749 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.

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 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.

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.

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.

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 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 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 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.

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 discloses an unmanned aerial vehicle photovoltaic inspection real-time path planning method and system, and relates to the technical field of control in a specific dimension. Comprising the steps of planning an initial route and setting inspection routes in different areas; an image is collected by adjusting the camera pitch angle of the second holder, and the distance is measured in real time through the laser range finder; uploading an image shot by the second pan-tilt camera, identifying the photovoltaic string, establishing a string position correction model, and calculating the three-dimensional coordinate of the center point of the string; implementing local route optimization according to the string center point coordinate data set and the initial route based on an ant colony algorithm; the unmanned aerial vehicle flies to a set waypoint along a flight route, the first pan-tilt camera shoots an image, identifies a picture center photovoltaic string, calculates a center point coordinate difference, and the unmanned aerial vehicle corrects the position and executes an inspection image acquisition task. The problems of insufficient real-time performance, poor adaptability to complex terrains and unstable image acquisition quality are solved, and the purposes of strong real-time performance, strong adaptability, high image quality, high recognition capability and high precision are achieved.

The invention provides an unmanned aerial vehicle route planning operation safety monitoring system and method based on virtual-real fusion, belongs to the technical field of unmanned aerial vehicle safety monitoring, and aims to solve the problems that the test training difficulty of an unmanned aerial vehicle is increased and the experiment cost of the unmanned aerial vehicle is high due to interference and terrain transformation in the task execution process of the unmanned aerial vehicle. The safety monitoring system comprises a ground distributed semi-physical virtual-real fusion simulation platform and an air experiment module. The ground distributed semi-physical virtual-real fusion simulation platform is used for constructing a virtual test scene of task execution of the unmanned aerial vehicle, optimizing a waypoint planning decision according to feedback information in a task execution process, and performing safety judgment and real-time obstacle avoidance on the current waypoint planning decision so as to optimize the waypoint planning decision; and the space-time experiment module is used for monitoring the flight state of the unmanned aerial vehicle and executing the optimized waypoint planning decision.

The invention relates to an end-to-end automatic driving system based on multi-modal fusion, and the system comprises an environment sensing module which comprises a plurality of types of sensors and is used for collecting environment information of different modals; the feature extraction module is used for carrying out feature extraction on the environment information of different modes and converting the environment information into aerial view space to obtain multi-mode BEV features; the feature fusion module is used for fusing the multi-modal BEV features to obtain fused features; the behavior planning module is used for outputting a future waypoint of the target vehicle and a prediction result of a control instruction according to the fusion features by adopting a trained behavior planning network; and the output fusion module is used for carrying out track prediction and multi-step control prediction and selecting an optimal driving behavior based on a situation strategy. Compared with the prior art, the method has the advantages that the environment sensing precision and decision generalization ability in a complex scene are effectively improved, error accumulation between modules is reduced, and a more efficient solution is provided for end-to-end automatic driving.

The invention discloses an air route optimization method and system based on a genetic algorithm and geographic information, and relates to the technical field of intelligent navigation and path planning, and the method comprises the steps: receiving geographic data related to air route planning, and constructing a multi-level spatial index; a waypoint optimization algorithm is adopted, and under the support of spatial indexes, a genetic algorithm based on population diversity adaptive adjustment is utilized to determine a waypoint sequence of the route; for any two adjacent waypoints in the waypoint sequence, adopting a path optimization algorithm, combining geographic information provided by the spatial index, and utilizing a genetic algorithm to generate a corresponding optimal path segment; and carrying out iterative optimization on the optimal path segment, carrying out iterative optimization on the waypoint sequence and the optimal path segment, and integrating an optimization result to the autonomous navigation system after convergence. The spatial information processing efficiency is remarkably improved, high-risk and limited areas are effectively eliminated, and the optimal layout of a global route structure is realized.

The invention discloses an intelligent navigation method and device of an unmanned aerial vehicle, equipment and a storage medium. The method comprises the following steps: acquiring point cloud data and motion data, and calibrating to obtain a relative position and time synchronization parameter; performing map construction on the point cloud data and the motion data to generate a three-dimensional point cloud map; analyzing the boundary coordinates of the flight area, the flight height and the route step length, and generating route planning parameters; the boundary of the target inspection area is defined by combining the three-dimensional point cloud map, and an optimized route and corresponding waypoint sequence data are generated through route planning; acquiring real-time pose data of the unmanned aerial vehicle, comparing the current position with the waypoint sequence, and triggering image acquisition when a preset waypoint reaches a tolerance range; inspection data is obtained; and integrating the real-time pose data, the three-dimensional point cloud map, the flight state and the inspection data to form an intelligent navigation route. According to the invention, high-precision positioning and linkage of waypoints and data acquisition are realized, and the navigation precision, the inspection efficiency and the operation safety are improved.

The invention provides an aircraft guidance control method under constraint of passing points. The method comprises the following steps: step 1, discretizing a guidance control process; step 2, carrying out segmentation processing on the guidance process of the limited passing point; 3, solving a state transition control sequence between the passing points; 4, solving the passing speed of the passing point; and step 5, calculating an overall guidance process, dividing the whole guidance process into k relatively independent guidance processes according to the step 1 when the positions and the speed states of all the passing points are known, and performing guidance control between two adjacent passing points. According to the method, aircraft guidance control under guidance time and passing point constraints is realized, the coupling problem which is difficult to effectively solve in the prior art in the guidance problem is solved, a guidance relative motion track and guidance control cost can be accurately given, the control track is smooth, and the guidance control precision is high. The method is suitable for various guidance control processes based on model prediction and inversion, and an engineering implementation method can be provided for guidance control tasks of various scenes.

The invention provides a self-adaptive accurate rust removal method and system based on unmanned aerial vehicle visual feedback, and relates to the technical field of large metal structure surface maintenance and anticorrosion treatment.The rust removal method comprises the steps that a global path is planned, and a global flight waypoint sequence is generated; a local image of the current to-be-derusted area is collected; the corrosion characteristics of the current to-be-derusted area are analyzed, and initial derusting parameters and initial derusting time are set; after each derusting operation is finished, a frame of intermediate result image is collected; based on the difference between the intermediate result image and the historical image, performing quantitative evaluation on the effect of the previous rust removal operation, and performing feature analysis on the residual rust region; self-adaptively adjusting and generating rust removal parameters of the next stage; and the steps of derusting operation, image collection, evaluation and parameter adjustment are repeatedly executed, and after the termination condition is met, the unmanned aerial vehicle is switched to the next to-be-derusted area.

The invention relates to the technical field of unmanned aerial vehicles and bridge inspection, and discloses a bridge support inspection method based on an unmanned aerial vehicle. The method comprises the following steps: importing a bridge three-dimensional point cloud model and a reference slice elevation, and extracting a bridge pier contour; calculating an acquisition interval according to the load parameters, and determining a bridge pier center line as an inspection baseline; buffer inspection waypoints are generated on the two sides of the center line, and the nearest waypoint is selected as an inspection starting point; generating an environment map comprising the bridge piers and the surrounding preset range of the bridge piers; and planning a path by adopting an A-Star algorithm, and carrying out smooth processing through a B spline algorithm to generate an inspection path. Various technical means are fused, an efficient, safe and accurate bridge inspection solution is provided, the manual operation risk and cost are reduced, the inspection efficiency is improved, the flight stability is optimized, and technical support is provided for future intelligent bridge inspection.

The invention discloses a tilt rotor aircraft flight control method in a tilt transition stage, and the method comprises the steps: determining a reverse tilt state point which is a waypoint of which the to-be-fly distance to the starting point of a multi-rotor mode descent stage is greater than or equal to the shortest design deceleration distance and the height is H; and when the tilt rotor aircraft is located at the reverse tilt state point, controlling the tiltable rotors to tilt reversely according to the tilt angle instruction, and controlling the tilt rotor aircraft to fly forward according to the reverse tilt transition flight speed. In the reverse tilting transition stage, the tilting angle rate is calculated based on the to-be-fly distance, and corresponding flight control strategies are executed for different stages in the reverse tilting transition stage in combination with comparison of the to-be-fly distance and the forward distance threshold value and the tilting angle range, so that accurate control over the position of the aircraft in the reverse tilting transition stage is achieved.