monitoring changes of dynamic obstacles and dynamic targets in a map in real time, and the local path and / or the local path are / is updated and planned according to the real-time data to obtain an updated path. Potential collision risks can be quickly found, obstacle avoidance measures can be taken in time, and the safety of a multi-agent system and the smooth completion rate of tasks are improved.

The invention provides a learning-based map-free autonomous navigation obstacle avoidance method, a medium and equipment. The method comprises the following steps: carrying out map-free autonomous navigation obstacle avoidance on the mobile robot by adopting a reusable planning capability library; the multiplexing planning capability library comprises a global planning module and a local planning module; the global planning module is used for guiding a global navigation direction and generating ordered and discrete path points; the local planning module generates real-time actions based on a learned local navigation obstacle avoidance model; and controlling the mobile robot to move according to the action generated by the local planning module so as to sequentially navigate the mobile robot to each path point generated by global planning. According to the method, through a global and local combined planning architecture, an algorithm is modularized, so that the method adapts to different navigation tasks, a large amount of retraining based on a deep reinforcement learning navigation method is avoided, and the universality and interpretability of a model are improved.

The invention provides a long-distance moving path planning method and device for a lunar rover, and the method comprises the steps: carrying out the classification of topographic features based on the whole-moon typical landform features through the wide-angle camera image of a lunar investigation orbiter; dividing the to-be-planned moving path into a plurality of local passable areas based on the classified passable areas; for each local passable area, generating a digital elevation model and a digital orthoimage by using a narrow-angle camera image of a moon investigation orbiter; performing global path planning to generate a global reference path; constructing a local digital elevation model and a local digital orthoimage by using a three-dimensional lunar surface image sensed by the lunar rover in real time; and according to the local digital elevation model, the local digital orthoimage, the measured data of the lunar rover and the prior lunar soil mechanical parameters, verifying the global reference path based on the kinetic model, and outputting a global planning strategy. The method can be used for long-distance moving path planning of the lunar rover, and is high in accuracy.

The invention relates to the technical field of mechanical arm control, and discloses a mechanical arm path planning method and system.The method comprises the steps that in a simulation environment, a reward function is generated based on the current pose and the target pose of the tail end of a mechanical arm, a deep reinforcement learning network is trained based on the reward function, and a global planning path is obtained; constructing a dynamic motion primitive system by taking the global planning path as teaching; obstacle information is obtained, and an obstacle potential field model is constructed under the condition that an obstacle affects the working space of the tail end of the mechanical arm; based on the obstacle potential field model, a rejection item in the dynamic motion primitive system is constructed, and an obstacle avoidance path is generated based on the rejection item and is used for guiding the tail end of the mechanical arm to move away from the obstacle until the tail end of the mechanical arm reaches the target pose, so that the technical problem of simultaneously realizing real-time obstacle avoidance and global optimum in mechanical arm path planning is solved.

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 discloses a multi-unmanned aerial vehicle search and rescue task planning method based on ABC-DQN hierarchical optimization, and belongs to the technical field of multi-unmanned aerial vehicle cooperative search and rescue. According to the method, task planning is carried out on a search and rescue area applying multiple unmanned aerial vehicles based on an artificial bee colony algorithm and a deep Q network, and adaptive cooperative search and rescue of the multiple unmanned aerial vehicles is completed. According to the method, global planning of a search and rescue task and path planning of a local area are both constrained and optimized, scene analysis of a search and rescue area and selection of the number of unmanned aerial vehicles with different performances are firstly carried out, and the unmanned aerial vehicle of a central control node is determined; and then the central unmanned aerial vehicle completes static global region division by using an ABC algorithm, then unmanned aerial vehicles in a local region perform adaptive search and rescue path planning based on a DQN algorithm, and in a local search and rescue process, the local unmanned aerial vehicles and the central unmanned aerial vehicle perform information interaction. Once the target is determined to appear, the central unmanned aerial vehicle further reduces the global area and performs ABC algorithm division again until the search and rescue target is determined to be within the required range.

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 discloses an autonomous mapping and path planning method for a mobile robot, and the method comprises the steps: carrying out the inspection of a target structure and environment through a robot carrying radar equipment, and fusing the data of a radar, a camera and an inertial unit through an extended Kalman filter (EKF). And performing global planning according to the improved ant colony algorithm, and performing local planning and obstacle avoidance by using a fuzzy control method to obtain an optimal path. According to the method, indexes such as cost (travel length) of alternative paths and information acquisition (an unknown area capable of being observed at a certain point) are evaluated by quantifying a boundary area and an optimal view angle under a current environment condition, and uncertain factors in an execution process are comprehensively considered. In this way, feasible path configuration can be determined, and a task scheme is adjusted in time according to changes of real-time obstacles and the like, so that the intelligent level of the robot is improved, and efficient, obstacle avoidance and accurate movement are achieved.

The invention discloses a complex large environment unmanned aerial vehicle autonomous exploration method, system and device and a medium, and relates to the field of path planning and intelligent navigation, the method comprises the following steps: constructing and updating a local occupation grid map based on image information and laser radar point cloud, carrying out semantic segmentation, and establishing a topology subgraph based on a semantic segmentation result; constructing a topological map based on the updated locally occupied grid map and the topological sub-graph by adopting a space internally tangent ball segmentation mode; boundary updating and viewpoint sampling are carried out in the topological map, and global planning is carried out based on the updated boundary and viewpoints obtained through sampling; in the global planning process, whether the unmanned aerial vehicle arrives at a global planning target is determined in real time, so that the unmanned aerial vehicle can realize autonomous exploration of a complex large environment. According to the method, the problem that a cluster aircraft or ground station map is difficult to share due to huge map data volume is solved, navigation and obstacle avoidance can be carried out under the condition that only a local occupied grid map is maintained, and the updating capability of an exploration boundary is reserved.

The invention discloses a complex narrow space aviation cable double-arm co-fusion cooperation assembling method. The method comprises the steps that a workbench with a supporting aircraft body simulation part and a double-arm robot system on the upper surface is built; the robot system visually observes the installation experiment environment and carries out global planning; designing an uncertain event processing strategy, and finding a target aviation cable; carrying out a free type target aviation cable butt joint installation experiment, and completing butt joint of bundled suspended target aviation cable connecting pieces suspended on the two sides of the aircraft body simulation piece; carrying out a fixed target aviation cable plugging installation experiment, and completing the plugging of a single plug suspended at a cable perforation module and a socket located on a truss of an aircraft body simulation part; according to the invention, a bunched suspension cable butt joint installation scene, a single-bunched cable insertion installation scene, a cable perforation scene, a cable wiring scene and the like of an actual aircraft body can be simulated, and the development of an automatic cable installation technology in the aerospace field is promoted.

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 provides a path planning method and device, and the method comprises the steps: determining a map based on the environment of a target planning region, the map comprising a starting node, a target node and an obstacle; an improved A star algorithm is adopted to search from a starting node and a target node in two directions in sequence to obtain all intermediate nodes between the starting node and the target node, so that a global planning path is obtained, and all the intermediate nodes pass through all non-obstacle nodes in a planar neighborhood or a spatial neighborhood based on the previous nodes of the intermediate nodes; the path cost of each non-obstacle node is estimated by adopting a path cost function based on a safety penalty factor and a turning penalty factor; determining whether each included angle formed by each node in the global planning path and a previous node and a next node is within a set angle range or not to globally optimize the global planning path to obtain a globally optimized global planning path; and carrying out local path optimization on the globally optimized global planning path by adopting a dynamic window method.

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.