1816 results about "Path plan" patented technology

The invention relates to a heterogeneous multi-unmanned aerial vehicle cooperative path planning method based on multi-agent deep reinforcement learning, and solves the problem that a traditional path planning algorithm is difficult to deal with a dynamic planning problem and an agent cooperation problem compared with the prior art. And the existing multi-unmanned aerial vehicle cooperative path planning algorithm has the defects of insufficient feature extraction capability, low experience learning efficiency and poor cooperative strategy flexibility in a dynamic uncertain environment. The method comprises the following steps: analyzing a multi-unmanned aerial vehicle cooperative path planning task; building an airspace reinforcement learning environment; introducing an unmanned aerial vehicle kinetic equation; modeling a multi-unmanned aerial vehicle decision model in multi-unmanned aerial vehicle cooperative path planning as a POMDP model; designing an MASAC-SEPR algorithm model to carry out path planning on multiple unmanned aerial vehicles, and generating a multi-agent collaborative path optimization network model; and training the multi-agent collaborative path optimization network model. According to the method, the efficiency and the accuracy of multi-unmanned-aerial-vehicle cooperative path planning are remarkably improved, and a reliable solution is provided for heterogeneous multi-unmanned-aerial-vehicle cooperative operation in a dynamic uncertain environment.

The invention discloses a rescue robot path planning method and system under industrial vision assistance, and relates to the field related to industrial vision, and the method comprises the steps: collecting three-dimensional space data of a rescue environment in real time, generating a dynamic environment point cloud data set, and constructing a three-dimensional semantic map of a rescue area; thermal imaging data updated in real time are called, path analysis is carried out in combination with the three-dimensional semantic map, and a path planning strategy set is obtained; and predicting the motion track of the dynamic obstacle based on the local dynamic obstacle avoidance strategy, optimizing the global path planning strategy according to obstacle prediction track data, and generating a motion control instruction of the rescue robot. The technical problem of poor real-time performance and adaptability of path planning caused by insufficient perception of environment dynamic information in path planning of an existing rescue robot is solved, the strong perception capability depending on industrial vision is achieved, the environment dynamic information is accurately captured in real time, and the real-time performance of path planning is improved. And the real-time response speed of path planning and the adaptability to a dynamic environment are improved.

The invention discloses a reinforcement learning unmanned forklift truck obstacle avoidance scheduling method and system for a dynamic obstacle, relates to the technical field of unmanned driving, and discloses the reinforcement learning unmanned forklift truck obstacle avoidance scheduling method for the dynamic obstacle. According to the method, the system and the system, the system and the system, global path planning and local obstacle avoidance decision making are carried out in combination with the reinforcement learning network, the problems of obstacle avoidance response delay and unreasonable path planning in a dynamic environment are solved, the obstacle avoidance response speed, the path planning rationality and the multi-modal data fusion precision in the dynamic environment are improved, and then the safety and efficiency of unmanned forklift dispatching are improved.

The invention discloses a multi-robot collaborative scheduling system in an automatic warehousing system, which relates to the technical field of robot collaborative scheduling and comprises a task management module, a path planning module, a communication collaborative module, an exception handling module, a warehousing space dynamic partition module and a task fusion scheduling module. The task management module comprises a task priority calculation unit, a task distribution unit and a dynamic energy consumption evaluation unit, the path planning module comprises a global path optimization unit, a local path adjustment unit and an obstacle avoidance path optimization unit, and by arranging the task management module, the dynamic task priority calculation and distribution function is achieved, and the dynamic energy consumption is evaluated. The problem of adaptation of dynamic task requirements and complex environments is solved, and the completion speed of task allocation and path planning is increased; by arranging the path planning module, the function of dynamically optimizing the path according to the real-time environment is realized, the problem of path conflict optimization in multi-robot scheduling is solved, and the path obstacle avoidance and execution efficiency is ensured.

The invention discloses a land surveying and mapping path planning method based on an unmanned aerial vehicle technology, and the method comprises the steps: enabling an unmanned aerial vehicle to achieve the precise perception and recognition of static and dynamic obstacles in a dynamic environment through a self-adaptive multi-mode perception fusion algorithm; on the basis of multi-modal state estimation and the game theory, modeling and predicting behaviors and future trajectories of the dynamic obstacles; an obstacle avoidance path is generated and optimized in combination with an adaptive fast random tree and deep reinforcement learning; the unmanned aerial vehicle tracks a planned path and deals with dynamic environment changes in real time; the multiple unmanned aerial vehicles realize cluster cooperation through a group self-organizing flight optimization algorithm, and dynamically adjust task allocation and flight strategies; and through incremental environmental perception updating and feedback-based path planning optimization, the perception and decision model is automatically updated after each task is executed. According to the method, the defects of a traditional path planning algorithm in a complex dynamic environment are overcome, and the real-time performance and adaptability of the unmanned aerial vehicle for executing the land surveying and mapping task are greatly improved.

The invention relates to the technical field of equipment control, and provides a vision-based collaborative robot end control method and related equipment thereof. Multi-modal dynamic anti-interference filtering and semantic fusion are carried out on original visual data to obtain three-dimensional semantic point cloud data, and coordinate system alignment is carried out on the three-dimensional semantic point cloud data according to a robot-based coordinate system to obtain a target pose parameter set; performing dynamic path planning on the target pose parameter set to obtain a joint space motion track, and performing inverse kinematics solution and motor instruction synthesis on the joint space motion track according to robot D-H model parameters to obtain a motor control instruction, and performing closed-loop feedback optimization on the motor control instruction according to the state data of the end effector to obtain a dynamic correction control instruction. According to the method, through full-link optimization from sensing, planning to execution, the operation precision and safety of the collaborative robot in an uncertain environment are remarkably improved.

The invention discloses a path planning heuristic function generation platform and method based on collaborative optimization of a large-scale language model and evolutionary computation. According to the technology, the large-scale language model (LLM) and evolutionary computation (EC) work cooperatively. The platform generates or mutates a heuristic function expressed as an executable code through LLM based on a structured prompt containing an environment context and performance feedback; and an EC framework (such as genetic programming) is combined with performance evaluation feedback to perform selection and iterative optimization on a heuristic code population, and population diversity is maintained. The method aims at overcoming the limitation that a traditional heuristic design is difficult and poor in adaptability, a high-quality heuristic function adapting to a complex and dynamic environment is automatically generated, and therefore the efficiency of a path planning algorithm and path quality are remarkably improved.

The invention relates to the technical field of robot autonomous navigation and control, and discloses a robot system based on a sensing system, which comprises a multi-mode sensing module for outputting data to a dynamic environment modeling module; the dynamic environment modeling module is used for transmitting the model to the path planning module; the path planning module outputs the path sequence to the neural motion control module; the neural motion control module outputs the control signal to the dynamic execution module; the dynamic execution module is used for receiving the pulse control signal and driving each joint of the robot to execute the path in combination with inverse kinematics solution and a feedback control mechanism; and the collaborative optimization module is in two-way communication with the modeling module, the path planning module and the neural motion control module. According to the method, the contradiction between real-time performance and integrity of environment modeling in a complex scene is solved by fusing cross-modal information complementary characteristics of binocular vision, millimeter-wave radar and inertial data and combining mathematical representation of a hypergraph topology model on a dynamic obstacle interaction relationship.

The invention provides a multi-AGV distributed path planning method based on graph reinforcement learning, which is used for solving the technical problem of low path planning efficiency of a multi-AGV system in an information limited dynamic environment. The method comprises the following steps: establishing a dynamic heterogeneous graph model in multi-AGV collaborative path planning; constructing a distributed partially observable Markov decision model based on a graph structure in combination with path planning and collaborative decision; on the basis of a multi-agent deep reinforcement learning framework of a graph neural network and a multi-head attention mechanism, a multi-AGV cooperative path planning algorithm for coping with an information-limited dynamic environment is designed, information aggregation is realized on the basis of local perception and neighborhood interaction by adjusting weights of nodes and edges in a dynamic heterogeneous graph, and a multi-AGV cooperative path planning algorithm for coping with an information-limited dynamic environment is realized. And the environment change is updated and adapted in real time. According to the method, the path planning efficiency can be improved through an information aggregation mechanism in an information-limited dynamic environment, multi-AGV cooperative path planning is optimized through the graph neural network, and efficient and stable operation of the system is ensured.

The invention relates to the technical field of storage equipment control, and discloses a dynamic cooperative control method and system for digital twin-driven storage equipment. The method comprises the following steps: firstly, constructing a storage equipment digital twin model which covers a storage space three-dimensional model, equipment motion parameters and a real-time operation state; and then collecting sensor data in real time, and generating a collaborative operation path through a dynamic path planning algorithm containing conflict detection and priority allocation. And correcting the equipment motion track and updating the model state by using the space-time consistency verification model, finally inputting the corrected track into an equipment control instruction generation module, and outputting a driving signal to control the storage equipment. The system comprises a digital twinborn model construction module, a sensor data acquisition module, a collaborative operation path generation module, a motion trail correction module, a control instruction generation module, a driving module and the like. The storage space utilization efficiency and the equipment collaborative operation capability are improved, real-time monitoring and fault prevention are achieved, and intelligent upgrading of a storage system is promoted.

The invention relates to the technical field of artificial intelligence, can be applied to the field of medical health, and discloses a navigation method, device and equipment in an unexplored environment and a medium. Analyzing a pre-trained large language model into a structured semantic representation at least comprising target entity information, spatial constraint information and contextual intention information; inputting the target entity into a pre-trained large language model to generate a standardized task description question, inputting the standardized task description question and real-time environment image data into a pre-trained visual language question and answer model, and outputting a semantic answer result about whether the target entity exists or not; and if yes, generating a navigation strategy through a pre-trained large language model based on the structured semantic representation, and controlling the robot to execute a navigation action through a preset path planning algorithm. The method does not need to depend on an environmental map and scene training, realizes navigation in an unexplored environment by utilizing cooperation of a large language model and a visual language question and answer model, solves the problem of strong dependency of a traditional scheme on prior data, and has good generalization ability and environmental adaptability.

The invention belongs to the technical field of path planning of unmanned equipment, and relates to a medical rescue unmanned aerial vehicle and robot dog cooperative linkage method, which comprises the following steps: integrating LiDAR point cloud, visual images, IMU inertial data and compensated UWB positioning data into a multi-modal data stream subjected to space-time alignment through time alignment and space alignment technologies; respectively extracting LiDAR and visual feature poses through parallel SLAM (Simultaneous Localization and Mapping) calculation, and carrying out graph optimization and joint optimization by utilizing a GTSAM library, so as to construct a globally optimized pose and a global semantic map; the path planning algorithm provides dynamic channel routing for the unmanned aerial vehicle and the robot dog based on a global semantic map, the feature fusion weight is adjusted by considering the environment illumination intensity and the point cloud density, the reverse iris control mechanism detects whether the robot dog enters a shielding area through a UWB signal intensity attenuation value, the unmanned aerial vehicle is switched to a UWB signal follower, and the UWB signal follower is switched to the unmanned aerial vehicle. And through combination with a Kalman filter, penetration positioning in a shielding area is realized, so that stable execution of a rescue task in a complex environment is ensured.

The invention discloses an unmanned aerial vehicle route planning method based on an improved grey wolf optimization algorithm. The method comprises the following steps: 1, environment modeling and parameter setting; 2, designing constraint conditions and a target function; 3, initializing an algorithm and generating a population; 4, improving an algorithm and updating a track; and step 5, iteration termination and result output. The flight path of the unmanned aerial vehicle is efficiently coded in a spherical vector coding mode, and the flexibility and the calculation efficiency of flight path representation are remarkably improved; by introducing the Levy flight strategy, the global search capability of the algorithm is enhanced, and falling into a local optimal solution is avoided; meanwhile, a parameter adaptive adjustment strategy is combined, so that the convergence speed and precision of the algorithm are remarkably improved. According to the method, the problem of flight path planning in a complex mountain environment and a threat area is effectively solved, a safer and more efficient flight path is planned for the unmanned aerial vehicle, and the method has higher practicability and reliability.

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 relates to the technical field of bridge construction, in particular to an intelligent cleaning device and method for a box girder side formwork. The device comprises a mobile platform, a control device, a mechanical arm assembly, a cleaning device, an infrared induction sensor, a dust collection device, a multispectral visual recognition module, a 3D environment modeling module, a path planning module, a cleaning quality detection module and a self-adaptive learning database. The multispectral visual recognition module recognizes the stain type and the adhesion strength. The 3D environment modeling module is used for constructing a three-dimensional point cloud model of a working area in real time; the path planning module generates an optimal cleaning path based on a reinforcement learning algorithm; the cleaning quality detection module detects residual stains and triggers secondary cleaning; the adaptive learning database stores historical cleaning data and optimizes a cleaning strategy through a machine learning model. The full-process automatic cleaning operation of the box girder side form can be achieved, and the comprehensive functions of dirt recognition, autonomous path planning, efficient cleaning, real-time detection and the like are achieved.

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, which relates to the parking space management field, discloses a visual parking lot parking space release management system comprising a dynamic twinborn modeling module, an intelligent path planning module, a dynamic twinborn modeling module, a dynamic twinborn modeling module and a dynamic twinborn modeling module. The dynamic twin modeling module comprises a multi-source data acquisition module, a point cloud preprocessing module, a semantic enhancement modeling module, a real-time rendering and optimizing module and a dynamic updating and maintaining module; the intelligent path planning module comprises a high-precision positioning and environment sensing module and a space-time constraint path planning module. According to the visual parking lot parking space release management system, the distance between the vehicle and the parking space, the turning frequency, the energy consumption and the gradient can be comprehensively considered through the improved D * Lite algorithm, the traffic situation of the parking lot in the future is predicted in combination with the traffic situation prediction module, the path is recalculated every 5 seconds, the path of the parking space can be effectively planned, and the parking space release management efficiency is improved. And the problem of vehicle energy consumption can be effectively avoided.

The invention discloses a robot unknown environment autonomous exploration navigation method based on CTSAC, and the method combines the powerful sequence perception capability of Transform and a Soft Actor-Critic deep reinforcement learning framework, and introduces curriculum learning of a regular review mechanism, laser radar partition optimization processing, and refined reward setting. The state of the robot is represented by laser radar data and gyroscope data which are subjected to laser radar partition optimization processing, meanwhile, after state information is subjected to refined reward setting, corresponding reward values are obtained, corresponding experience sequences are stored in an experience pool, and data obtained through sampling of the experience pool are supplied to a neural network for training. The method is suitable for the robot to perceive the surrounding environment through the sensor in an unknown environment to perform path planning so as to reach a specified target point or establish an environment map, and is used for complex tasks such as space exploration, search and rescue, reconnaissance and the like.

In various examples, a technique for generating a path between a current location and a target waypoint is disclosed that includes receiving a route plan that is associated with a plurality of waypoints representing locations in a physical environment. The technique also includes identifying a search space that includes the route plan, and identifying a target waypoint of the plurality of waypoints—the target waypoint being in a portion of the search space between a current location of a mobile robot and an end waypoint of the route plan. A path between the current location of the mobile robot and the target waypoint may then be generated.

The invention discloses an intelligent car coupler identification method and system of a car dumper based on a picking and re-hooking robot, and relates to the technical field of intelligent identification, the method comprises the following steps: multi-modal data is collected and preprocessed, and the multi-modal data comprises laser radar point cloud data, camera images and infrared data; fusing the processed multi-modal data, identifying coupler information by adopting a convolutional neural network and an attitude estimation algorithm, and generating a coupler identification result; a path planning algorithm is adopted, a grabbing point path of the car coupler is calculated, interferents are avoided, the grabbing path is optimized, and an optimal grabbing path is generated; and the robot moves to the position of the car coupler according to the optimal grabbing path, the grabbing posture is corrected in real time through image perception, and a mechanical arm tail end executor is driven to clamp the car coupler for grabbing. Through multi-modal data fusion and path planning, the coupler is accurately recognized, the grabbing path is optimized, and the grabbing precision and stability of the robot in a complex environment are improved.

The invention discloses a low-altitude logistics unmanned aerial vehicle path planning method and system, and relates to the technical field of unmanned aerial vehicle logistics transportation, and the method comprises the steps: firstly, carrying out the optimization through calculating a preliminary path and combining the terrain and weather information; a sensor is used for detecting and dynamically avoiding obstacles; secondly, data transmission between the unmanned aerial vehicle and the ground control center is achieved, and the ground control center carries out monitoring, control and data processing. According to the low-altitude logistics unmanned aerial vehicle path planning system, efficient path planning is realized through multi-module cooperation. According to the invention, the flight safety, path planning accuracy and energy utilization efficiency of the unmanned aerial vehicle can be improved, the operation cost is reduced, and the system adapts to complex environmental conditions.

The invention provides an unmanned system autonomous navigation method based on Beidou and multi-source information adaptive fusion, and belongs to the technical field of high-precision intelligent navigation. According to the invention, autonomous navigation is realized by constructing a three-layer navigation architecture of space-time alignment, intelligent fusion and behavior control. In the space-time alignment layer, performing time and space alignment on the acquired multi-source sensing data; in the intelligent fusion layer, environment types are identified through CNN, modeling is carried out on observation uncertainty of GNSS, LiDAR and IMU in combination with variational Bayesian reasoning, confidence weights are dynamically updated, and a layered federated filtering architecture is adopted to output a full-parameter navigation solution; and in the behavior control layer, the trigger threshold value and the safety margin of the behavior state are dynamically adjusted in the path planning execution process, the different behavior states are subjected to priority ranking and dynamic switching, linear speed and angular speed control instructions are output according to the behavior states, and autonomous navigation is achieved. The problems of low navigation precision, high fusion rigidity, decision disjunction and the like are solved.

The invention discloses an intelligent scheduling and path planning system and method for a mine carry-scraper, and belongs to the technical field of mining, and the system comprises an environment sensing module which is used for collecting the environment data of a mine operation area in real time, and the environment data comprise the equipment state, the road condition and the dynamic obstacle position; processing the collected data to generate environment state parameters; and the task scheduling module is used for allocating operation tasks according to the equipment state, the task target and the environment state parameters, and performing global optimization on task allocation to minimize task completion time and energy consumption. Generating a task allocation scheme based on the equipment state and the task target through a task scheduling module; the path planning module realizes optimal path planning through multi-device game optimization, and the global optimization control module coordinates the work of each module, optimizes global targets such as task completion time, energy consumption and conflicts, realizes deep fusion of scheduling and path planning, and improves the working efficiency, global optimality and robustness of mining devices.

The invention discloses an automatic stacking control system based on visual positioning. The automatic stacking control system comprises a multi-mode visual positioning module, a model building module, a dynamic path planning module, a mechanical arm execution module, an error correction module and a storage module. The multi-mode visual positioning module comprises an RGB camera, a sensor and a laser radar and is used for obtaining three-dimensional space information of goods and a stacking area in real time. The multi-modal visual positioning module is mounted on one side of the stacking area; the model building module is used for building a stacking area, the cargo position and the position of the mechanical arm in a three-dimensional space, when the system is used, information about the position of the stacking area and information about whether new cargoes can be stacked in the stacking area or not can be obtained, then movement at any position in the space can be achieved through the three-axis cooperation mechanical arm, and the three-axis cooperation mechanical arm can move at any position in the space. The application range of the system is widened, and cargoes can be accurately conveyed to the position of a stacking area through the dynamic path planning module and the error correction module.

The invention relates to the technical field of warehouse logistics equipment, and discloses an omni-directional intelligent mobile forklift control system. The system comprises a dynamic path planning module, an omnidirectional motion control module, a load stability monitoring module, an environment adaptive adjustment module and an intelligent obstacle avoidance decision module. The dynamic path planning module collects storage environment multi-dimensional spatial data and constructs a dynamic environment topological map; the omni-directional motion control module analyzes driving wheel parameters based on the map and generates an omni-directional motion composite motion instruction set; the load stability monitoring module collects a fork pressure distribution matrix and a cargo gravity center offset according to an instruction set, and calculates a stability compensation parameter; the environment adaptive adjustment module fuses illumination and slope data to generate a motion control constraint condition; and the intelligent obstacle avoidance decision module predicts a dynamic obstacle trajectory, outputs an obstacle avoidance path re-planning instruction and updates a map. The system can meet the requirements of modern intelligent warehousing.

The invention provides a construction robot operation path optimization method, and belongs to the technical field of automatic scheduling and path optimization, and the method comprises the steps: 1, distributing an optimal operation sequence for each robot based on a construction task; 2, acquiring BIM data and field environment information, and determining an initial construction path of each robot in combination with the optimal operation sequence of each robot; 3, the initial construction paths of all the robots are analyzed, and the construction path of each robot is determined based on the path analysis result; 4, the position and speed of each robot are collected in real time in the construction process, the robot operation coverage rate, shutdown time and construction quality data are periodically counted, and then the path planning coefficient of each robot is determined; and 5, iteratively optimizing the path planning parameter of each robot based on the path planning coefficient of each robot until a preset efficiency improvement target is met. And the overall construction efficiency and the intelligent level are obviously 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 an intelligent platform system for modern industrial system construction, and the system specifically comprises a heterogeneous data fusion center which is used for collecting and standardizing the multi-source heterogeneous data of enterprise equipment and environment in an industrial chain in real time; the knowledge graph construction module is used for generating a real-time operation situation graph according to the multi-source heterogeneous data; the AI decision engine is used for executing risk prediction and resource optimization path planning according to the real-time operation situation map; the intelligent dispatching center is used for dynamically reconfiguring idle equipment, talents and funds of an industrial chain according to resource optimization path planning; and the ecological value contract module is used for calling a block chain to convert the data assets into on-chain verifiable collaborative benefits according to the reconfiguration result of the intelligent dispatching center. According to the invention, industrial chain data fusion, situation awareness, intelligent decision, resource dynamic configuration and data asset collaborative income conversion are realized, and the overall operation efficiency and collaborative value of an industrial system are improved.

The invention relates to the technical field of unmanned aerial vehicles, and discloses an unmanned aerial vehicle path planning method, device and equipment and a storage medium, and the method comprises the steps: responding to a received inspection task, and obtaining an inspection map corresponding to the inspection task; based on the inspection map, generating a first planned path through a path planning algorithm, and controlling the unmanned aerial vehicle to inspect according to the first planned path; in the inspection process, dynamic obstacle information on the first planned path is obtained; based on the dynamic obstacle information, obtaining a predicted motion track of the dynamic obstacle; generating an obstacle avoidance strategy based on the predicted motion track of the dynamic obstacle; and controlling the unmanned aerial vehicle to inspect according to the first planned path and the obstacle avoidance strategy. The unmanned aerial vehicle can be ensured to accurately plan the flight path in real time in a complex flight environment, and dynamic obstacles can be effectively avoided.

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.