1244 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 an inspection robot navigation method, system and device based on Beidou and binocular vision fusion and a storage medium, and relates to the field of robot navigation and obstacle avoidance, and the method comprises the following steps: carrying out multi-modal data fusion processing based on an initial coordinate of an inspection robot and an image captured by a binocular vision system to obtain an environment sensing result; carrying out global planning and local optimization based on an environment perception result, and carrying out cooperation to generate a trajectory planning scheme; based on the environment sensing result and the trajectory planning scheme, intelligent navigation cooperative regulation and control are carried out, and an inspection robot motion control strategy is obtained; by improving the environmental perception accuracy, optimizing the path planning and performing intelligent navigation regulation and control, the navigation problem of the inspection robot in complex environments such as a transformer substation can be effectively solved, the inspection safety and efficiency are improved, and the method has remarkable practical application value.

The invention discloses a 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 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 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 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 belongs to the technical field of path planning, and discloses a path planning method and system fusing multi-modal terrain perception and vehicle dynamics, and the method comprises the steps: obtaining and fusing multi-modal geographic data to generate a terrain physical representation, and calculating the terrain complexity based on the terrain physical representation; predicting an overturning index and a stability margin of the vehicle based on the terrain physical representation, and generating a trafficability cost function; taking the trafficability cost function as a constraint condition, selecting a motion element according to terrain complexity to perform trajectory fitting, and generating a vehicle path; executing the vehicle path to obtain a vehicle state, correcting the trafficability cost function by using the vehicle state, and optimizing the vehicle path; and constructing a loss function, training the path planning model, and performing path planning by using the trained model. According to the invention, by establishing a terrain vehicle bidirectional coupling mechanism, safe and adaptive local path planning under a complex unstructured terrain is realized, and the traffic capacity and safety of the system in a dynamic environment are significantly improved.

The invention is applicable to the technical field of emergency rescue, and relates to an optimal path planning method based on a fire rescue road network directed graph, which comprises the following steps: reconstructing the road network directed graph according to a road priority principle by using multi-source data such as an open street map road network, road conditions, survey data, real-time road conditions and the like and fusing remote sensing image data; on the basis, based on a dynamic weight model, an optimal path planning algorithm based on a fire rescue road network directed graph is further constructed. The application of the method can effectively make up for the deficiency of online map path planning used in the existing research, so that the path planning better fits the path planning requirement in a real fire rescue scene, and the driving path of fire rescue can be accurately calculated, so that the path is more reasonably planned, and the efficiency of fire rescue is improved.

The invention relates to the technical field of intelligent mobile robot path planning, in particular to a dynamic sensing path planning method for a mobile robot in an unknown environment. According to the method, an environment complexity model is established based on the obstacle density and the dynamic activity, and map partition and obstacle avoidance judgment are adaptively adjusted according to the environment complexity model; carrying out incremental updating on the near, middle and far sub-regions based on the point cloud similarity; extracting contours of the obstacle point clouds and carrying out topological optimization; dynamic candidate points are identified through double marking of rays and scanning points, and collision time is predicted and estimated in combination with spatial clustering and trajectory; and according to the collision time, hierarchical obstacle avoidance and local or global re-planning are implemented, and a repaired path is smoothed. According to the method, the path planning efficiency and the real-time obstacle avoidance capability of the mobile robot in a dynamic complex environment are remarkably improved, the consumption of computing resources is reduced, and the method is suitable for application scenes needing high dynamic response, such as warehouse logistics and unmanned driving.

The invention provides an urban low-altitude unmanned aerial vehicle instant distribution scheduling and path planning method considering demand prediction. The method comprises the following steps: establishing a three-dimensional environment map of an unmanned aerial vehicle distribution system by adopting a grid-topology hybrid modeling strategy based on three-dimensional urban environment map data; according to the three-dimensional environment map of the unmanned aerial vehicle distribution system, the starting point and the target point of the unmanned aerial vehicle, the optimal flight path of the unmanned aerial vehicle, the time required by the optimal path and the transportation cost are obtained by using an unmanned aerial vehicle path planning algorithm of space-time improvement A *; and according to the order data, the unmanned aerial vehicle performance parameters, the optimal flight path of the unmanned aerial vehicle, the time required by the optimal path and the transportation cost, using the CNN-LSTM-Attention order prediction model and the task allocation model to output a task allocation scheme including a virtual order and an actual order. Accurate spatial data support is provided for path planning of the unmanned aerial vehicle, the reasonability of low-altitude logistics distribution path planning is improved, and finer data support is provided for urban airspace management.

The invention belongs to the technical field of inspection robot systems, and particularly relates to an inspection robot path planning method and system.The visual semantic perception module collects an industrial environment image through a top industrial camera, after graying and Gaussian filtering preprocessing, feature points are detected and matched through an ORB algorithm, and a path planning result is obtained; in combination with an illumination self-adaptive threshold screening mechanism, mismatching points are eliminated, semantics are marked, and a semantic feature map is constructed; the initial path planning module generates an initial path through a semantic cost-containing A * algorithm based on the map; the dynamic obstacle avoidance module captures a moving obstacle by using a visual sensor, and predicts a trajectory through Kalman filtering; the path optimization module combines an initial path and an obstacle track, optimizes the path by using quadratic programming of a fusion curvature constraint and an energy consumption model, and corrects positioning by fusing vision and IMU data through a dynamic weight fusion algorithm; and the execution feedback module generates an instruction according to the optimized path, re-triggers path optimization, forms a closed loop, and ensures the inspection stability.

The invention relates to a robot decision control method based on gradient rarefaction and a robot. The method comprises the following steps: acquiring multi-modal sensor data of a robot; based on a preset sparsification strategy, generating a dynamic mask corresponding to the gradient matrix of the multi-modal large model; based on the generated dynamic mask, screening an effective gradient in a back propagation process of the dynamic mask; updating parameters corresponding to the effective gradient in real time, and obtaining the output of the multi-modal large model based on the updated parameters; according to the obtained multi-modal sensor data and the output of the multi-modal large model based on the updated parameters, feature fusion is carried out, and a combined state code including an environment state, a robot body state and historical decision information is generated; and according to the determined joint state code and based on a time sequence model, generating an action sequence, a force control parameter and a path planning dynamic decision instruction of the robot, so that the robot can act based on the generated dynamic decision instruction, thereby realizing decision control of the robot.

The invention relates to the technical field of automatic driving, and discloses a dense semantic map navigation system based on multi-sensor fusion and factor graph optimization, which comprises a sensor module, an environment mapping module, a real-time positioning module, a detection and classification module and a path planning control module, a complete technical architecture from environment mapping, real-time positioning, obstacle detection and tracking, trajectory prediction, global path planning and local path planning is covered, and high-precision environment mapping, real-time positioning, obstacle detection and avoidance and path planning are realized by using various sensors such as a laser radar, an IMU (Inertial Measurement Unit) and a binocular camera and a sensing data fusion technology. In combination with deep learning and an optimization algorithm, high-precision autonomous navigation of the unmanned vehicle in a complex dynamic environment is realized, and the robustness and safety of path planning are improved.

The invention relates to the technical field of unmanned aerial vehicle path planning, in particular to a multi-aerial vehicle cooperative path planning method, system and device in a city and a storage medium. Comprising an information acquisition module, a path generation module, a clustering updating module and an optimal judgment module. According to the method, the initial position, the target position and the obstacle information of each unmanned aerial vehicle are acquired, an urban low-altitude airspace task scene is defined, the MP-GWO algorithm is utilized to generate a multi-aerial-vehicle cooperative initial path, differential penalty coefficients are set for different types of no-fly zones, a K-means clustering mechanism is embedded into a GWO main cycle, and the multi-aerial-vehicle cooperative initial path is obtained. According to the method, parallel sub-populations are divided according to a multi-target fitness vector, each sub-population independently executes an alpha, beta and delta guide updating strategy, a triple termination judgment mechanism integrating fixed iteration times, a convergence threshold value and elite stagnation is adopted, a current optimal planning path is output, efficient optimization of the path in a complex urban environment is achieved, and the path optimization efficiency is improved. The method is suitable for urban low-altitude airspace multi-unmanned aerial vehicle cooperative tasks.

The invention relates to an intelligent path planning technology, and discloses an industrial robot multi-target path planning method based on a quantum annealing algorithm, and the method comprises the steps: obtaining the environment information of an industrial site; path planning is carried out based on the environment information, and a quantum Hamiltonian is constructed according to an optimization target of a path planning problem; encoding each optional path in the path planning space by adopting quantum bits, and initializing the quantum bits to be in a uniform superposition state; quantum system energy is gradually reduced through a quantum annealing algorithm, and an optimal solution of quantum bits is searched; after quantum annealing is finished, a final path is extracted; carrying out smooth optimization processing on the extracted final path; and generating a motion instruction of the industrial robot according to the optimized final path. The invention further discloses a control system, the industrial robot and a computer readable storage medium. According to the method, the global optimality of path planning is improved while multi-target path planning is efficiently carried out on the industrial robot.

The invention discloses a robot variable stiffness surface path planning method based on three-dimensional perception and application. The method comprises the following steps: processing workpiece surface point cloud data; a surface defect detection model is established, and deformation deviation generated by workpiece surface defects in the point cloud data is compensated; and performing path planning analysis on the point cloud model to generate a self-adaptive tool path. In the process that the robot executes the planned path, passive analysis is conducted through a variable stiffness impedance control law under energy constraint, force control surface treatment is achieved by collecting a force feedback track as a reference and combining a quadratic programming optimization controller, and task execution of the robot planned path is completed. According to the method, a planned path model is combined, an AI auxiliary programming framework is designed, and a user parameterization task and tool are guided through a natural language interface and a knowledge representation engine. According to the method, the adaptive capacity of the robot to the complex variable stiffness surface is improved, the machining consistency and efficiency can be remarkably improved, and the wide application prospect is achieved.

The invention discloses a dynamic path planning control method and system for a photovoltaic panel cleaning unmanned aerial vehicle, and a storage medium, and relates to the field of unmanned aerial vehicle path planning. Collecting environment information of the photovoltaic power station and parameter information of the unmanned aerial vehicle in real time; an undirected topological graph is constructed based on photovoltaic power station environment information discretization, nodes of the undirected topological graph are temporary obstacle positions and photovoltaic panel surface pollution distribution conditions, and edges between the nodes represent unmanned aerial vehicle cleaning paths; by taking cleaning maximization as a target and taking parameter information of the unmanned aerial vehicle as a constraint condition of an unmanned aerial vehicle cleaning path, constructing a dynamic path planning model; the dynamic path planning model adopts an improved A * algorithm to adjust a preset initial path in real time according to the environment information and the equipment state parameters which are updated in real time, and an optimal cleaning path is generated. The invalid flight mileage of the unmanned aerial vehicle is reduced, and energy consumption and battery loss are directly reduced.

Disclosed in the present invention is a material picking method based on a mobile robot, comprising the following steps: step S1, acquiring a real-time production plan and a bill of materials, wherein the bill of materials comprises material codes, material names, material quantity and lead time; step S2, on the basis of the material codes, dividing the bill of materials into a plurality of task units suitable for single operation of the mobile robot, and assigning corresponding picking stations; step S3, performing picking state labeling on the task units on the basis of the lead time of materials in the task units, and assigning corresponding mobile robots on the basis of the labeled picking states; and step S4, the mobile robots performing path planning on the basis of the assigned task units, and executing the assigned task units on the basis of the planned path, so that racks to which the materials in the assigned task units belong are transported to specified picking stations for operators to pick the required materials.

The invention relates to the technical field of path planning, and discloses a mountain wind power equipment transportation path planning and hoisting control method and system. The method comprises the following steps: scanning a mountain road through a laser ranging array to obtain a road cross section database, and modeling to obtain blade scanning envelope data; performing dynamic path planning by adopting a terrain adaptability predictive control algorithm, and obtaining a safe transportation path according to stress constraint verification; the sensor array monitors the blade state in real time and corrects the track; a temperature sensor collects tower drum temperature data, and the size change is predicted through a thermal deformation compensation mechanism; and the positioning system obtains hoisting reference coordinates and controls the hoisting posture in combination with the compensation parameters. The technical problems that dynamic sweeping modeling, stress constraint verification and real-time track correction are lacked in wind power equipment transportation path planning in the mountain environment, and thermal deformation compensation and multi-system collaborative optimization are lacked in hoisting control are solved. And the transportation safety and the hoisting control precision of the mountain wind power equipment are improved.

The invention relates to the technical field of loader maintenance, and discloses an automatic auxiliary system for maintenance and carrying of a loader differential mechanism. A maintenance parameter acquisition module of the system acquires current pose deviation data and vibration spectrum characteristics of a differential assembly and clamping force distribution parameters of a maintenance tool clamp in real time to generate a maintenance parameter set; the pose compensation module generates a dynamic compensation vector sequence according to the pose deviation data and updates the compensation step length in combination with the vibration spectrum characteristics; the carrying path planning module generates a carrying track control point sequence based on the maintenance process time window constraint and the clamping force distribution parameters; and the execution control module activates the dynamic compensation vector sequence and generates a pose correction instruction when detecting that the pose deviation data exceeds a first activation threshold value, activates the carrying track control point sequence and generates a track correction instruction when the clamping force distribution parameter exceeds a second activation threshold value, and inputs the instruction to a maintenance mechanical arm execution unit.

The invention discloses an unmanned aerial vehicle group low-energy-consumption flight path planning method based on distributed computing, and the method comprises the steps: constructing a three-stage distributed computing node network which comprises a unique core edge node, region edge nodes configured according to the scale of an unmanned aerial vehicle group, and unmanned aerial vehicle airborne nodes consistent with the scale of the unmanned aerial vehicle group; the core edge nodes, the region edge nodes and the unmanned aerial vehicle airborne nodes are in communication connection, sub-regions are uniformly divided according to the area of a task region, each sub-region corresponds to one region edge node, and the boundaries of the sub-regions coincide with terrain obstacles; the computing power distribution is that the core edge node undertakes global aggregation, the area edge node undertakes local calculation, and the unmanned aerial vehicle airborne node undertakes preprocessing. The defects that in the prior art, a traditional distributed mode lacks a scenarized energy consumption model and global collaboration, and large-scale application of the unmanned aerial vehicle group is restricted are overcome, and the real-time performance of flight path planning and energy consumption control are improved.

The invention relates to the technical field of dredging engineering, in particular to a dredger autonomous path planning method based on reinforcement learning, which comprises the following steps: acquiring a multi-dimensional state space vector in real time; constructing a digital twinborn scene, training a strategy network by adopting a three-stage course learning strategy through increasing environment complexity, and dynamically adjusting a path planning priority through a layered reward function; migrating the trained strategy network to a ship entity; recognizing an obstacle based on a target multi-mode sensing system, evaluating a collision risk through a dynamic probability model, and performing local path reconstruction when the collision risk exists; and in a multi-ship collaborative operation scene, constructing a global optimization model, performing dynamic task redistribution on each ship by adopting a distributed iterative solution and incremental updating strategy until convergence, and outputting a collaborative optimization scheme of each ship. According to the method, the path planning efficiency in a multi-ship collaborative operation scene can be improved, and the training cost is reduced.

The invention provides an unmanned aerial vehicle flight path dynamic planning method and system, and relates to the technical field of flight path planning, and the method comprises the steps: obtaining the starting and ending points of the flight of an unmanned aerial vehicle, and planning an initial unmanned aerial vehicle flight path based on the starting and ending points of the flight of the unmanned aerial vehicle and a preset constraint; constructing an unmanned aerial vehicle flight environment model based on a preset flight area map, the elevation information and the obstacle information; route optimization is carried out based on the initial unmanned aerial vehicle flight route and a flight environment model, and an unmanned aerial vehicle initial obstacle avoidance route is generated; the unmanned aerial vehicle initial obstacle avoidance route is optimized based on a preset iterative optimization model, and an optimized unmanned aerial vehicle obstacle avoidance route is obtained; and updating the optimized unmanned aerial vehicle obstacle avoidance route to the initial unmanned aerial vehicle flight route to generate a final unmanned aerial vehicle flight route. According to the invention, the flight time and cost are saved, the adaptability to the environment, especially the urban complex environment, is enhanced, and the safety is guaranteed.

The invention discloses a multi-robot cooperation interaction control method and system based on deep learning, and the method comprises the steps: obtaining historical interaction data, carrying out the environment state coding and feature extraction, and carrying out the correlation score matrix calculation, and obtaining a dynamic interaction weight coefficient; according to the situation representation vector and the interaction weight coefficient, performing future behavior prediction to obtain intention probability distribution data; performing encryption processing on the sensitive information according to historical interaction data to obtain encrypted shared data, performing weighted average aggregation in combination with pre-trained decision model parameters to generate a global collaborative decision model, and inputting the encrypted shared data into the global collaborative decision model to obtain a collaborative action scheme; and according to the cooperative action scheme and the intention probability distribution data, performing multi-constraint task allocation optimization to obtain an optimal task allocation scheme, and performing adaptive path planning to obtain a final action path. According to the method, real-time efficient collaborative decision making in a high-dynamic complex environment can be realized.

The invention discloses a towel rack laser welding control method and welding device based on AI visual guidance, and relates to the field of laser welding. Time-space synchronization constraint conditions of the visual six-axis module and the welding six-axis module are established based on the welding seam point set, and a double-station alternating path is generated in combination with a path planning algorithm; the double-station alternating path is converted into a control instruction to be issued to a visual six-axis module and a welding six-axis module, and the welding six-axis module executes a welding task according to the control instruction; and laser welding characteristics are obtained in real time when the welding six-axis module executes a welding task, and a composite control model is constructed based on the laser welding characteristics so as to dynamically adjust welding parameters. According to the method, collaborative path generation, collision pre-judgment and local path optimization between the double modules are achieved, and the intelligent collaborative efficiency of the welding task, the performability of path planning and the stability of the welding process are effectively improved.

The invention relates to the technical field of man-machine interaction, and discloses a multi-modal task interaction assisting method and system and computer equipment, and the method comprises the steps: collecting multi-modal interaction data of a user executing a multi-dimensional interaction task; determining a user behavior feature vector based on the multi-modal interaction data, and constructing a task completion quantitative model; determining the weight of each operation step by adopting a dynamic weight distribution strategy based on the multi-dimensional interaction task completion degree quantification model, and dynamically adjusting a preset adaptive coefficient by adopting a preset adjustment mechanism based on the multi-modal interaction data and the weight of each operation step; generating a dynamic task guiding strategy by adopting a dynamic path planning algorithm based on the weight of each operation step and a dynamically adjusted preset adaptive coefficient; and executing the dynamic task guiding strategy to obtain a multi-modal execution result. According to the method, a complete closed-loop process is formed from data acquisition, analysis, decision making to execution, intelligent services can be provided for middle-aged and elderly users, and the user experience and the task execution efficiency are improved.

The invention relates to the technical field of electric power, in particular to an unmanned aerial vehicle route automatic planning method and system for a complex space of a transformer substation. Analyzing at least one inspection task instruction input by a user to obtain a task type corresponding to each inspection task instruction and a data acquisition requirement corresponding to the task type; according to a preset three-dimensional semantic map of the transformer substation and the data acquisition requirement of each inspection task instruction, a flight route of at least one unmanned aerial vehicle is automatically generated by adopting a path planning algorithm, and the flight route comprises a waypoint sequence, a flight height, a shooting angle and a holder action instruction; the three-dimensional semantic map comprises no-fly zone information in the transformer substation, a plurality of devices, the position of each device, the device type, the electrification attribute and the safety rule label. And controlling the corresponding unmanned aerial vehicle to inspect target equipment in the transformer substation according to the flight route of each unmanned aerial vehicle. And large-scale and standardized inspection operation is realized.

The invention provides a path planning method, electronic equipment and a computer program product. The intelligent agent realizes path planning based on a pulse neural network of width, and the path planning method comprises the steps that a target area is divided based on environment information of the target area and an optimization target, a rasterized map composed of different sub-areas is determined, the optimization target comprises calculation efficiency and environment adaptability of the intelligent agent, and the rasterized map is determined based on the calculation efficiency and the environment adaptability of the intelligent agent. Each sub-region corresponds to one pulse neural network sub-node, and the rasterized map comprises a starting point position, an ending point position and obstacle information; based on the environment feature vectors of the sub-regions of the rasterized map and the position information of the intelligent agent, the current state of the intelligent agent is determined, and the current state is determined based on the Markov decision process; and based on the current state of the intelligent agent and a path planning algorithm, performing path planning on the rasterized map, and determining a planned path.