23803 results about "Aerospace engineering" patented technology

Systems and methods related to unmanned aerial vehicle (UAV) landing platforms are provided. In one example, a system includes a platform adapted for launching and / or landing a UAV. A plurality of folding elements are pivotably coupled to the support plate to move between closed and open positions, the closed position holding the UAV to the support plate, and the open position allowing the UAV to launch from and / or land on the support plate. One or more motors are configured to move at least one of the plurality of folding elements from the open position to the closed position to align the UAV on the support plate.

The invention discloses an obstacle avoidance method and device for unmanned aerial vehicle power inspection autonomous flight based on a laser radar and a storage medium, and relates to the technical field of unmanned aerial vehicle obstacle avoidance, and the method comprises the steps: configuring the laser radar and a visual sensor on an unmanned aerial vehicle, and collecting the three-dimensional point cloud data and image data of a flight environment in real time; optimizing the point cloud data to generate a three-dimensional environment model; and based on the three-dimensional environment model, classifying the obstacles into static obstacles and dynamic obstacles, if the obstacles are static obstacles, identifying the types of the obstacles through a CNN model and performing positioning, and if the obstacles are dynamic obstacles, clustering motion tracks through a K-Medoids clustering method. According to the invention, static obstacles can be identified and positioned, and motion trail prediction can be carried out on dynamic obstacles such as flying birds through a dynamic obstacle prediction technology, so that a preliminary flight path is generated, surrounding obstacles are sensed and avoided in real time in the flight process, and it is ensured that the unmanned aerial vehicle can be smoothly executed in an electric power inspection task.

The invention relates to a mobile structure for dispatching and accommodating drones, comprising a landing platform, a lifting unit for adjusting the height of the landing platform, and a storage unit. The lifting unit allows lifting and lowering of the landing platform up to a height from which a drone can take off and land (take-off height), and to a height in which the landing platform can transfer a drone to a storage level of the storage unit or receive the drone from a storage level (storage level height).

The invention relates to a landing platform for a drone (6), comprising a landing surface comprising two conveyor belts (2) which are arranged parallel in the same horizontal plane and which are spaced apart such that a drone (6) standing on the landing surface can simultaneously have contact with both conveyor belts (drone conveyor belts), two sliding bars (3) which each extend at least partially parallel to the running direction of the drone conveyor belts (2) and can be moved toward each other above the drone conveyor belts in a direction perpendicular to the running direction of the drone conveyor belts so that the sliding bars can slide a drone (6) standing on the landing surface, and a loading unit (5) comprising a lifting device (7) for cargo, the lifting device being arranged between the drone conveyor belts, wherein the drone (6) standing on the landing surface can be moved, by movement of the drone conveyor belts (2) and / or movement of the sliding bars (3), into a position in which the drone (6) can receive cargo arranged on the lifting device (7) (loading position).

The invention discloses a self-adaptive control unmanned aerial vehicle flight path correction method and system, and relates to the technical field of unmanned aerial vehicle flight control, and the method comprises the steps: collecting flight environment data through a sensor group carried by an unmanned aerial vehicle, constructing a three-dimensional environment model, and dividing the three-dimensional environment model into space units, the real-time pose of the unmanned aerial vehicle is determined based on point cloud matching and pose estimation technologies, trajectory offset is predicted according to a preset flight path, flight trajectory deviation parameters are generated, and a triaxial compensation strategy of horizontal plane, vertical direction and lateral offset is analyzed through trend analysis of the deviation parameters. And finally performing dynamic compensation correction on the flight path of the unmanned aerial vehicle according to the three-axis adjustment amount. The technical problem that the real-time accurate correction efficiency of the flight path of an existing unmanned aerial vehicle in a complex environment is low is solved, and the technical effect of shortening the dynamic compensation response time of the flight path deviation of the unmanned aerial vehicle is achieved through an industrial automatic control system device manufacturing technology and an intelligent monitoring device.

Methods, systems and apparatus, including computer programs encoded on computer storage media for unmanned aerial vehicle flight for performing an inspection of land, property, structures or other objects. Automated aerial surveys allow a UAV to obtain aerial data without human manual control of a UAV. For certain aerial surveys, a UAV is not capable of completing the survey without refueling, or exchanging out used batteries for fresh batteries. For such aerial surveys, a method and system is needed to allow an operator to automatically perform an aerial survey while determining battery usage and replacement. Also, in circumstances where manual control of the UAV is needed, contingency-based software controls may be needed.

Disclosed herein are a system and a method for controlling multi-UAV cooperative tracking of a ground-moving target. The system includes UAVs, a processor, and a storage device. The UAVs are wirelessly communicatively connected to the processor. The processor is configured to generate a UAV command transmitted to the UAVs. The UAVs are configured to transmit tracking data to the processor. The processor includes a terrain building module, a flight simulation module, a UAV parameter generation module, a flight trajectory prediction module, a flight trajectory determination module, and a flight trajectory control module. The system provided in the embodiments of the present disclosure integrates Model Predictive Control (MPC) with an Improved Whale Optimization Algorithm (IWOA) to enhance the algorithm's convergence speed and precision, thereby improving the accuracy and stability of target tracking.

The invention discloses an electric power inspection system and method based on an unmanned aerial vehicle, which are used for solving the problem of how to realize autonomous path planning of the unmanned aerial vehicle in a complex three-dimensional environment so as to reduce redundant flight time and improve inspection efficiency. The electric power inspection system comprises a path planning unit, an obstacle avoidance positioning unit and a dynamic obstacle avoidance unit which are connected in sequence. Three-dimensional map data and real-time environment information of a to-be-inspected power line are obtained through a path planning unit, real-time path planning is carried out based on the three-dimensional map data and the real-time environment information, and an optimal flight path of the unmanned aerial vehicle is dynamically generated; obstacle avoidance positioning is carried out on the power equipment on the optimal flight path and the surrounding environment thereof through an obstacle avoidance positioning unit, and real-time positioning information of an obstacle is generated; and a dynamic obstacle avoidance unit constructs a dynamic obstacle map based on the real-time positioning information, and automatically plans an obstacle avoidance strategy according to the dynamic obstacle map, so that the unmanned aerial vehicle realizes dynamic obstacle avoidance based on the obstacle avoidance strategy.

A base station is disclosed for an unmanned aerial vehicle (UAV) that includes: an enclosure defining a window that is configured to receive the UAV to allow for entry of the UAV into the base station and exit of the UAV from the base station; a door that is movably connected to the enclosure such that the door is repositionable between a closed position and an open position; a sealing member that extends about the window and which is configured for engagement with the door so as to form a seal therewith in the closed position; and a heating system that is supported by the enclosure and which is configured to heat the door and / or the sealing member to support operation (e.g., opening and closure) of the door in a cold environment, wherein the heating system includes at least one light source and at least one heating element.

A control method and system for precise landing of an unmanned aerial vehicle. The method comprises the following steps: on the basis of a multi-sensor fusion technique, using a lidar and a stereo vision algorithm to collect environmental data and perform primary processing, and generating comprehensive environmental perception data (step S1); on the basis of the comprehensive environmental perception data, using a digital elevation model algorithm to re-construct a three-dimensional terrain of a landing area, and generating a three-dimensional terrain model (step S2); on the basis of the three-dimensional terrain model, using an A* search path-planning algorithm to perform risk assessment and plan a safe landing path, and generating an optimized landing path (step S3); on the basis of the optimized landing path, using an adaptive control algorithm to adjust a flight attitude in real time by means of a fuzzy logic controller, and generating flight parameter adjustment (step S4); on the basis of the flight parameter adjustment, using machine vision and a decision tree algorithm to execute autonomous obstacle avoidance and emergency response, and generating a safe landing execution scheme (step S5); and on the basis of the safe landing execution scheme, using an ultrasonic sensor and a ground feedback system to confirm and fine-adjust a landing point, and completing final landing confirmation (step S6).

The invention relates to the field of unmanned aerial vehicle automation, in particular to a three-dimensional modeling and unmanned aerial vehicle landing area address selection method based on laser point cloud data. The method comprises the steps of collection and dynamic compensation of laser radar point cloud data, multi-scale adaptive registration of three-dimensional point cloud, topographic feature extraction and evaluation, selection of an optimal landing point, path planning based on an improved A algorithm, and real-time environmental data updating and dynamic adjustment mechanism. According to the method, point cloud data is dynamically compensated through Kalman filtering, a 3D-NDT algorithm is utilized to perform high-precision three-dimensional environment modeling, key topographic features are extracted through an RANSAC algorithm to comprehensively evaluate topographic complexity and safety distance, an optimal landing point is selected, an optimal path of an unmanned aerial vehicle is planned in combination with an improved A algorithm, and path safety and flight energy efficiency are ensured. By updating environment data in real time and dynamically adjusting a flight path and a landing point, the unmanned aerial vehicle is adapted to sudden environment changes, and safe and stable autonomous landing of the unmanned aerial vehicle in a complex and changeable environment is ensured.

The invention relates to the technical field of unmanned aerial vehicle control, and discloses an unmanned aerial vehicle inspection control system and method based on data analysis, and the method comprises the steps: receiving environment feature data of a target region, and constructing a static environment model; obtaining kinetic parameters of the unmanned aerial vehicle, and calculating a feasible flight envelope in combination with the static environment model; after taking off, the unmanned aerial vehicle collects environment data in real time, and a multi-level environment topology model is constructed; acquiring state data in real time, and judging an obstacle avoidance level; if yes, local path planning is carried out, and an optimal flight path is generated; acquiring trajectory deviation and energy consumption data, dynamically calibrating the flight envelope, and optimizing the flight envelope; according to the method, environment perception, trajectory optimization and online learning are combined, the method is adaptive to a complex environment, and the intelligent level of unmanned aerial vehicle inspection is improved.

A base station is disclosed for an unmanned aerial vehicle (UAV). The base station includes: an enclosure; a slide mechanism that is connected to the enclosure and which is repositionable between a retracted position and an extended position; and a cradle that is connected to the slide mechanism and which defines a chamber that is configured to receive the UAV such that the UAV is movable into and out of the enclosure during repositioning of the slide mechanism between the retracted position and the extended position. The cradle includes: an upper shell; a lower shell that is connected to the upper shell; and at least one thermal insulator that is located between the upper shell and the lower shell.

The invention discloses an unmanned aerial vehicle path planning method and system based on digital twinning. The method comprises the following steps: constructing a three-dimensional environment model based on first target environment data in first target multi-source environment perception data; constructing a digital twin based on the geometric model, the physical model, the control model, the target physical data hybrid dynamic model and the three-dimensional environment model; inputting the second target multi-source environment sensing data into the digital twin, and dynamically simulating the flight behavior of the physical unmanned aerial vehicle based on the second target multi-source environment sensing data to obtain simulated flight environment state data; and based on the simulated flight environment state data, performing path planning on the physical unmanned aerial vehicle in combination with a prediction control algorithm and a path planning algorithm to obtain a target flight path, transmitting the target flight path to the physical unmanned aerial vehicle, and controlling the flight of the physical unmanned aerial vehicle. The method can dynamically simulate the real-time change of the physical unmanned aerial vehicle, plan the optimal flight path and effectively avoid obstacles, and can be widely applied to the technical field of unmanned aerial vehicle path control.

The invention relates to the technical field of unmanned aerial vehicles, and discloses an unmanned aerial vehicle parking apron dynamic landing point adjustment method and system based on environment awareness, and the method comprises the steps: obtaining environment data and flight parameters, constructing a three-dimensional terrain grid model, and analyzing the stability; loading obstacle data and path constraints to generate a comprehensive evaluation model, and simulating to obtain regulation and control data; training the model to generate initial adjustment parameters, and combining the parameters with the model to obtain dynamic adjustment simulation information; constructing a multi-path optimization model to obtain optimal parameters; and combining real-time sensing to generate a safety probability, and adjusting a landing strategy. The system comprises a data acquisition module, a terrain modeling module and the like. According to the scheme, intelligent optimization and real-time adjustment of a landing point are realized through multi-dimensional environment perception and dynamic modeling, the landing safety and reliability of the unmanned aerial vehicle in a complex environment are improved, and the method is suitable for an intelligent landing scene of the unmanned aerial vehicle.

The invention discloses a low-altitude unmanned aerial vehicle flight statistical system, and relates to the technical field of air traffic management. A flight statistical system of a low-altitude unmanned aerial vehicle comprises a multi-source sensing fusion module, a twin airspace modeling module, a behavior portrait modeling module, a non-cooperative target recognition module, a flight statistical analysis module and a self-adaptive scheduling module. According to the invention, a three-dimensional twin airspace model of a monitoring area is constructed based on high-precision map data, a building three-dimensional model and flight information, and space mapping and collision risk analysis of a flight path are carried out, so that real restoration of the flight path and environment collision prediction are realized; the behavior feature vector is generated and the random forest algorithm is adopted to carry out classification modeling, and the behavior portrait of the unmanned aerial vehicle is constructed, so that accurate identification and classification of the behavior pattern of the unmanned aerial vehicle are realized.

The invention provides an unmanned aerial vehicle autonomous flight path planning and image acquisition method and system, and relates to the technical field of unmanned aerial vehicle inspection. Firstly, through intelligent three-dimensional grid division and dynamic route planning, the coverage rate and efficiency of feature region monitoring are remarkably improved; secondly, due to the integration of real-time meteorological data and historical feature data, the pertinence and adaptability of acquisition are enhanced, and high-quality images can be obtained in a complex and changeable environment; through combination of real-time processing and ground deep learning analysis, the accuracy and efficiency of feature extraction are greatly improved; and on the basis of a self-adaptive supplementary sampling mechanism of the region of interest, quick response and accurate positioning of the suspected feature region are realized, and reliable data support is provided for subsequent detailed analysis and decision making. The method is not only suitable for identification tasks of various preset features, but also has good expansibility and flexibility, and can be adjusted and optimized according to different application requirements.

The invention discloses an unmanned aerial vehicle low-altitude intelligent collaborative management system based on dynamic airspace gridding, and relates to the technical field of unmanned aerial vehicle management. Comprising the following steps: acquiring real-time environment information of a low-altitude airspace and operation state information of an unmanned aerial vehicle group; performing dynamic airspace gridding processing according to the real-time environment information to obtain a plurality of airspace grid units and airspace attribute information; according to the airspace grid units, the airspace attribute information and the operation state information of the unmanned aerial vehicle group, a cooperative flight path planning scheme is generated, and the technical key points are as follows: the information acquisition link provides comprehensive and accurate basic data, the dynamic airspace gridding processing realizes flexible adaptation of the airspace, and the cooperative path planning ensures efficient and ordered group flight. Flight control and dynamic adjustment guarantee smooth propulsion of tasks; all links support and promote each other, for example, a prediction result of the airspace state advanced judgment model can provide prospective guidance for path planning, and a deviation monitoring and adjusting mechanism can correct flight deviation in time.

The present application relates to a method and a system for intercepting and controlling target-drones (2), allowing to control the target-drone's flight route (4), causing it to land in a predetermined landing position. For that purpose, the method of the invention provides the configuration of flight routes (4) for controlling the target-drone's flight path in order to direct it to the landing position, without causing any physical damage to the target-drone (1). This control is achieved by using police-drones (1) which are adapted to transmit redirection signals (3) used as a way to program a new flight route (4) for the at least one target-drone (2) to a landing position.

The invention relates to the technical field of unmanned aerial vehicle control, and provides a route-free automatic inspection system, which comprises an unmanned aerial vehicle, an airborne box, an autonomous flight module, a visual acquisition module, an environment sensing module, a control instruction module and a position evaluation module, and is characterized in that the airborne box is loaded in the unmanned aerial vehicle so as to acquire environment data in the flight process of the unmanned aerial vehicle in real time; the control instruction module receives coordinates of a target area input by a user and issues the coordinates of the target area to the unmanned aerial vehicle, the environment sensing module obtains the coordinates of the target area and environment data and dynamically calculates a pre-planned path, and the position evaluation module performs evaluation according to the real-time position of the unmanned aerial vehicle and the pre-planned path to form an evaluation result. The environment sensing module is triggered to dynamically adjust a pre-planned path according to an evaluation result and adjust to form a flight route, the autonomous flight module controls the unmanned aerial vehicle according to the flight route, and the visual acquisition module identifies the state of an inspection object in real time and executes corresponding shooting or video recording operation.

The invention discloses a multi-unmanned aerial vehicle combined flight control system coping with wind power change, and relates to unmanned aerial vehicle flight control, and the system comprises a flight data collection module which is used for collecting unmanned aerial vehicle flight data in real time, and the unmanned aerial vehicle flight data comprises wind power data, attitude angle data and height data of each unmanned aerial vehicle; the data receiving and processing module is electrically connected with the flight data acquisition module, and the data receiving and processing module is used for receiving unmanned aerial vehicle flight data acquired in real time and performing noise removal on the acquired data. According to the multi-unmanned aerial vehicle combined flight control system provided by the invention, the wind field prediction model is constructed by combining the LSTM neural network with the time attention mechanism, wind power periodic change characteristics are accurately captured, real-time correction is performed in cooperation with the Kalman filter, the prediction precision and timeliness are both optimized, and the system is suitable for large-scale popularization and application. A multi-unmanned aerial vehicle cooperative kinetic model is constructed based on a prediction result, so that the formation trajectory tracking error is reduced.

The invention relates to the technical field of unmanned aerial vehicle clusters, in particular to an unmanned aerial vehicle cluster formation method. Comprising the following steps: system initialization and configuration, cluster communication and data exchange, collaborative algorithm calculation and control input, positioning and navigation assistance, global and dynamic task planning setting, energy management and optimization strategy implementation, and biological enlightenment formation control application. Compared with a traditional formation operation method, the unmanned aerial vehicle cluster formation method is higher in autonomy, and the unmanned aerial vehicle cluster formation is more accurate through an advanced autonomous flight control system and a sensor technology. Information sharing and cooperative actions among the unmanned aerial vehicles in the cluster formation are realized through an efficient communication technology, and even if part of the unmanned aerial vehicles fail, other unmanned aerial vehicles can still continue to execute tasks.

The disclosure provides a method and system for trajectory tracking control of a vehicle-manipulator coupling system with finite time prescribed performance. Specifically, a coupling weaken trajectory planning method is designed to reduce the system's coupling effects. A finite time performance function is designed to constrain the trajectory tracking error. In a case the constraint conditions corresponding to the finite time performance function are satisfied, the trajectory tracking error is converted to obtain a transformed error. The sliding mode surface is designed based on the transformed error to control the transformed error to converge in a finite time, and the external disturbance of the vehicle-manipulator coupling system is observed based on non-linear disturbance observer. The control input of the vehicle-manipulator coupling system is designed based on the sliding mode surface and the non-linear disturbance observer output. This ensures that the vehicle-manipulator coupling system can operate precisely along the desired trajectory.

The invention relates to the technical field of unmanned aerial vehicle positioning, in particular to an unmanned aerial vehicle flight path positioning method and system based on multi-source information fusion. The method comprises the following steps: acquiring an original observation data stream of an unmanned aerial vehicle in real time through a sensor array, and preprocessing to output a multi-source data sample set; calculating relative motion increment between adjacent key frames through an IMU pre-integration model, and extracting local pose estimation through a visual geometric calculation method; evaluating the confidence of each sensor in real time to dynamically generate an adaptive weight coefficient; and constructing a tight coupling fusion filter taking position, speed and attitude errors as core state quantities, and outputting a global optimal trajectory of the unmanned aerial vehicle. According to the invention, the weight is dynamically distributed through the real-time confidence of the sensor, the multi-source data association is fully mined, and the flight path positioning precision and environmental adaptability of the unmanned aerial vehicle are improved.

The invention relates to the technical field of unmanned aerial vehicle track control, in particular to an unmanned aerial vehicle track control method and system. The method comprises the following steps: firstly, acquiring flight braking parameters and distribution task data of an unmanned aerial vehicle, and extracting low-altitude distribution meteorological condition feature data; then, flight state simulation is carried out based on flight braking parameters and task data, simulation quantification is carried out on flight attitude instability in combination with meteorological conditions, and then trajectory avoidance behavior control learning is carried out; and finally, constructing a trajectory control model by applying a strategy gradient algorithm, and sending the trajectory control model to an unmanned aerial vehicle control center to execute trajectory control. According to the invention, the unmanned aerial vehicle trajectory control technology is optimized, so that the unmanned aerial vehicle trajectory control technology is more perfect.

The invention discloses a power construction unmanned aerial vehicle path planning method and system, and relates to the technical field of space calculation, and the method comprises the following steps: determining a target region needing power construction and a preset construction point coordinate; obtaining a topographic map of the target area based on the geographic information database, constructing a three-dimensional map model, and marking the preset construction point coordinates; generating an initial construction path of the unmanned aerial vehicle; the method comprises the following steps: acquiring meteorological data in real time through meteorological data of a meteorological station in a target area, establishing a meteorological obstacle three-dimensional model, and dividing a preset radius threshold with a meteorological obstacle as a circle center into non-flying areas according to the severity level of the meteorological obstacle; adjusting the initial construction path based on the non-flying area; the cruise height is adjusted according to the meteorological data and topographic relief, a relief tracking path is generated, and the constant relative height is kept; and combining the battery capacity, the flight speed and the task priority, dynamically distributing inspection road sections, and generating a corresponding power construction unmanned aerial vehicle flight path.

Inspection robots with a payload engagement device are described. An example inspection robot may have a housing, a drive module, having at least one wheel and a motor, where the drive module is operatively coupled to the housing. The example inspection robot may also have a payload coupled to the drive module, where the payload includes a sensor mounted to the payload, and a payload engagement device operationally coupled to the drive module and the payload, where the payload engagement device applies a selected downward force on the payload.

Resonant sensors for environmental health risk detection are disclosed. A mechanical member may include at least one meso-scale or micro-scale resonator disposed on a surface of the mechanical member. Additionally, the at least one meso-scale or micro-scale resonator may include a plurality of first carbon particles configured to uniquely resonate in response to an electromagnetic ping based at least in part on a concentration level of the first carbon particles within the at least one meso-scale or micro-scale resonator. Further, the at least one meso-scale or micro-scale resonator may be configured to resonate at a first frequency in response to the electromagnetic ping when the mechanical member is in a first state, and may be configured to resonate at a second frequency in response to the electromagnetic ping when the mechanical member is in a second state.

The invention discloses an autonomous obstacle avoidance and path optimization control method for an unmanned aerial vehicle in a complex environment. The method comprises the following steps: A, establishing a simulation environment containing static obstacles and dynamic obstacles; b, obtaining a group of path schemes approaching an optimal solution and a plurality of candidate paths based on a particle swarm algorithm and the initial position of the unmanned aerial vehicle; c, based on a multi-agent depth deterministic strategy gradient algorithm, constructing a state space, an action space and a reward function used for training an obstacle avoidance motion strategy of multiple unmanned aerial vehicles; d, a multi-agent depth deterministic strategy gradient algorithm is used for training, and a path planning strategy is optimized; and E, evaluating scores of all candidate paths through a reinforcement learning mechanism, and calculating to obtain an unmanned aerial vehicle flight path with the highest comprehensive score as an optimal path. According to the invention, a safe and efficient flight path can be quickly and accurately planned for the unmanned aerial vehicle in a complex dynamic scene, and efficient obstacle avoidance of the unmanned aerial vehicle in the complex dynamic scene is realized.

The invention relates to the technical field of mechanical arm control, in particular to an unmanned aerial vehicle mechanical arm control system for power transmission line inspection. The method comprises the following steps: the distortion correction module is used for collecting and processing a patrol video and generating a corrected image sequence, the pose estimation module is used for performing three-dimensional pose estimation on a corrected image and performing motion trail fitting, and the extension evolution module is used for performing entity reconstruction and extension evolution on a fitting trail, so as to obtain a corrected image sequence; the extension trajectory data generation module is used for generating extension trajectory data, the constraint analysis module is used for performing constraint analysis based on a trajectory model, generating a feasible trajectory and synchronizing with an inspection video to obtain a synchronous trajectory positioning point, and the control planning module is used for performing inspection route planning according to the feasible trajectory and controlling an unmanned aerial vehicle mechanical arm in real time to complete an intelligent inspection task. According to the invention, the reliability of unmanned aerial vehicle inspection is improved.