686 results about "Path plan" patented technology

The invention discloses a path planning and hierarchical cooperative control method and system for an unmanned aerial vehicle cluster. The system comprises a path planning module and a formation motion control module. The method comprises the steps that firstly, an improved RRT * algorithm is adopted by a path planning module, through a multi-strategy heuristic node expansion mechanism fusing target bias and artificial potential field guidance and comprehensively considering path length, channel volume and Z-axis height change, a center path and a three-dimensional safe channel which take into account safety and smoothness are planned for an unmanned aerial vehicle cluster; and then, based on the central path, the formation motion control module adopts a distributed model prediction control framework, designs different optimization targets for a navigator and a follower, and solves an optimal control instruction on line, so that a cluster is guided to complete trajectory tracking, collision avoidance among individuals and self-adaptive formation reconstruction in a secure channel. According to the invention, the navigation problem of the unmanned aerial vehicle cluster in a complex obstacle environment is solved, and the path planning efficiency and the robustness of cooperative control are improved.

The invention discloses a dynamic path optimization method based on response time domain attenuation, and relates to the technical field of artificial intelligence and intelligent path planning, and the method comprises the steps: generating a node network composed of navigation points, terrain units or interaction regions, and forming a node network topology structure; generating a dynamic state feature set used for describing game scene changes, and forming input data used for follow-up node priority dynamic adjustment; converting the dynamic state feature set into node-level standardized event data, and distributing the node-level standardized event data to a node state management module through an event bus; setting a current node priority for each node in a node state management module based on the node-level standardized event data; forming a time domain attenuation model of the node priority; the priority recovery coefficient is improved; in the path planning stage, executing a dynamic path search algorithm to generate a passing path with the minimum total cost and the optimal path smoothness; when it is detected that player operation or scene change causes node response mutation, a local re-planning mechanism is triggered, smooth path transition is achieved, and overall jumping is avoided. According to the method, the problems of path congestion, frequent switching and unsmoothness caused by lack of dynamic node state and event response calculation in the prior art are solved. Through node priority time domain attenuation and dynamic path optimization, the technical effects of smooth path, efficient passing and node load balancing are achieved.

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 an intelligent storage multi-AGV scheduling method and device and a medium, and relates to the technical field of automatic scheduling, and the method comprises the steps: collecting the state and environment perception data of AGVs, and carrying out the standardization processing; constructing a dynamic conflict scheduling model, inputting the standardized AGV state and environment perception data into the dynamic conflict scheduling model for task allocation and path planning, and outputting a preliminary task allocation and path planning scheme; and integrating task execution feedback data with historical task data, optimizing parameters of the dynamic conflict scheduling model, and outputting an optimized task allocation and path planning scheme. According to the method, the task execution feedback data and the historical task data are integrated, and parameter optimization is performed on the dynamic conflict scheduling model based on the scheduling knowledge graph and the meta-gradient descent strategy, so that continuous self-evolution and precision improvement of the scheduling strategy are realized.

The invention discloses an unmanned aerial vehicle autonomous flight control method and a related device. The method comprises the steps of obtaining environment map information and unmanned aerial vehicle information; based on the environmental map information and the unmanned aerial vehicle information, obtaining a global optimal path by using a fusion dynamic exploration path planning algorithm; taking each path section in the global optimal path as a center to generate a plurality of ellipsoid areas covering the path sections, converting the boundary of each ellipsoid area into a convex polyhedron formed by a plurality of hyperplanes, and connecting the convex polyhedrons in series according to the sequence of the path sections to obtain a safe flight corridor; in the trajectory optimization process, the safe flight corridor is used as a constraint basis of space feasibility, it is ensured that the trajectory is always located in the safe flight corridor, and space constraints are determined; and carrying out trajectory tracking by adopting a DLQR controller based on the spatial constraint. According to the invention, efficient cooperation of path planning and trajectory tracking is realized.

The invention discloses a green plum fruit picking robot based on multi-source data fusion and a picking method thereof, and relates to the technical field of agricultural intelligent equipment. The robot comprises a crawler-type self-adaptive chassis, a multi-degree-of-freedom mechanical arm, a multi-source sensing module, a bionic flexible end effector, an intelligent control unit and a fruit collecting box, and all the parts are electrically connected with the intelligent control unit. The multi-source sensing module synchronously collects vision, spectrum, touch, acoustics and environment data, and accurate recognition and positioning of mature fruits are achieved through hierarchical weighted attention fusion algorithm processing. And fruit damage is reduced in combination with a path planning algorithm introducing a flexible obstacle avoidance coefficient and a flexible picking execution mechanism. The self-adaptive chassis can adapt to complex terrains, and parameters are dynamically optimized through reinforcement learning in the picking process. The green plum picking machine solves the problems that traditional picking is low in efficiency and high in damage rate, and existing equipment is poor in adaptability, and the intelligent level and operation quality of green plum picking are improved.

The invention relates to the field of modern intelligent manufacturing and automation, and discloses a method for planning a path of an intelligent robot with a body based on visual inspection, which comprises the following steps: acquiring real-time image data, preprocessing and extracting position coordinates of an obstacle, and generating an obstacle distribution diagram; an A star algorithm is adopted to generate a preliminary optimal route sequence, and map information is updated; uncertain interference in the path is judged, and a path alternative set is generated through coordinate calibration; a time-varying parameter and a security constraint optimization path strategy are integrated through adaptive path optimization; key node coordinates are extracted and are fused with obstacle boundary identification to obtain a path execution sequence; real-time performance is checked, if calculation is overtime, a rollback mechanism is triggered, and an initial path is loaded and fused with real-time obstacle information to generate a final feasible route; a control instruction sequence is generated according to the final route and transmitted to the robot for execution. According to the method, efficient and adaptive path planning in a dynamic environment can be realized, and the real-time response capability and task execution reliability of the robot are improved.

The invention relates to the technical field of multi-vehicle cooperative control, and discloses a multi-AGV distributed cooperative path planning method and system, and the method comprises the steps: carrying out the task distribution based on the AGV state and position distance after receiving the order picking wave issuing; performing global path planning on the selected AGV, and determining continuous path points to be driven; the AGV dynamically blocks a front path point to form a temporary exclusive channel; detecting conflicts among the AGVs through a double conflict judgment mechanism based on timestamps and direction representation; if no conflict exists, the task is executed, and if the conflict exists, deadlock judgment is carried out firstly; if deadlock occurs, a heuristic avoidance strategy is adopted to search temporary obstacle avoidance points, and a path is re-planned through an A * algorithm in combination with a direction priority principle; and if the deadlock does not exist, directly carrying out path adjustment. According to the scheme, through dynamic blocking and double conflict judgment, waiting and collision are effectively reduced, and the passing efficiency and safety are improved; and in combination with a heuristic avoidance method based on a ternary search tree, the scheduling flexibility and intelligence of the system in a deadlock scene are enhanced.

The invention discloses an AUV (Autonomous Underwater Vehicle) path planning method based on a multi-strategy collaborative enhanced artificial mouse algorithm, and belongs to the technical field of AUV path planning. In order to solve the problems of slow convergence speed and low convergence precision of an existing path planning algorithm in the field of AUV path planning, the method comprises the following steps: carrying out path normalization by adopting an artificial travel mouse algorithm to obtain an optimal path, taking a connecting line of a starting point and an end point as a center line during initialization, adding random disturbance to a center line interpolation point to generate a path point, and carrying out path planning on the path point; generating an initial population; the path with the smaller fitness is selected as a superior path, and global exploration, hole digging or foraging / pursuer avoiding is decided according to the value of the self-adaptive nonlinear attenuation dynamic energy factor; evaluating the fitness of the individual, and if the fitness of the iteration is more excellent than the optimal fitness of the individual, updating the optimal fitness of the individual; if the fitness is more excellent than the global optimum fitness, updating the global optimum; and finally obtaining a final path based on the global optimal solution.

The invention relates to the technical field of micro-nano robots, and provides a motion control method and system for a magnetic control micro-nano robot carrying medicine in a simulation environment, and the method comprises the steps: obtaining a motion image of a magnetic control silicon dioxide-based micro-nano robot cluster in the simulation environment in real time; according to a preset transportation task, the current state of the micro-nano robot cluster is judged; based on the current state of the micro-nano robot cluster, establishing a magnetic field generated by the micro-nano robot cluster and the three-dimensional Helmholtz coil, a fluid resistance field of a simulation environment and a dynamic model of a simulation environment channel boundary condition; and based on the motion image acquired in real time, using a deep learning model to identify real-time pose information of the micro-nano robot cluster and the simulated environment channel boundary information, and using a path planning algorithm to perform trajectory planning according to the real-time pose information and the simulated environment channel boundary information. And the magnetic field is adjusted to control the movement of the micro-nano robot cluster.

The invention relates to a robot path planning method, electronic equipment and a medium, and belongs to the technical field of intelligent robos.The method comprises the steps that an obtained static cost map and an obtained dynamic cost map are combined to obtain a global cost map; performing global path planning on the global cost map to obtain a global optimal path; performing local path planning based on the global optimal path to obtain a local optimal track, and controlling the robot to execute the local optimal track until a global target point is reached; and determining the dynamic cost of the local optimal trajectory, updating the dynamic cost map, further updating the global cost map, and performing path planning again. According to the method, the dynamic cost of executing the local optimal track by the robot is updated into the dynamic cost map, so that the robot can actively avoid a historical difficult passing area in the path planning process, and the navigation stability and long-term operation efficiency of the robot in a complex and narrow environment are improved.

The invention discloses an unmanned aerial vehicle cluster task planning method and system based on hierarchical reinforcement learning, and the method comprises the steps: constructing a hierarchical decoupling model, decomposing an end-to-end complex decision problem which is difficult to solve into a task distribution layer and a path planning layer, and decomposing an unmanned aerial vehicle cluster task planning problem into different levels for optimization solving, a task allocation scheme and a path planning scheme are respectively obtained, a path planning layer accurately executes a time coordination instruction issued by an upper layer while ensuring flight safety, the coordination efficiency is better when an unmanned aerial vehicle group cooperatively processes a task, and a path planning coordination mechanism based on a leading architecture can be expanded to a larger number of unmanned aerial vehicles, so that the unmanned aerial vehicle group can be expanded to a larger number of unmanned aerial vehicles. And the method can better adapt to the requirements of high-dynamic and high-uncertainty complex environments.

The invention discloses a cable tunnel autonomous obstacle avoidance inspection method, system and equipment based on multi-sensor fusion and a medium, and relates to the technical field of data processing, and the method comprises the following specific steps: correcting motion data collected by an inspection robot, carrying out pre-integration calculation according to the corrected motion data to obtain motion change data, and carrying out data processing on the motion change data; and predicting a frame state vector of the robot at the current moment, carrying out laser vision tight coupling optimization of the inspection robot, carrying out state prediction on a dynamic obstacle, carrying out collision risk assessment in combination with the frame state vector of the inspection robot, and establishing a path planning objective function to carry out real-time path re-planning. According to the method, the problem that the convergence speed and the real-time performance of a traditional algorithm are difficult to balance in a dynamic complex scene is solved through laser and vision tight coupling optimization. Through dynamic obstacle prediction, probability risk assessment and real-time path planning, an autonomous inspection system with active obstacle avoidance and real-time path generation capabilities is established.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle path planning method based on deep reinforcement learning. The method comprises the following steps: constructing an unmanned aerial vehicle-missile confrontation environment model for providing reward calculation, termination judgment and a visual interface so as to provide training data and evaluate the path planning performance of a reinforcement learning strategy; an Actor-Critic deep neural network model based on strategy gradient is constructed, the unmanned aerial vehicle state information is taken as input, the maneuvering acceleration is taken as output, and adaptive learning of the unmanned aerial vehicle maneuvering strategy is realized; in the reinforcement learning process, mechanisms such as dominant function normalization, generalized dominant estimation and gradient cutting are introduced, and the training stability and convergence speed are improved; by adopting a layered reward mechanism and an improved PPO algorithm, the method effectively solves the problems of difficulty in training convergence, poor strategy stability and low energy utilization rate of a traditional deep reinforcement learning method in a complex air combat environment, realizes autonomous decision making and optimal avoidance path planning of the unmanned aerial vehicle under a high confrontation condition, and improves the unmanned aerial vehicle experience. And the avoidance success rate and the training efficiency are obviously improved.

The invention relates to the technical field of intelligent traffic and smart city waste management, and discloses a path planning method and system for a garbage transport vehicle based on multi-source data, and the method comprises the steps: generating a reference path based on historical collection and transportation data, the method comprises the following steps: performing data cleaning on pre-acquired garbage quantity data, vehicle state data, real-time traffic data, hydroelectric data and fuel consumption data, performing multi-source time sequence feature fusion anomaly detection on a cleaned data set, performing trajectory inversion on the cleaned data set, performing target constraint through a mixed integer programming model, and performing multi-source time sequence feature fusion anomaly detection on the cleaned data set. Then gradient descent parameter optimization is carried out on the initial optimization model, elastic resource scheduling is carried out on an optimized path scheme based on a large event plan library, a path instruction set is generated, dynamic re-optimization evaluation is carried out on the path instruction set based on garbage amount change and traffic condition data fed back in real time, and an updated path is generated; according to the invention, the path planning efficiency of the garbage transport vehicle based on the multi-source data can be improved.

Provided are a method, system, and non-transitory computer-readable medium for real-time autonomous path planning for a robotic device. The method includes receiving data about the robotic device in a three-dimensional workspace, encapsulating objects in the environment of the robotic device including the robotic device, a target, and one or more obstacles in simulated robotic space, calculating a direction of movement for the robotic device according to three virtual forces including a virtual attractive force, a virtual repulsive force, and a virtual tangential force acting at least partially perpendicularly relative to the virtual repulsive force, mapping each virtual force in the three-dimensional workspace into torque vectors in the simulated robotic space at each joint, converting a sum of the torque vectors into one or more commands for the robotic device defining a path to reach the target, and sending the one or more commands to the robotic device.

The invention discloses an electric field inspection unmanned aerial vehicle path planning method, and belongs to the technical field of electric field inspection, and the method comprises the following steps: collecting multi-dimensional environment data and task demand data for an electric field inspection scene, forming a path planning basic data set, carrying out the preprocessing and modeling of the basic data set, and obtaining a path planning result; the method comprises the following steps: constructing an electric field inspection environment grid model and quantifying a path planning constraint rule, designing a dynamic path planning algorithm adaptive to an electric field scene based on the grid model and the constraint rule, generating an initial optimal path, and converting the dynamic path planning algorithm into an operable path planning system. And a data processing, path calculation, real-time adjustment and result output function module is developed. According to the electric field inspection unmanned aerial vehicle path planning method, a power side-load side core inspection path is locked through a power flow tracking algorithm, searching is only carried out in the core path and two adjacent grids, non-key branch lines are excluded, and compared with traditional full-scene searching, the calculation space is reduced by 60% or above.

The invention relates to the technical field of path planning, in particular to a path planning method and device for a construction site, computer equipment and a storage medium, and the path planning method comprises the steps: constructing a static three-dimensional model according to a building model and topographic data of the construction site; acquiring obstacle data in the construction site in real time, and mapping the obstacle data into the static three-dimensional model to obtain a dynamic three-dimensional model; establishing a path search graph model according to the dynamic three-dimensional model; performing path planning through the path search graph model and a preset ant colony algorithm; in the path planning process, the probability of selecting the next path is determined by the pheromone concentration, the heuristic factor and the straight line guide factor at the same time. According to the embodiment, the transportation path can be dynamically adjusted according to the construction environment, so that the construction site transportation efficiency is improved.

The invention discloses an inspection robot path planning method and system based on environmental perception, and relates to the technical field of path planning, and the method comprises the steps: traversing an inspection target region to determine a plurality of environmental perception point locations; obtaining a plurality of environmental perception data sets; performing data fusion processing on the plurality of environment perception data sets to generate a three-dimensional environment model; determining an initial planning path of the inspection robot; performing environment adaptability analysis on the initial planning path, and determining a target planning path; and controlling the inspection robot to execute an inspection task in the inspection target area according to the target planning path, and feeding back inspection state data in real time. According to the method and the device, the technical problems of low task execution efficiency and poor reliability of the inspection robot caused by lack of dynamic optimization of the inspection robot path planning in the prior art are solved, and the technical effects of realizing accurate planning and dynamic environment adaptation of the inspection robot path and improving the task execution efficiency and reliability of the inspection robot are achieved.

The invention discloses an active obstacle avoidance type following method and device for a robot, relates to the technical field of robot control, and mainly aims to avoid walking interruption of the robot due to missing of a target person. According to the main technical scheme, continuous multi-frame environment images are acquired; according to the position change data of the target person in the multi-frame environment image, utilizing a Kalman filtering algorithm to predict a to-be-marched path of the target person; determining a pedestrian shielding source for shielding the target person according to the relative position relationship between other pedestrians and the target person in the continuous multi-frame environment image and the to-be-marched path; determining a first shielding risk area according to the to-be-marched path and the spatial position information of the static obstacle; determining a second shielding risk area based on the pedestrian shielding source; planning an obstacle avoidance path of the robot by using a path planning algorithm according to the first shielding risk area, the second shielding risk area and the to-be-marched path; and controlling the robot to move according to the obstacle avoidance path so as to realize active obstacle avoidance type following of the target person.

The invention provides an autonomous cleaning control method of a photovoltaic panel intelligent cleaning robot, and relates to the technical field of automatic control. The autonomous cleaning control method of the photovoltaic panel intelligent cleaning robot comprises the following steps of S1, based on a photovoltaic panel surface environment, S2, based on an original sensor data set, S3, based on a smudginess index distribution diagram, S4, based on a cleaning demand prediction model, S5, based on an optimized cleaning path plan, S6, based on cleaning execution state feedback, and S7, based on an adjusted cleaning parameter set. Surface images and temperature data of the photovoltaic panel are collected through visual and infrared sensors, noise filtering and standardization processing are carried out, data quality and consistency are improved, reliable input is provided for follow-up analysis, a multi-sensor fusion technology is combined with fuzzy logic control to process environmental uncertainty, unified smudginess evaluation indexes are generated, detection accuracy is enhanced, and the method is suitable for large-scale popularization and application. And misjudgment caused by limitation of a single sensor in a traditional method is avoided.

The specification discloses an image recognition-based robot charging method, system and electronic equipment, which has the advantages of low cost, high precision, wide recognition range, etc. The method comprises: acquiring a work scene image by using a monocular camera on the robot, and extracting a charging pile image from the work scene image; performing image recognition on the charging pile image to obtain position information of a feature icon on the charging pile in the charging pile image; judging a positional relationship between the robot and the charging pile according to the position information; performing path planning according to the positional relationship, generating charging path parameters, and controlling the robot to move to the charging pile for charging based on the charging path parameters. The system comprises: a charging pile image extraction module, a feature icon positioning module, a robot positioning module, and a path planning control module. The processor in the electronic equipment implements the method when executing a program.

The embodiments of the present disclosure relate to the field of intelligent warehousing. Provided are a path planning method, apparatus and system. The method comprises: a first device receiving an autonomous navigation area sent by a second device; determining whether an initial path is within the autonomous navigation area, the initial path being a path planned by the second device for the first device to execute a first movement task; and when the initial path is within the autonomous navigation area, enabling an autonomous navigation function and executing autonomous navigation, wherein the end position of the autonomous navigation is the destination of the first movement task.

The embodiment of the invention discloses a hoisting path dynamic planning-based double-truck-crane cooperative hoisting method, device and equipment and a storage medium, and relates to the technical field of truck-crane cooperative hoisting. According to the method, a hoisting task model and environment information of a hoisting scene are combined, the motion path of double truck-cranes is optimized through a path planning algorithm, and the hoisting path dynamic planning-based double-truck-crane cooperative hoisting efficiency is improved. And stability evaluation and correction are carried out on the path points. And the central control system generates a cooperative control instruction according to the corrected motion path, coordinates the rotation angle, the variable amplitude angle and the lifting height of the truck crane in real time, and ensures stable lifting of the component pose. Therefore, the precision and the safety of collaborative operation of the double truck cranes are effectively improved, the stability problem possibly occurring in the hoisting process can be responded in real time, and the stability and the high efficiency of the operation process are ensured.

The invention relates to the technical field of multi-unmanned aerial vehicle cooperative edge computing unloading, and discloses a multi-unmanned aerial vehicle task unloading and path planning joint optimization method which comprises the following steps: 1) in a ground equipment-unmanned aerial vehicle cooperative system, dividing tasks into high / low priorities according to time efficiency, calculating local processing time delay, splitting according to needs, and matching initial service unmanned aerial vehicles in a communication range; 2) constructing a communication model, a flight model and a calculation model, and respectively describing equipment transmission, unmanned aerial vehicle movement and task processing characteristics; 3) in combination with constraints such as task priority and computing power load, establishing a dual-agent optimization objective function of ground equipment (unloading decision) and unmanned aerial vehicle (path planning); 4) adopting an improved SAC algorithm fused with an aging priority item, and obtaining a joint optimal scheme of task unloading and path planning through double-agent dynamic interaction iteration; and 5) an unloading instruction is issued according to the scheme, path planning and task processing are executed, and the optimal effect of cooperation of multiple unmanned aerial vehicles is achieved. According to the method, the problems of poor unloading and path adaptability and insufficient timeliness guarantee in a traditional scheme are solved, the high-priority task time delay can be reduced, the multi-unmanned aerial vehicle cooperation efficiency is improved, and the method is suitable for emergency rescue and other timeliness sensitive scenes.

The invention discloses an unmanned aerial vehicle adaptive path planning method based on risk triggering and local optimization. The method comprises the following steps: (1) establishing an unmanned aerial vehicle motion and obstacle environment model; (2) constructing an enhanced artificial test field and calculating resultant force; (3) calculating a risk index and generating a trigger signal; (4) executing PSO local re-planning during triggering; and (5) updating the position of the unmanned aerial vehicle and outputting a planned path. The invention provides a PSO-APF hybrid single unmanned aerial vehicle path planning method based on event triggering, and the method achieves the real-time, safe, smooth and efficient path planning in an obstacle environment through the construction of an enhanced artificial potential field model, a risk index and event triggering mechanism, a self-adaptive local re-planning window and a PSO optimization module. And a closed-loop process of potential field guidance, risk assessment, event triggering, local optimization and path updating is formed.

The invention relates to the technical field of path planning, in particular to a priority-based multi-agent path planning method and system, and the method comprises the steps: abstracting a target scene into an undirected graph; setting a set of agents participating in path planning in the target scene; setting a starting point and an ending point of each agent; defining a priority, a calculation formula of the priority, an initialization rule of a definition and an updating rule in a subsequent path planning step for each agent, wherein the corridor priority represents the defined agent, and the movement priority represents the defined agent; and performing path planning on the agents in the set based on the graph, the starting point and the ending point of each agent, the priority and the calculation formula of the priority. The method is used for improving the quality of the solution generated in the map with the corridor structure.

The invention discloses a ship dynamic path planning method and system based on an improved DRRT algorithm, and relates to the technical field of ship automatic sailing, and the method comprises the steps: obtaining the two-dimensional plane environment parameters of a ship sailing sea area, determining the boundary range of the sea area, collecting the position and contour data of a static obstacle, and collecting the state information of a dynamic obstacle in real time, determining a starting point and a target point of ship path planning; an improved DRRT algorithm is adopted, sampling points are determined through target and path point bias sampling, the extended step length is determined through step length adaptive adjustment, a path node set is determined through path node trimming and reconnection, and an initial path is generated through cubic B-spline smoothing; obstacles are monitored, whether part of path results are invalid or not is judged, if the failure phenomenon exists, an improved DRRT algorithm is adopted for re-planning, and a dynamic path is obtained. According to the invention, the comprehensive requirements of the real-time performance, the safety and the path quality of the path planning in the intelligent navigation process of the ship can be met.

The application relates to a robot path planning method, an electronic device and a medium, and belongs to the technical field of intelligent robots, wherein the method comprises the following steps: merging an acquired static cost map and a dynamic cost map to obtain a global cost map; performing global path planning on the global cost map to obtain a global optimal path; performing local path planning based on the global optimal path to obtain a local optimal trajectory, controlling the robot to execute the local optimal trajectory until a global target point is reached; determining the dynamic cost of the local optimal trajectory, updating the dynamic cost map, and further updating the global cost map to re-perform path planning. The dynamic cost of the robot executing the local optimal trajectory is updated into the dynamic cost map, so that the robot can actively avoid a historical difficult-to-pass area in the path planning process, and the navigation stability and long-term operation efficiency of the robot in a complex and narrow environment are improved.

The invention provides a global path planning method and system for a wheel-foot robot in multiple motion modes, and relates to the technical field of robot path planning, and the method comprises the steps: obtaining multi-source data of the wheel-foot robot; calculating actual motion cost values of the wheel-foot robot in various motion modes in different terrains; constructing a deep learning network model; outputting a motion cost prediction value through a deep learning network model; constructing a loss function, and optimizing the deep learning network model by taking the minimum function value of the loss function as a target; sampling the global map through a path planning algorithm to generate an undirected graph; through the optimized deep learning network model, motion cost prediction values of all sampling points in the undirected graph in different motion modes are output; determining a plurality of middle sampling points through a heuristic search algorithm; and connecting the initial sampling point, each middle sampling point and the target sampling point in sequence to obtain a global path of the wheel-foot robot in various motion modes.