123 results about "Safe path" patented technology

The invention relates to the technical field of machine vision and perception, in particular to a ton bag lifting unmanned control system based on a binocular vision camera and a laser radar, which comprises an intelligent control unit, a lifting appliance executing mechanism, a sensing unit and a special ton bag, the sensing unit comprises a binocular vision camera and a laser radar and is used for collecting depth vision and three-dimensional point cloud information of an operation area; the intelligent control unit fuses multi-source data, locates a lifting lug by improving a weighted multi-feature fusion algorithm, plans a safety path and generates a staged instruction; the lifting appliance executing mechanism lifts and pulls a collapsed lifting lug through an electromagnetic adsorption module, a mechanical gripper module clamps the lifting lug, and reliable operation is achieved in cooperation with a verification mechanism; the special ton bag is matched with a sensing and executing module through a high-contrast color and a pre-embedded metal piece. The full-process unmanned operation is achieved, the robustness and operation safety of the complex environment are improved, and the ton bag hoisting requirements of multiple industries are met.

The invention relates to an unmanned aerial vehicle cluster safety path planning reinforcement learning method and device. The method comprises the following steps: inputting unmanned aerial vehicle group states, task targets and environment observation information into a deep reinforcement learning strategy of a continuous action space, and generating a global guidance quantity; an explicit security constraint set between the unmanned aerial vehicles and between the unmanned aerial vehicles and obstacles is constructed, through graph structure modeling and a GNN adaptive mechanism, the unmanned aerial vehicles / obstacles are represented by dynamic graphs, and neighborhood and relative position speed information is utilized to predict CBF parameters; and performing safety projection correction on the guide quantity based on the constraint and the CBF parameter to obtain an actual control instruction corresponding to the safety path. After the instruction is executed, feedback information such as state deviation and constraint tensity is collected and used for adjusting a reinforcement learning strategy, loop iteration is carried out to continuously output an optimization path, and whole-course collision-free planning of the cluster is achieved. By adopting the method, stable and reliable safety protection in a complex dynamic environment can be realized.

The invention relates to the technical field of automatic driving and artificial intelligence, in particular to a driving track planning method based on a visual language model and an intelligent driving system. The trajectory planning method comprises the steps of multi-modal data acquisition and preprocessing, visual language model reasoning, execution control and the like. The intelligent driving system comprises a multi-modal data acquisition and preprocessing module, a visual language model reasoning module and an execution control module. The visual language model reasoning module is integrated with a Patch selection module, a token compression module and a speculation decoding module; according to the invention, an end-to-end driving planning reasoning framework is constructed based on the visual language model, the vehicle trajectory planning is directly generated from the original multi-view perception data and the high-level driving intention by a single model, the architecture of an automatic driving planning system is simplified, and the response speed and robustness are improved. The method can effectively meet the requirement of the automatic driving vehicle for generating the safe path in real time in a vehicle-mounted embedded environment, and has important practical value.

The invention discloses an intelligent robot navigation system with a body based on a multi-modal large model, and the system comprises an RGB-D camera module which captures a color image and a depth image of an environment in real time, and carries out the preprocessing of the color image and the depth image, and outputs the preprocessed image; the laser radar SLAM module provides a basis for subsequent coordinate calculation and navigation path planning; the multi-modal VLM module is used for calculating the three-dimensional coordinates of the target location in the map; and the navigation control module controls the robot to move. The beneficial effects of the invention lie in that the system realizes efficient environmental perception and natural language understanding through multi-modal fusion, and can accurately identify a target location, calculate a three-dimensional coordinate and plan a safe path, thereby improving the autonomous navigation precision and reliability of the robot, and being suitable for intelligent movement control in a complex scene.

Data is the “fuel” that powers the machine learning “engine” for Artificial Intelligence. However, identifying high quality data that can catalyze smarter AI, AGI, and SuperIntelligent systems is becoming an increasingly challenging bottleneck for machine learning. This invention not only describes novel methods for identifying the most valuable data, but it also presents an entirely new framework for understanding the information content of AI-relevant datasets. The methods can be used by intelligent systems autonomously or in collaboration with humans. Novel methods for accelerating AI learning, and for updating the knowledge of AI systems in real-time, are also disclosed. Consistent with the view that human survival may depend on the fastest path to AGI also being the safest path, the invention describes catalysts which help maximize alignment between the values of AGI and humans. These innovative catalysts increase not only the intelligence, but also the safety, of AI systems.

According to the motion planning method and device for the multi-mechanical-arm cooperative task, a large number of potential conflicts are actively avoided in the early stage of task allocation through division of a virtual wall and a safety area and a danger area, the difficulty and the calculated amount of follow-up track collaboration are remarkably reduced, and the success rate and the efficiency of planning are improved. Cost and load balance are considered in the task allocation stage; in the path point sorting stage, minimizing the total movement distance and the total completion time is taken as a target; and track generation adopts a time optimal algorithm. The layered decoupling design avoids the huge calculation overhead of centralized planning, and meanwhile, the quality of a final solution is ensured through the optimization strategy of each stage. The innovative cost function takes collision distance into consideration, and guides the planner to select a safer path. Through layered and ordered collision solution strategies, such as deceleration, local re-planning and optimal waiting, it is ensured that a collision-free solution can be always found in a complex dynamic environment, and robustness is high.

The invention discloses a multi-degree-of-freedom mechanical arm obstacle avoidance path planning method based on industrial vision, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: collecting and preprocessing an initial environment image of a current working scene of a mechanical arm, obtaining a standardized working scene image data set, carrying out dynamic object detection, and obtaining an environment perception evaluation report; identifying an unobserved area of the working scene based on the environment perception evaluation report, performing collision path calculation, and generating a visual angle adjustment action instruction; the safety path sequence is issued to a mechanical arm joint and executed, images in front of the mechanical arm are continuously collected during execution, and if an unpredicted sudden obstacle is detected, the four-dimensional risk map is dynamically updated, and path re-planning is conducted; and when the tail end of the mechanical arm successfully completes the task, obstacle avoidance path planning data of the mechanical arm are recorded and stored persistently, and an obstacle avoidance path planning record is generated. According to the method, the real-time problem of path planning of the mechanical arm is solved through construction of the four-dimensional dynamic risk map.

The invention discloses a breast surgery puncture positioning system, which relates to the technical field of image positioning, and is characterized in that a preoperative medical image and an intraoperative ultrasonic image of a patient are acquired to generate a three-dimensional model, an optimal puncture path is planned by receiving a needle insertion point of a user, and the spatial pose of a puncture needle is tracked; the optimal puncture path and the position and posture of the puncture needle are displayed on the real-time ultrasonic image in an overlapped mode, real-time deviation is calculated, the puncture needle is guided and adjusted to the target focus, and real-time compensation is carried out based on the respiratory signal and / or the displacement of the reference datum. Through the multi-modal image fusion and real-time dynamic tracking technology, the puncture precision and hit rate are remarkably improved, meanwhile, the system intelligently plans a safe path and provides an AR navigation interface, the operation difficulty and dependence on doctor experience are greatly reduced, physiological displacement interference is overcome through respiratory gating and a motion compensation mechanism, and the system has the advantages of being high in accuracy and high in reliability. Accuracy and reliability in the whole operation process are guaranteed, and finally standardized and safe mammary gland puncture diagnosis and treatment operation is achieved.

The invention relates to the technical field of mobile robot autonomous navigation path planning, and discloses a complex orchard scene-oriented hybrid sampling global path planning method, which comprises the steps of adopting a Gaussian hybrid sampling strategy, switching between global uniform sampling and Gaussian offset sampling through a probability threshold, and considering global exploration and key area guidance; a dynamic node expansion strategy is adopted, the expansion step length is adaptively adjusted according to the distance between the expansion step length and a target point, and the expansion direction is optimized by fusing vectors facing a sampling point and the target point and introducing an obstacle influence factor; a layered collision detection strategy is adopted, and step-by-step filtering is carried out through rough detection based on a quadtree, secondary screening based on an axis alignment bounding box and accurate geometric judgment in sequence so as to improve the efficiency; and a dynamic iteration termination mechanism is adopted, and the minimum number of iterations and the path quality convergence state are combined as composite termination conditions. The method can quickly generate a smooth and safe path, and is especially suitable for structured scenes such as orchards.

A deep neural network(s) (DNN) may be used to perform panoptic segmentation by performing pixel-level class and instance segmentation of a scene using a single pass of the DNN. Generally, one or more images and / or other sensor data may be stitched together, stacked, and / or combined, and fed into a DNN that includes a common trunk and several heads that predict different outputs. The DNN may include a class confidence head that predicts a confidence map representing pixels that belong to particular classes, an instance regression head that predicts object instance data for detected objects, an instance clustering head that predicts a confidence map of pixels that belong to particular instances, and / or a depth head that predicts range values. These outputs may be decoded to identify bounding shapes, class labels, instance labels, and / or range values for detected objects, and used to enable safe path planning and control of an autonomous vehicle.

The invention discloses a multi-AUV safe path planning method based on deep reinforcement learning. According to the method, a deep reinforcement learning algorithm model based on an MATD3 method is adopted, a policy network and a value network are updated by adopting security constraints, the security of policy learning is improved, the expected reward revenue is maximized under the condition that the expected security cost constraints are met, a frequent minimum and maximum optimization process is avoided by adopting first-order penalty optimization, and the security of policy learning is improved. Meanwhile, safety correction based on a safety layer is added in the training process to guarantee safety in the early stage of training, and exploratory and safety balance is brought to strategy optimization of reinforcement learning through safety constraint and safety correction. According to the multi-AUV path planning method, the time cooperation constraint and the space cooperation constraint of multi-AUV path planning are comprehensively considered, the centralized training and decentralized decision-making architecture is applied to multi-AUV path planning, the path planning method capable of ensuring cooperation safety is provided for a multi-AUV system, and the safety and the reliability of path planning are improved.

The invention relates to the technical field of intelligent control of fire-fighting robots, and discloses a fire-fighting robot capable of autonomously moving and automatically detecting and extinguishing fire and a control method. The method comprises the following steps: acquiring original detection data including a visible light image, a thermal infrared image, a laser point cloud and a sound signal; after feature extraction is carried out on the data sources, mapping the data sources to a unified environment three-dimensional coordinate system according to a preset space alignment rule, and generating fusion environment data; a fire source is identified and positioned based on the data, and the three-dimensional coordinate, the combustion intensity and the expansion rate of the fire source are obtained; in combination with fire source information and obstacle contours, performing global path planning by adopting an improved fast exploration random tree algorithm fused with a fire scene safety evaluation index; and finally, according to the planned path, the safety index and the state of the robot, a task execution instruction sequence containing a moving and fire extinguishing strategy is generated. According to the invention, the precision and consistency of multi-modal sensing are improved, and safe path planning under the dynamic fire scene risk is realized.

The invention relates to the technical field of logistics, in particular to an RGV dolly avoidance system and method.The RGV dolly avoidance method is characterized in that a multi-mode sensing module integrates data of a laser radar, a camera and a millimeter wave radar to form an environment sensing report, and an intelligent decision-making module rapidly assesses threats and generates an avoidance strategy based on a deep reinforcement learning algorithm; the dynamic path planning module adopts an improved ant colony algorithm and plans a safe path in combination with obstacle parameters and kinematic characteristics of a trolley, the precise control module drives a steering system to execute the path through a motion control algorithm, and the redundant communication module adopts wireless / wired multi-link to guarantee real-time communication, map the running state in real time and early warn faults. Data support is provided for system optimization, and all the modules cooperate to achieve the functions of environment perception, decision planning, accurate control and state monitoring.

The invention discloses a railway maintenance field operation protection card control method and device and a medium, and relates to the technical field of rail transit equipment, and the method comprises the steps: carrying out the spatial diffusion of a comprehensive risk index value, generating a dynamic risk field, carrying out the field intensity gradient vectorization and path integration of the dynamic risk field, and generating a safety path coordinate sequence; performing spatio-temporal trajectory matching on the safe path coordinate sequence to obtain a path execution deviation degree, performing spatio-temporal causal analysis on the path execution deviation degree, and constructing an event causal diagram; and obtaining the violation contribution degree of the event causal graph through anti-fact query, and carrying out strategic compiling on the basic permission rule base according to the violation contribution degree to generate an incremental control strategy and a visual decision report. According to the method, the dynamic risk field is generated and the event causal graph is constructed, so that the risk root is accurately positioned, and the rule dynamic adaptability and the decision interpretability are enhanced.

The invention provides a safety partition and safety path control method and system for a series press line, and belongs to the technical field of press control. The method comprises the steps that a mode selection instruction is received, a whole line operation mode is recognized, and safety partition configuration is generated; collecting safety signals and moving trajectory data in real time, and establishing a whole line safety control model and a stamping unit safety sub-model in a bus safety controller; calling a whole-line safety control model and a safety sub-model to compare and evaluate a safety state in real time, and triggering safety operation according to a whole-line operation mode when the safety state is abnormal; in the teaching or running process of the carrying path, a safety motion state machine is established for the carrying equipment in the activated partition, and safety control is conducted on the carrying path; and when a person needs to enter the line body, responding to a maintenance request, automatically planning a fault maintenance small partition, executing energy locking and activating a safety scanner to dynamically monitor the position of the person according to a safety partition configuration mode. According to the invention, accurate matching of the security resources and the production requirements is realized.

The invention relates to the technical field of computer vision and intelligent navigation, in particular to an intelligent obstacle avoidance method, system and device based on monocular pure vision. The method comprises the following steps: generating a global cost map containing all static obstacles based on a processed original image sequence; generating a global path capable of bypassing all static obstacles according to the global cost map; when the user advances according to the global path, generating a local cost map containing dynamic obstacles based on the processed current image sequence; fusing corresponding areas in the local cost map and the global cost map to obtain a final cost map; according to the final cost map and the global path, generating a target path which accords with the direction of the global path and bypasses the dynamic obstacle; and converting the target path into a navigation instruction and feeding back the navigation instruction to the user to guide the user to safely arrive at the destination position. According to the invention, efficient and safe path planning is realized while low cost is ensured, and the travel safety of a user is ensured in a complex environment.

The invention provides a parking lot space management and control method and system, and relates to the technical field of intelligent transportation, and the method comprises the steps: inputting calibrated parking space state mapping data, a final parking space guiding path scheme and updated special parking space state data into a space-time prediction algorithm, and carrying out the multi-source data fusion and parking space demand trend analysis, a parking space demand prediction map is obtained; based on the map, carrying out dynamic division and parking space resource allocation on a parking lot area to obtain a dynamic area allocation strategy; and based on a dynamic area distribution strategy, carrying out data synchronization on continuously updated parking space state data and a payment processing process, automatically completing payment verification when the vehicle leaves the parking lot, releasing path planning resources after the verification is passed, and updating the parking space state database. According to the invention, through closed-loop management of multi-technology fusion, accurate sensing of parking space states, safe path guidance, dynamic optimization of resources and efficient billing of departure are realized.

The invention relates to a self-adaptive safe path planning method for a gas inspection robot, and belongs to the technical field of robot path planning. According to the method, firstly, a global path is generated by improving an A star algorithm, smoothness is optimized, then a multi-sensor data sensing environment is fused, local real-time obstacle avoidance planning is carried out by adopting a dynamic window method, and a deep reinforcement learning model is introduced to realize dynamic decision making. According to the method, the problems that in the prior art, in a dynamic complex environment, path planning is rigid, obstacle avoidance is not timely, and high-risk areas cannot be adaptively avoided are effectively solved, the efficiency and safety of path planning can be remarkably improved, and the autonomous operation capacity and the intelligent decision-making level of the robot in the underground complex environment are enhanced.

The invention discloses a DEM map dynamic obstacle avoidance path planning method and system based on a rime algorithm, DEM terrain modeling, rime algorithm meteorological prediction and an improved A * path planning algorithm are combined, safe path planning in a dynamic environment is achieved, the method and system can be applied to unmanned aerial vehicle inspection (such as power line icing monitoring), mountain rescue, automatic driving and other scenes, and the method and system have good application prospects. The problems that a traditional A * algorithm is limited, multi-constraint fusion is difficult, pure real-time meteorological data lags behind and the like are solved, and a safer and more reliable path planning navigation scheme is formed.

The invention relates to the technical field of production emergency facility control, in particular to a production emergency facility intelligent control method and system based on edge computing, and the method comprises the steps: building a polar coordinate system monitoring region when a water immersion signal is monitored; the edge gateway collects real-time environment data through a water level sensor array, carries out space-time correlation analysis on the real-time environment data to establish an immersion area diffusion model, and predicts a predicted boundary trajectory of an immersion area through a polar coordinate system according to the diffusion speed and the diffusion direction of the immersion front calculated by the immersion area diffusion model. Determining an affected navigation path area; according to the predicted boundary trajectory and the affected navigation path area, planning a dynamic safety path for the automated guided vehicle in the threat area, packaging the dynamic safety path into a navigation instruction set, and sending the navigation instruction set to an automated guided vehicle scheduling system; the safe operation of the automatic guided vehicle in the sudden water immersion emergency scene is ensured, and the reliability of production emergency processing is improved.

This invention provides a real-time path generation method and system for robot corner turns, which ensures safety from objects approaching from the opposite side of a corner by generating a path that makes a large turn around a corner while taking real-time obstacle information into consideration. [Solution] A method for generating a real-time path for a robot to turn corners involves generating a cost map of the robot's surroundings based on a grid map and information on obstacles detected by the sensor unit, generating the shortest possible avoidance path from the robot's current position to the destination while avoiding obstacles, detecting an obstruction area at the robot's current position which is an area not detected by the sensor unit to detect the corner entry point, generating a turning area securing path based on the shortest avoidance path, determining weight values ​​for the shortest avoidance path and the turning area securing path, generating an interpolated path based on the shortest avoidance path, the turning area securing path, and the weight values, and generating the final path based on the shortest avoidance path and the interpolated path.

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

The invention discloses an outdoor heavy-load AGV navigation control system and a control method thereof, and aims to solve the problems of low navigation precision and poor control stability of an existing outdoor heavy-load AGV in a complex environment. The system comprises a hardware system and a control method. The hardware system comprises a navigation box, a domain controller, a sensor assembly, an execution and interface assembly, a power supply assembly, a communication assembly, a man-machine interaction and monitoring assembly and an electrical control assembly; the control method comprises the following steps: 1) the environment sensing module based on multi-sensor fusion adopts a Kalman filtering algorithm to construct a high-precision map and detect obstacles; the path planning module is used for generating and optimizing a safe path in real time in combination with an improved A * algorithm; 3) the motion control module integrates a PID control algorithm and a fuzzy control algorithm to realize accurate tracking; 4) the system monitoring module monitors the state of the AGV in real time; and the navigation precision, the operation stability and the safety of the outdoor heavy-load AGV in a complex dynamic environment are remarkably improved.

The invention relates to the technical field of path planning, and particularly discloses a multi-dumper cooperative trajectory planning method and system based on process decoupling, and the method comprises the steps: carrying out the priority division and dynamic distribution of unloading points according to the spatial positions and operation states of the unloading points; constructing a multi-layer annular potential field model of the operation area based on a potential field method, and generating potential field distribution with a boundary guiding effect; on the basis of an improved Hybrid A * algorithm, a steering penalty term and a potential field weight term are combined, and a global reference path meeting vehicle kinematics constraints is generated; generating a final speed planning model based on an S-type speed planning algorithm; multi-vehicle conflict detection is carried out through the space-time envelope, potential collision points are identified, and the speed is dynamically adjusted according to vehicle priorities; a collaborative trajectory planning process is decomposed into a plurality of independent sub-processes, and an optimal cost unloading point distribution criterion, a potential field method and a Hybrid A * algorithm are combined, so that efficient collaborative and safe path planning of multiple dump trucks in a complex unstructured unloading area is realized.

The invention discloses a power plant safety intelligent early warning method based on a dynamic electronic fence, and the method comprises the steps: firstly analyzing the historical work ticket data of a power plant through a BERT-BiLSTM-CRF hybrid model, and recognizing an equipment entity, an operation type and a risk level; in combination with space coordinates of a power plant equipment three-dimensional model, an initial virtual safety boundary is automatically divided according to work ticket operation content, operation personnel operation actions, multi-person position relations and equipment states are fused in real time, and a buffer distance is dynamically adjusted to form a dynamic electronic fence; based on a dynamic electronic fence and high-precision positioning data, a group operation anti-collision model and a safe path planning strategy are established, a graded early warning mechanism is constructed, and corresponding grade early warning is triggered according to the real-time distance between an operator and the electronic fence through linkage of an intelligent wearable device and a power plant monitoring system. According to the invention, the intelligence and refinement level of power plant safety management can be significantly improved, and the occurrence rate of operation safety accidents is effectively reduced.

The invention provides a multi-vehicle cooperative remote control driving method, system and device and a medium, and belongs to the technical field of vehicles. The method comprises the following steps: optimizing original path data, generating a standardized path, carrying out path standardization processing, and converting individual driving experience into a reproducible and optimizable standardized path data packet, so that repeated exploration of a slave vehicle is avoided, and the overall passing efficiency of a motorcade is greatly improved; sending the standardized path to each slave vehicle to realize group intelligent parallel exploration and map construction; according to the slave vehicle environment data, the standardized path is modified, and the slave vehicle safety path is generated so as to drive the slave vehicle safety path, so that environment real-time matching and dynamic adjustment are realized, the vehicle does not blind from a historical path and intelligently deals with environment change, accidents possibly caused by mechanical reproduction are avoided, the safety and robustness are obviously improved, and the method has wide applicability.

The invention discloses an operator position monitoring method, device and equipment and a medium, and the method comprises the steps: binding pre-obtained target operator information with corresponding business data, and generating a safety constraint of a target operator according to the business data; acquiring multi-source positioning data of the target operator in real time through a positioning terminal worn by the target operator, and inputting the multi-source positioning data into a preset neural network, so that the neural network performs data fusion on the multi-source positioning data to generate a correction position of the target operator; and through a preset reinforcement learning algorithm, according to the correction position and the security constraint, generating an optimal security path, and according to the optimal security path, carrying out monitoring and risk assessment on the current correction position of the target operator. According to the invention, the real-time performance and accuracy of the position monitoring of the operating personnel can be improved.

The invention provides a dynamic safety path planning and early warning system associated with process tasks, which comprises the following steps of: distributing corresponding construction work for each worker according to on-site execution tasks of a construction site, and determining a working moment and a working moment corresponding to each worker at the same time, the method comprises the following steps: respectively acquiring current construction data corresponding to each field worker to restore the construction progress of the construction field, analyzing a plurality of current dangerous areas existing in the construction field, and respectively formulating a safe working path for each working worker according to the current moment and the distribution condition of the current dangerous areas, according to the method, a current dangerous area is established, a safe off-duty path is established for each off-duty worker, when the worker enters the current dangerous area, the clothing characteristics and behavior characteristics of the worker are checked, and corresponding danger reminding is carried out, so that real-time monitoring, risk identification and active early warning can be carried out on the activity of the worker in the non-working stage, and the overall safety management level of a construction site is improved.

The invention relates to the technical field of unmanned walking devices, in particular to a deviation correction and obstacle avoidance control method for a large-scale multi-crawler walking device, and the method comprises the steps: carrying out the real-time detection of an obstacle and the continuous advancing judgment through an intelligent sensing system based on the three-dimensional laser radar point cloud data of an intelligent holder; when the track offset is larger than a set threshold value, multi-crawler walking device kinematics model analysis and multi-crawler walking device deviation correction judgment are carried out through an automatic deviation correction system, and a rapid adjustment strategy and a precise deviation correction control strategy are formulated; and when an obstacle which cannot pass or affects the passing stability exists in the front, a new advancing path is planned through the automatic obstacle avoidance system. According to the invention, remote obstacle intelligent identification and high-precision positioning and orientation of the multi-crawler walking device can be realized, a safe path for avoiding obstacles is planned by applying an automatic obstacle avoidance algorithm, and the multi-crawler walking device can make an obstacle avoidance decision autonomously, so that safe and stable driving of the multi-crawler walking device is ensured.

The application relates to a robot motion planning method based on a greedy algorithm and an improved hybrid A* algorithm, and the method comprises the following steps: determining a traversal order of a wall-climbing robot for traversing each detection area by using the greedy algorithm, specifically comprising the following steps: selecting a detection area as a starting point; selecting a detection area closest to the current detection area and not traversed as a next detection area; adding the selected next detection area to a path and marking the detection area as visited; continuously selecting a detection area closest to the current detection area and not traversed as a next detection area until all detection areas are marked as visited; returning to the starting point; and determining the traversal order; and planning a path between adjacent detection areas by using the improved hybrid A* algorithm according to the traversal order. The method has the beneficial effect that the greedy algorithm and the improved hybrid A* algorithm are used to provide a shortest safe path that meets kinematic constraints and can be tracked for the wall-climbing robot.