927 results about "Waypoint" patented technology

The invention provides a patrol route self-adjusting method and system based on unmanned aerial vehicle patrol, and the method comprises the steps: firstly obtaining a multi-source environment perception data set of a target area, then carrying out the feature extraction of the multi-source environment perception data set, inputting a dynamic route planning model, generating an obstacle avoidance correction vector set and an environment adaptation parameter set, and carrying out the feature extraction of the multi-source environment perception data set; and carrying out spatial position offset compensation on the reference route based on the obstacle avoidance correction vector set to generate an initial correction path node set, carrying out path smoothness optimization processing on the initial correction path node set according to the environment adaptation parameter set to generate a final dynamic patrol route, and finally, carrying out dynamic patrol. The dynamic patrol route is converted into a waypoint control instruction stream which can be executed by the unmanned aerial vehicle flight control system, and the waypoint control instruction stream is transmitted to the airborne controller in real time to drive the unmanned aerial vehicle to execute the patrol task, so that dynamic self-adjustment of the unmanned aerial vehicle patrol route is realized, and the patrol flexibility and safety are improved.

The invention relates to an unmanned aerial vehicle intelligent dynamic path guidance method and system based on deep reinforcement learning. The method comprises the steps of obtaining three-dimensional terrain point cloud and obstacle information of a flight area, constructing an environment topological structure diagram, extracting features through a three-layer diagram convolutional network, and generating high-dimensional environment feature representation. And a deep reinforcement learning state space is constructed, and a multi-target reward function is designed. And initializing the deep reinforcement learning network by using [-0.1, 0.1] uniformly distributed random parameters to generate an initial path scheme. Optimizing through a dynamic planning algorithm, adjusting a node sequence according to a path length and a task dynamic threshold value, and calculating a target function value. And judging whether the current path scheme reaches the optimal balance point, if not, adjusting the weight of the reward function by using a gradient descent method, and regenerating the path scheme until the path scheme is optimal. And finally, converting the scheme into a waypoint coordinate and speed instruction, performing real-time monitoring during flight, and triggering re-planning when an obstacle exceeds a safety threshold, so as to achieve the purposes of safe flight of the unmanned aerial vehicle in a complex environment and the like.

This document describes trajectory planning in a three-dimensional (3D) search space with a space-time artificial potential field. An example system includes a processor that obtains an initial pose, a goal pose, and an obstacle map for an environment. The processor uses a parking trajectory algorithm to determine a trajectory by searching in a space-time artificial potential field. The trajectory includes a series of 3D waypoints, including two-dimensional (2D) positional coordinates and time coordinates, to navigate from the initial pose towards the goal pose. Operation of the host vehicle is then controlled to maneuver along the trajectory using an assisted-driving or autonomous-driving system. In this way, path searching is performed in the entire 3D search space rather than iterating between two 2D search planes. This allows motion planning for autonomous parking, especially with static and dynamic objects present, to determine an optimal trajectory in a single iteration.

The invention discloses a real-time three-dimensional modeling method and system based on 5G flight control and a medium, and belongs to the field of three-dimensional modeling, and the method comprises the steps: obtaining a modeling demand and task environment parameters of a user, and planning a route; controlling the unmanned aerial vehicle to fly according to the planned route; judging whether the position data and the attitude data of the unmanned aerial vehicle at each waypoint are within a preset threshold range, and if so, controlling the unmanned aerial vehicle to collect image data of the target area; the collected image data is preprocessed; extracting feature point data of the target area by utilizing machine learning according to the preprocessed image data; converting the feature point data of the target area into sparse point clouds by using a triangulation algorithm; converting the sparse point clouds into dense point clouds through an MVS algorithm; and according to the pre-processed dense point cloud, a Poisson surface reconstruction algorithm is adopted to establish a three-dimensional grid model of the target area. Aiming at the low precision of unmanned aerial vehicle three-dimensional modeling in the prior art, the method improves the precision of unmanned aerial vehicle three-dimensional modeling.

The invention relates to the technical field of expressway unmanned aerial vehicle route planning, in particular to an intelligent route planning method and system for an expressway unmanned aerial vehicle, and the method specifically comprises the steps: planning unmanned aerial vehicle waypoints on an expressway based on a target three-dimensional model, and obtaining a waypoint set of the expressway; historical route data of the expressway are obtained, a deep learning algorithm is used for analyzing the historical route data, safe flight path features are extracted, a route safety evaluation model is constructed, and the route safety evaluation model is used for evaluating the route safety level of the route of the unmanned aerial vehicle; and determining route planning parameters according to the unmanned aerial vehicle task type, and determining an unmanned aerial vehicle route through the waypoint set, the route safety evaluation model and the waypoint planning parameters based on a multi-objective optimization algorithm. According to the invention, the safety and efficiency of the unmanned aerial vehicle in highway inspection and monitoring tasks are improved, and automatic planning and dynamic adjustment of the route of the unmanned aerial vehicle based on real-time data analysis are realized.

The invention belongs to the technical field of marine environment prediction, and discloses a ship navigation sea wave dynamic space-time prediction method and system based on deep learning. The method comprises the following steps: carrying out space-time alignment, missing value repair and standardization processing on acquired ship AIS data and an ERA5 reanalysis data set, and generating node feature vectors containing latitudes and longitudes, timestamps, wind speeds and significant wave heights; through node feature coding, centrality coding, space coding and time coding, ship trajectory node importance and time-space interaction relation are quantified. Constructing a SeaGraph model, and outputting an effective wave height prediction value of a target waypoint; and performing verification. According to the method, the space-time constraint of a traditional static modeling framework is broken through, the advantages of a self-attention mechanism and a graph network are integrated, the predictive modeling capability of dynamic evolution of a wave field in front of a ship navigation track is enhanced, and high-precision sea wave forecasting support is provided for intelligent ship navigation under complex sea conditions.

The invention relates to the field of unmanned aerial vehicle route acquisition, in particular to an unmanned aerial vehicle dynamic inspection route acquisition method and system based on laser point cloud. The method comprises the steps of comprehensive processing of three-dimensional point cloud data, marking of equipment inspection point positions to generate waypoints, safe distance collision inspection of the waypoints, position adjustment of the waypoints, planning of inspection paths and the like, through comprehensive processing of the three-dimensional point cloud data, including denoising, classification and thinning, the quality of the point cloud data can be remarkably improved, and by means of the laser point cloud technology, the quality of the three-dimensional point cloud data is improved. The terrain and equipment characteristics of the to-be-inspected area can be accurately captured, so that the accuracy and reliability of inspection are improved. The steps of waypoint safe distance collision inspection and waypoint position adjustment further enhance the autonomous flight ability of the unmanned aerial vehicle, and ensure the safety and stability of the inspection process. When the routing inspection path is planned, an optimal safe path algorithm is adopted, the path between two waypoints is searched, a local route is generated, and finally the local route is connected to form a complete route, so that the high efficiency and integrity of routing inspection are ensured.

The invention relates to the technical field of unmanned aerial vehicles, in particular to an intelligent flight path planning and energy management system and method for a long-endurance fixed-wing unmanned aerial vehicle. Comprising an environment sensing unit; the flight path planning unit is used for planning a flight path meeting task requirements, safety requirements and energy constraints based on the flight environment information of the unmanned aerial vehicle acquired by the environment sensing unit and pre-stored performance parameters of the unmanned aerial vehicle; and the energy management unit realizes energy dynamic management and optimization according to the real-time energy state of the unmanned aerial vehicle, the flight task and the planning result of the flight path planning unit. The flight path planning unit can call working condition energy consumption data such as navigational speed and height output by the energy management unit in real time, and dynamically adjust the weight of the path to avoid high-energy-consumption flight segments; the energy management unit synchronously and intelligently adjusts a main / standby battery charging and discharging strategy to optimize energy distribution according to a task time sequence (such as waypoint priority and track curvature) of a track.

The present disclosure belongs to a field of UAVs exploration technology, discloses a high-efficient autonomous exploration method, system, and terminal for UAVs, comprising: S1, heuristic waypoint generation: setting an exploration scope and waypoint spacing, and generating waypoints through waypoint generation algorithms; S2, global path planning: after generating heuristic waypoints, an A * algorithm is used to generate the global planning path; S3, real-time positioning and mapping: using point clouds for real-time positioning and mapping; S4, local B-spline trajectory generation: using B-spline parameterization method to generate local trajectories; S5, real-time obstacle avoidance and dynamic feasibility constraints: optimizing the trajectories to achieve fast convergence, generating smooth, collision-free, and dynamically feasible trajectories; S6, local real-time replanning: using a time sliding window for local replanning; S7, flight control: Using UAV control algorithms for controlling of UAVs robustly.

An unmanned ocean vehicle apparatus having a hull, solar array, hydrofoil, and electric thruster is operable on a surface of a body of water to travel from one point to another with long range capabilities. The unmanned ocean vehicle can further be placed in a more compact configuration, incorporating a mechanism to alternate between operational mode and collapsed storage mode to facilitate physical storage or shipment of the vehicle. Methods are described for optimizing travel and determining favorable movements of the unmanned ocean vehicle by calculating current and predicted conditions at certain waypoints using a combination of a sensor, a computer, and a control system. The physical support of hydrofoils, a drag reducing system, capsize-prevention technique, and a three-dimensional arrangement of solar panels provides efficient power management, optimal route strategy, and sustainability of the vehicle's high speeds in wildly varying ocean conditions.

This document describes motion planning with variable grid resolution for graph-search-based planning. An example system includes a processor that obtains an initial pose, a goal pose, and an obstacle map for an environment. The processor uses a motion-planning algorithm to determine a path or trajectory using two or more grid resolutions for a graph-based search. The path includes a series of waypoints, including two-dimensional positional coordinates (and time coordinates if a trajectory), to navigate from the initial pose towards the goal pose. Operation of the host vehicle is then controlled to maneuver along the path using an assisted-driving or autonomous-driving system. In this way, motion planning is performed for the entire path but uses a coarser grid resolution for the portion nearer the goal pose. This allows motion planning for autonomous parking, especially in environments that include static and dynamic objects, to be handled in a more computationally-efficient manner.

The invention discloses an autonomous navigation method for an unmanned vehicle in a complex outdoor environment, and the method comprises the steps: firstly, collecting data through a depth camera, a laser radar and other multi-source sensors, and carrying out the precise detection of dynamic obstacles and low obstacles through a multi-detector fusion algorithm and a visual language model; secondly, adopting a sector region multi-index waypoint sampling and optimization strategy to realize local path planning; and finally, designing a check point self-adaptive correction mechanism, and automatically planning a new substitute waypoint when the key check point is occupied by the obstacle for a long time. The method effectively solves the problem that the traditional navigation method is difficult to give consideration to dynamic obstacle sensing, low obstacle sensing and unreachable check points at the same time, and realizes the efficient and safe autonomous navigation function of the unmanned vehicle in a complex outdoor environment.

The invention relates to the technical field of low-altitude aircraft obstacle avoidance, and discloses an autonomous obstacle avoidance method and system for a low-altitude intelligent dynamic monitoring aircraft, and the method comprises the steps: carrying out the state vector construction of a position coordinate, a flight speed and a course angle of a low-altitude aircraft cluster, and obtaining a state space of the low-altitude aircraft cluster; performing Bezier curve calculation and genetic optimization on the control point sequence in the low-altitude aircraft cluster state space to obtain an optimal detection route parameter; performing geometric distance calculation and double-point removal processing according to the waypoint sequence of the optimal detection route to obtain a patrol airspace equation; performing multi-level reward function calculation through a hierarchical reinforcement learning framework to obtain a high-low layer decision network; time sequence differential learning and strategy gradient optimization are carried out based on the high-low layer decision network to obtain a distributed control strategy, so that global optimization control of the low-altitude aircraft cluster is realized, and stable operation of the low-altitude aircraft cluster in an unknown wind disturbance environment is ensured.

The invention provides an energy optimal path planning method and device for an unmanned surface vessel in a complex marine environment, and belongs to the technical field of path planning. The method provided by the invention comprises the following steps: constructing a comprehensive energy consumption model; constructing a hybrid enhanced particle swarm optimization algorithm; constructing a safety space set and an adjacent graph through a grid method, generating an initial optimal path by adopting an A * algorithm, and carrying out bounded random disturbance and safety correction on target particle waypoints; smoothing the path generated by iteration by adopting a B-spline technology, resampling the smoothed optimal solution, and then reinjecting the optimal solution into the population; adjusting an inertia weight and a learning factor based on the number of iterations, and introducing double guide factors to carry out secondary adjustment on a cognitive item of particle speed updating; and embedding the comprehensive energy consumption model as a fitness function into a hybrid enhanced particle swarm optimization algorithm, performing real-time energy consumption evaluation, individual and global optimal path updating and path smooth optimization on each candidate path in an algorithm iteration process, and outputting an optimal navigation path of the unmanned surface vessel.

An arrangement for guiding an agricultural vehicle (10) across a field (58) and a corresponding procedure comprise: a visual sensor system (62) directed at the field (58), which is connected to an image processing system (36) via signal transmission, which image processing system (36) is configured to identify features of the field (58) in the image signal of the visual sensor system (62) and to calculate a first path to be traveled for the vehicle (10) based on the identified features, a navigation unit (48) for determining the position of a reference point on the vehicle (10), a memory (72) in which stored waypoints for a second route to be traveled by the vehicle (10) are stored, a path calculator (70) configured to convert the first and / or second path into a common coordinate system, and a fusion unit (74) configured to merge the first path and the second path and, based on the merged path obtained in this way, to supply a signal to a steering control unit (34) to steer the vehicle (10) along the merged path and / or to store waypoints calculated on the basis of the merged path for the positions traveled by the vehicle (10) in a memory (72). It is proposed that the first path and / or the second path includes or comprises more than two waypoints and / or that the path calculator (70) or the image processing system (36) is configured to select field features (58) used to determine the first path based on the second path.

The invention discloses an unmanned aerial vehicle cooperative inspection route optimization method and device and electronic equipment, and relates to the field of unmanned aerial vehicle inspection, and the method comprises the steps: carrying out the deep cleaning, anomaly detection, multi-source feature fusion and dimension reduction, space-time calculation and result mapping of multi-source heterogeneous data of a to-be-inspected region; carrying out fusion, electromagnetic interference evaluation and optimization on the generated dynamic environment probability map and the flight risk evaluation model, carrying out key waypoint segmentation extraction and trajectory adjustment on the improved route scheme, generating a dynamic route adjustment scheme, and carrying out cooperative distribution of multiple unmanned aerial vehicle tasks on the dynamic route adjustment scheme, so as to obtain a dynamic route adjustment scheme; and carrying out route optimization on the distributed multi-unmanned aerial vehicle cooperative inspection scheme, and carrying out multi-dimensional flyability analysis and adaptive optimization on the multi-unmanned aerial vehicle cooperative inspection scheme to obtain an unmanned aerial vehicle inspection route. According to the invention, the technical problem of low efficiency and quality of unmanned aerial vehicle inspection due to the fact that the unmanned aerial vehicle inspection route only focuses on optimization of a single factor in the prior art is solved.

The invention discloses a path planning method and device for an unmanned ship, a storage medium and equipment, and relates to the technical field of traffic ships, and the method comprises the steps: building an environment map according to the current position information of a ship, the position information of a destination, the state information of the ship and the position information of an obstacle; based on ship motion characteristic constraints, an initial path point sequence is generated through an improved progressive optimal fast exploration random tree algorithm, and the waypoint deviation risk is eliminated. And carrying out motion constraint perception path optimization processing on the initial path point sequence to generate a smooth path which can be directly and efficiently executed by a tracking controller. A plurality of groups of optimized path point sequences are obtained based on ship characteristic parameters with different constraint degrees, the limitation that a path is difficult to adapt to complex sea conditions under a single motion constraint working condition is broken through, finally, path quality multi-target comprehensive evaluation is performed on the plurality of groups of optimized path point sequences, and an optimal target planning path is obtained through screening. The problems of insufficient feasibility and low efficiency of ship path planning are solved.

The invention provides a transformer substation unmanned aerial vehicle three-dimensional route real-time planning and autonomous obstacle avoidance system, and relates to the technical field of non-electrical variable control or adjustment systems. Through cooperative work of a navigation mode switching module and a near-field navigation control module, an electric field of equipment is used as a natural navigation beacon; and high-precision autonomous flight is realized in a strong electromagnetic interference area with RTK signal failure, and the dependence on external positioning is eliminated, so that the inspection blind area is eliminated. Meanwhile, a fusion environment map containing multi-physics field information is constructed, the value of observation points is scientifically quantified according to waypoint inspection efficiency indexes, and a path planning algorithm aims at optimizing a comprehensive target fusing energy consumption, risks and inspection efficiency. The design not only ensures that the unmanned aerial vehicle can safely get close to the core equipment, but also enables the planned route to be the most valuable path rather than the shortest path, and finally fundamentally improves the safety, the operation efficiency and the data validity of substation inspection.

The invention provides a visual language navigation method and system based on a dynamic grid map, and belongs to the technical field of navigation. Comprising the following steps: extracting visual language features of an RGB panoramic image by adopting a CLIP model; combining the visual language features and the depth features of the depth image with the absolute coordinates of the grid units to construct a grid map; updating the grid map according to the semantic correlation between the grid unit and the navigation instruction; obtaining a cross-modal interaction feature corresponding to the grid map based on a cascade attention mechanism, and analyzing the cross-modal interaction feature by using a Mama module to output a predicted waypoint; taking DD-PPO as a local strategy, taking the predicted waypoint as input to carry out action analysis, and generating probability distribution; and selecting and executing a navigation action according to the probability distribution. According to the invention, on the basis of avoiding long-distance dependence, the dynamic continuous environment change can be quickly responded, so that accurate visual language navigation for the intelligent agent is realized.

A computer-implemented method when executed by data processing hardware causes the data processing hardware to perform operations. The operations include receiving a navigation route for a mobile robot. The navigation route includes a sequence of waypoints connected by edges. Each edge corresponds to movement instructions that navigate the mobile robot between waypoints of the sequence of waypoints. While the mobile robot is traveling along the navigation route, the operations include determining that the mobile robot is unable to execute a respective movement instruction for a respective edge of the navigation route due to an obstacle obstructing the respective edge, generating an alternative path to navigate the mobile robot to an untraveled waypoint in the sequence of waypoints, and resuming travel by the mobile robot along the navigation route. The alternative path avoids the obstacle.

The invention discloses a substation unmanned aerial vehicle inspection dynamic route planning method and device. The method comprises the steps of collecting and constructing a substation 3DGS model, optimizing details, dividing areas, recognizing equipment parts, inversely calculating waypoint parameters, planning waypoints of a safety channel and a flight channel, generating a basic model data set, and dynamically generating an inspection route in combination with tasks. The device comprises a model construction module, a region division module and the like. On the basis of a 3DGS model and equipment part identification, through a multi-level waypoint system and a dynamic planning algorithm, full-process automation from model construction to route generation is achieved, it is ensured that unmanned aerial vehicle routing inspection is safe and efficient, equipment updating and task changing are adapted, the problems that traditional two-dimensional planning is insufficient in precision, and static route flexibility is poor are solved, and the unmanned aerial vehicle routing inspection efficiency is improved. And the automation and intelligence level of substation inspection is improved.

The invention relates to a method for automatically correcting and updating the route of an electric power inspection unmanned aerial vehicle, which comprises the following steps of: S1, collecting and integrating geographic information system (GIS) data of an inspection area, and constructing a topological graph; s2, obtaining detailed point cloud data of the ground and the power grid facilities, and constructing a 3D model according to the obtained point cloud data; s3, based on the topological graph and the 3D model, extracting a key position from the 3D model by using an algorithm, determining an initial waypoint, extracting waypoints from the key position of the power grid equipment, connecting all the extracted waypoints by using a path planning algorithm, and generating an initial inspection route; s4, correcting the initial inspection route in combination with real-time weather, equipment priority and historical data; and S5, combining the combination degree RTK GPS and an environment adaptive VIO algorithm to realize sub-meter positioning precision, and after the key position centralization is successful, automatically updating the waypoint and immediately uploading the waypoint to a control center to realize automatic updating of the route. The accuracy and efficiency of unmanned aerial vehicle inspection are effectively improved.

The invention provides a visual language navigation method and system for a continuous environment, and relates to the field of visual language navigation, based on a language instruction and an environment topological graph initialized to be null, an intelligent agent autonomously navigates to a specified position in an unknown continuous environment by adopting a step-by-step navigation mode, and the navigation efficiency is improved. The specific operation of each step-by-step navigation comprises the following steps: acquiring a local panoramic image and a current environment topological graph of the current position of the intelligent agent; deadlock detection is carried out based on meta-cognitive monitoring, candidate waypoints in the local panoramic image are identified if deadlock is not detected, node features of the candidate waypoints are generated and added to the current environment topological graph, and the optimal waypoint is selected from the current environment topological graph; generating a bottom action instruction of the current position of the intelligent agent through an active reasoning obstacle avoidance method based on a depth map; according to the invention, meta-cognitive monitoring, gating multi-modal fusion decision and inference-reaction active obstacle avoidance are organically combined, and a systematic solution is provided for visual language navigation in a continuous environment.

The invention provides an unmanned aerial vehicle route planning method for hydropower engineering, and relates to the technical field of hydropower engineering safety monitoring. The method comprises the steps of generating a three-dimensional terrain grid model corresponding to a target operation area, performing voxelization processing to obtain an initial voxel map, and determining a first candidate waypoint by combining the initial voxel map; based on the average normal vector distribution of the initial voxel map, segmenting and refining the voxel units to generate a fine voxel map, and determining second candidate waypoints in combination with the fine voxel map; performing collision detection on all candidate waypoints to obtain a target waypoint set; and generating an unmanned aerial vehicle operation route according to the target waypoint set. According to the scheme, the multi-level voxel map is constructed and optimized, candidate waypoint screening and space collision detection are combined, and unmanned aerial vehicle patrol monitoring route planning which adapts to complex terrains and is complete in coverage and high in feasibility is achieved at low calculation cost.

The invention provides an airspace gridding method based on flight time, electronic equipment and a storage medium, and relates to the technical field of airspace gridding, and the method comprises the steps: obtaining a flight path RE of a target unmanned plane; determining the level L of the airspace grid corresponding to the RE; obtaining each L-level target airspace grid corresponding to the RE to obtain an airspace grid list A; determining an L-level code of each target airspace grid to obtain a target airspace grid code list GL; according to the waypoint information corresponding to the RE, determining the time when the target unmanned aerial vehicle flies to each target airspace grid so as to obtain a to-be-coded time list t; encoding each time to be encoded in the t to obtain an L-level time encoding list TL; adding the TLi into the GLi to obtain a target airspace grid code containing the flight time; according to the invention, whether the airspace grid is occupied or not can be determined according to the corresponding time coding information, so that collision is avoided.

The invention discloses a visual language navigation method and system based on a monocular camera and a language instruction, and the method comprises the steps: S1, feature field construction: employing a 3DGS to construct a 3DGS feature field by employing visual information obtained by a monocular RGB-D camera; s2, implicit partial complementation: inferring the representation of a missing region through a context feature relationship to generate a complete feature map; s3, waypoint prediction: generating an aerial view feature map based on the 3DGS feature field, and predicting nearby navigable waypoints through a waypoint predictor; s4, based on active perception of uncertainty, when the robot is uncertain in navigation decision, more visual information is acquired and missing visual information is supplemented by rotating a camera; and S5, constructing a topological map based on the constructed panoramic feature map and the predicted path points, and carrying out navigation decision making by using a panoramic visual language navigation planning model in combination with a language instruction. The problem that monocular vision information is incomplete is effectively solved, and the success rate and efficiency of navigation are remarkably improved.

The invention discloses a route planning method and device for non-stop inspection of a fan, relates to the technical field of fan inspection, and mainly aims to perform accurate route planning on non-stop inspection of the fan and improve the efficiency and accuracy of non-stop inspection of fan blades. According to the main technical scheme, the method comprises the following steps: determining a shooting course corresponding to a mobile shooting terminal according to a windward angle of a target fan, wherein the windward angle is determined by performing key area identification and line connection on radar point cloud data of the target fan; determining waypoint dwell time according to the rotating speed of the target fan, wherein the rotating speed is determined based on position deviation calculation of adjacent images in the blade image data of the target fan; determining the number of waypoints and the distance between the waypoints according to the shooting range of the mobile shooting terminal and the blade length of the target camera; and deploying shooting waypoints in the shooting course according to the waypoint number, the waypoint spacing and the waypoint staying time to obtain a shooting route of the mobile shooting terminal. The method is used for non-stop routing inspection of the fan.

The invention relates to the technical field of unmanned aerial vehicle obstacle avoidance route optimization, and particularly discloses an unmanned aerial vehicle obstacle avoidance route optimization method based on an improved ant colony algorithm, comprising: generating an initial unmanned aerial vehicle obstacle avoidance route based on the improved ant colony algorithm and a pre-acquired current environment model; sequentially judging whether the unmanned aerial vehicle arrives at a terminal point and whether an obstacle is detected, and triggering an improved ant colony algorithm to re-plan a route based on two judgment results; the re-planned route is optimized for three times, and it is verified that the optimized route is safe; updating the current environment model; whether the unmanned aerial vehicle arrives at the terminal point or not and whether the obstacle is detected or not are judged in sequence again, and the optimization method is ended based on two judgment results. According to the invention, the obstacle avoidance route can be rapidly generated for a complex dynamic environment. On the premise of ensuring obstacle avoidance safety, redundant waypoints are eliminated, and smooth and stable flight conditions of the unmanned aerial vehicle are created.

The invention discloses a multi-specialty and multi-nest scheduling method and related device based on electric power joint inspection, and the method comprises the steps: formulating the inspection strategy of each specialty of transmission, transformation and distribution to design an inspection plan, and integrating the inspection plan into an unmanned aerial vehicle joint inspection plan library to obtain an inspection task set; clustering a plurality of types of routing inspection tasks with similar features in the routing inspection task set by adopting a clustering algorithm to obtain a routing inspection flight task consisting of a plurality of devices; based on the established waypoint of the inspection flight task and the endurance time of the unmanned aerial vehicle, generating an inspection route of the unmanned aerial vehicle through a TSP algorithm; and based on the inspection route of the unmanned aerial vehicle, performing one-to-one matching on each inspection flight task and each unmanned aerial vehicle nest by adopting a greedy algorithm, and finally forming automatic scheduling of the unmanned aerial vehicle nest. The problems that in the prior art, routing inspection tasks are distributed unreasonably, cross-professional multi-nest routing inspection is not fully considered, and systematic and comprehensive research on the whole process of routing inspection operation is lacked are solved.

The invention relates to an air-ground collaborative exploration method in an unknown environment based on three-dimensional Gaussian splashing, and belongs to the field of air-ground robot collaborative exploration in a dangerous environment. The method comprises the following steps: S1, acquiring image data of a to-be-explored area by using an unmanned aerial vehicle, describing a complex scene and a differentiable rendering technology by using three-dimensional Gaussian distribution, accurately estimating a camera pose of the unmanned aerial vehicle and reconstructing a three-dimensional environment in real time by combining a simultaneous localization and mapping (SLAM) system, traversing a space and marking a series of key waypoints; step S2, the ground robot realizes accurate exploration of a local three-dimensional environment by using waypoints provided by the unmanned aerial vehicle and combining a collaborative mechanism of global planning and local planning; and S3, dynamically updating the path plan through the robot in the exploration process, and constructing a fine three-dimensional map of the exploration area. According to the invention, the air-ground robot can collaboratively explore the unknown environment and establish the environment map.