506 results about "Flight speed" patented technology

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.

The invention provides a control method and system for an unmanned aerial vehicle to check and hit a ground station. The method comprises the following steps: firstly, acquiring real-time image data, carrying out terrain contour comparison, identifying a disaster risk area, secondly, generating a task mark file, compressing the task mark file into an encrypted data packet with a geographic label, then sending the encrypted data packet to a ground station, simultaneously receiving a rescue priority instruction, and adjusting the hovering height and the inspection navigational speed of the unmanned aerial vehicle. The method comprises the steps of receiving a device ready state code, sending the device ready state code to a ground station, verifying the integrity of the device ready state code to generate an action authorization identifier, controlling an unmanned aerial vehicle to fly to a safe coordinate, controlling the unmanned aerial vehicle to execute a task when a positioning signal transmitter receives a synchronous verification signal, and automatically deleting data after the task is completed; according to the technical scheme provided by the invention, rapid response, accurate execution and data closed-loop management of the unmanned aerial vehicle to the multi-target area in a disaster rescue scene are realized.

The invention relates to the technical field of unmanned aerial vehicle positioning, in particular to an unmanned aerial vehicle positioning method and system combining environment characteristics. According to the technical scheme, the method comprises the steps that multi-modal data of the environment where the unmanned aerial vehicle is located are collected through a multi-source heterogeneous sensor, and the multi-modal data comprise GNSS signal data, visual image data, laser radar point cloud data, inertia measurement data and electromagnetic interference monitoring data; and carrying out cooperative processing on the multi-modal data by utilizing the fusion deep learning model. A comprehensive basis is provided for subsequent processing through multi-source data collection, data quality and feature matching reliability are improved through fusion processing, timely and accurate positioning is guaranteed through real-time calculation, safe and efficient flight is achieved through a decision-making model based on accurate positioning, and the flight speed is high. The method not only solves the traditional technical problems of signal shielding, interference, dynamic interferent influence and contradiction between real-time performance and precision in a complex environment, but also shows excellent positioning stability and adaptability in various complex scenes.

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 relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle dynamic obstacle avoidance method based on multi-sensor fusion. Comprising the steps of receiving an image sequence, a depth point cloud set, a pose state parameter and a flight speed parameter; calculating an illumination distortion gradient value and a speckle noise entropy value based on the image sequence and the depth point cloud set; performing coordinate registration and feature extraction on the image sequence, the depth point cloud set and the pose state parameter, and outputting a visual feature set and a radar feature set; generating a weighted visual feature set and a weighted radar feature set through a weight adjustment function, and generating a joint heterogeneous feature tensor through alignment splicing; and generating a body attitude and thrust control instruction by using the combined heterogeneous feature tensor through a probability prediction model and a model prediction control algorithm device. According to the method, through cross-domain multiplexing and deep coupling of the body physical parameters in the data stream, error accumulation and decision delay caused by multi-stage series calculation are avoided, and global performability of an obstacle avoidance task is facilitated.

The invention relates to the technical field of aerospace, and discloses an ALOT-based low-altitude intelligent obstacle avoidance route planning system, which comprises a data collection module, a data calculation module, a feedback module, a feedback optimization module and an optimization management module, and is characterized in that the data collection module is responsible for collecting various environmental information required in the flight process of an unmanned aerial vehicle; the data calculation module performs calculation and analysis by using an algorithm based on the data collected by the data collection module to generate a preliminary dynamic route planning model, the feedback module performs real-time monitoring and feedback on a calculation result of the data calculation module and evaluates the flight state of the unmanned aerial vehicle, and the feedback optimization module adjusts a flight strategy according to a feedback result. According to the method, obstacle avoidance and route planning are optimized, optimization measures for adjusting the flight speed and changing the route are put forward in time, an optimization management module manages and implements the optimization measures, it is ensured that the unmanned aerial vehicle flies safely and efficiently according to the optimized route, meanwhile, the implementation effect of the optimization measures is monitored, and effective adjustment is conducted.

The invention discloses an unmanned aerial vehicle cluster relay system optimization method, and aims to significantly reduce the overall outage probability of the system by performing adaptive optimization on cooperative position deployment and flight trajectories of multiple unmanned aerial vehicles under multiple actual constraint conditions of flight speed, safety distance, flight boundary and the like. Therefore, the reliability and the service quality of the multi-unmanned aerial vehicle relay communication network are improved. According to the method, a Markov decision process (MDP) model is constructed, a multi-unmanned aerial vehicle trajectory optimization problem is converted into a multi-agent reinforcement learning problem, and efficient optimization of unmanned aerial vehicle trajectories is realized by using a centralized training-distributed execution framework, so that the outage probability of a communication system is minimized.

The invention provides a path planning and energy management cooperative regulation and control method for a multi-agent unmanned aerial vehicle, and the method comprises the following steps: building a kinetic model based on a six-degree-of-freedom motion equation of a four-rotor unmanned aerial vehicle, and building an energy consumption model through combining the power of a motor and the discharge characteristics of a battery; sensing the state of the unmanned aerial vehicle in real time, defining a state space and an action space of a deep reinforcement learning algorithm, and generating navigation points by using the deep reinforcement learning algorithm; receiving a delivery request, and dynamically allocating tasks according to the state of the unmanned aerial vehicle by adopting a deep reinforcement learning algorithm; performing path planning on the unmanned aerial vehicle for task allocation by adopting an A * algorithm according to the navigation points in combination with the multi-dimensional cost, and obtaining an optimal path; dispersing the optimal path into a speed instruction, and dynamically adjusting the course angle and the flight speed of the unmanned aerial vehicle through a PID controller; through combination of deep reinforcement learning and an A * algorithm, the problems of path planning and energy management of the multi-agent quad-rotor unmanned aerial vehicle logistics distribution system in complex terrains are effectively solved.

The invention discloses an unmanned aerial vehicle continuous speed space gesture control system and method based on a somatosensory glove. The system comprises a gesture data acquisition module, a data transmission module and a flight control execution module. The gesture data acquisition module comprises a palm data acquisition module and a palm data resolving module. The current attitude quaternion is used for calculating the thumb orientation to control the flight direction, the finger bending angle is converted into the flight speed, the thumb direction is used as the flight direction, the finger bending degree is converted into the flight speed, unmanned aerial vehicle glove control of continuous speed direction space and large and small space is achieved, computing resource overhead is reduced, and the working efficiency is improved. Meanwhile, the limitation of a traditional unmanned aerial vehicle remote controller is overcome, the control efficiency is improved, the method has the advantages of being visual and convenient to operate, rapid in response, accurate in control and the like, the method is suitable for flexible control scenes of the unmanned aerial vehicle, interaction immersion and friendliness are enhanced, and user experience and flight safety are improved.

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.

The invention provides an attitude control method and device for a vertical take-off and landing unmanned aerial vehicle, equipment and a medium, relates to the technical field of flight control law design, and is used for solving the problem of how to realize rapid transition of the vertical take-off and landing unmanned aerial vehicle from a horizontal state to a vertical state. The method comprises the following steps: determining a current stage of the vertical take-off and landing unmanned aerial vehicle in a transition process according to a pitch angle and a flight speed of the vertical take-off and landing unmanned aerial vehicle; the transition process refers to the process of transition from a horizontal state to a vertical state; determining a pitch angle speed control target according to the current stage; a proportional integral differential control law structure is adopted, and a pitching control surface control instruction is determined according to the pitching angular velocity control target and the pitching angular velocity; the vertical take-off and landing unmanned aerial vehicle can be controlled to be rapidly transited to a vertical state from a horizontal state through a pitching control surface control instruction.

The invention provides a bird body collecting device for engine bird impact test speed calibration, which comprises a mounting bottom plate and a buffer collector, and is characterized in that the collector comprises a collecting guide pipe, and the guide pipe comprises a vertical section, a bent section and a horizontal section which are communicated in sequence; a buffer assembly is arranged at the bottom of the vertical section and fixed to the bottom plate, and a bird body divider is arranged at an opening of the horizontal section. The front cabin is fixedly connected with the opening of the horizontal section, a speed monitor and an electromagnetic switch device are arranged in the front cabin, an anti-splashing cabin door is arranged at the inlet end of the front cabin, the electromagnetic switch device is connected with the anti-splashing cabin door, and the speed monitor is used for monitoring the flight speed of a bird body and is connected with the electromagnetic switch device; when the speed signal is monitored, the electromagnetic switch device is triggered to control the anti-splashing cabin door to be closed. The device has the advantages of miniaturization, automation, good buffering effect, easiness in operation and the like, and solves the problems of large bird impact, difficulty in residue collection and the like during bird speed calibration.

The invention discloses a meta universe task offloading and resource scheduling method based on deep reinforcement learning, and relates to the technical field of wireless communication, and the method comprises the steps: 1, putting an unmanned aerial vehicle cluster in a user dense region, and enabling a fixed edge server to obtain the position coordinates of the fixed edge server, each user and each unmanned aerial vehicle; 2, a user sends a rendering request to the fixed edge server through the unmanned aerial vehicle cluster; step 3, constructing an optimization target, converting the optimization target into a Markov decision process, solving the optimization target through a PPO algorithm, and obtaining a horizontal flight rate, a flight direction and a vertical direction rate of the unmanned aerial vehicle and a computing resource set available for edge equipment; step 4, the fixed edge server issues a decision result to the user and the unmanned aerial vehicle, and the user and the unmanned aerial vehicle execute a decision; and step 5, after the unmanned aerial vehicle completes the rendering task, returning a result to the user. The method has the characteristics of multi-node collaborative optimization, energy consumption optimization and resource utilization rate optimization.

The invention discloses an unmanned aerial vehicle control system and method for agricultural seeding, relates to the technical field of unmanned aerial vehicle control, and solves the technical problems of seed falling path deviation and seeding precision reduction caused by the fact that route generation depends on preset topographic data and position points but real-time environment change is not considered in the prior art. The first flight height of the unmanned aerial vehicle at the current wind speed is obtained; acquiring the real-time horizontal offset of the unmanned aerial vehicle based on the first flight height; based on the first flight height and the real-time horizontal offset, determining a target position of the seed falling in the to-be-sown area; the actual depth of the seeds embedded into the soil is calculated through the kinetic energy theorem; judging whether the actual depth is within a preset normal embedding depth range or not; the real-time flight speed of the unmanned aerial vehicle in the flight process is obtained, and the unmanned aerial vehicle is controlled to complete sowing according to the real-time flight speed and the real-time flight speed; according to the invention, the deviation of a seed falling path is corrected, and the seeding precision is improved.

The invention discloses an unmanned aerial vehicle variable-speed aerial photography method and device for multi-class ground feature elevation and a medium, and relates to the technical field of photogrammetry, and the method mainly comprises the steps: taking the texture of each direction of a static target as an information collection demand, and generating a cross route with mutually vertical horizontal planes as initial route data; controlling the unmanned aerial vehicle to fly according to the initial route data, and shooting a sequence image at a forward-looking preset angle; integrating the digital earth surface model data and the sequence image, and extracting an elevation data set of each static target in the sequence image; according to the elevation data set, analyzing the elevation distribution proportion change of the static targets in the adjacent sequence images, and carrying out speed adjustment along with the proportion change; and generating final route data according to the speed regulation result, and controlling the unmanned aerial vehicle to perform variable-speed aerial photography according to the final route data. The flight speed of the unmanned aerial vehicle is dynamically adjusted, dense collection of high-rise building images is guaranteed, and redundant data in low areas are avoided.

The invention relates to a tilt rotorcraft semi-physical simulation system and method, and relates to the technical field of wind tunnel flight test semi-physical simulation. Based on the initialized system with the test parameters set, six-component balance load, wind speed and attitude data are collected in real time, and the collected key physical data are filtered and then sent to the flight simulation subsystem; the flight simulation subsystem carries out calculation based on a flight dynamics model, a rotor wing power model, a control instruction preprocessing model and the received data and outputs an actuation instruction; the actuator receives an actuation instruction and drives the aircraft model, and the airborne sensor feeds back new attitude data to form a control closed loop; the flight simulation subsystem sends state data to the 3D visual simulation module in real time to realize synchronization of control and visual; in this way, the stable state under the combination of different tilting angles and flight speeds is recorded, and a tilting transition corridor is obtained through fitting. Therefore, simulation precision, test safety and iteration efficiency are taken into consideration, and fused semi-physical simulation is formed.

The invention discloses a large fixed-wing unmanned aerial vehicle fire extinguishing performance evaluation test method, and relates to the field of aviation equipment. The method solves the problem that quantitative evaluation of fire extinguishing agent throwing precision and effective coverage range of a large fixed-wing unmanned aerial vehicle in a complex wind field environment is difficult, scale test parameter design is carried out based on similarity criteria and reference wind tunnel size, and then test model (unmanned aerial vehicle and fire extinguishing agent water tank) and adjustable cabin door structure design are carried out. The unmanned aerial vehicle model is installed on the upper tunnel wall of a wind tunnel through a lifting supporting rod in a back supporting mode, the fire extinguishing agent measuring system is installed on the central axis of a wind tunnel test section and is vertically aligned with a water tank, finally a large fixed-wing unmanned aerial vehicle fire extinguishing performance evaluation wind tunnel test is carried out, the effective range of a fire extinguishing agent is quantitatively treated, and the fire extinguishing performance is evaluated. The test method can effectively research the fire extinguishing performance of the large unmanned aerial vehicle under the influence of the flight speed, the launching angle, the height and the like of the unmanned aerial vehicle.

A method 400 of capturing and processing electroluminescence (EL) images 1910 of a PV array 40 is disclosed herein. In a described embodiment, the method 400 includes controlling the aerial vehicle 20 to fly along a flight path to capture EL images 1910 of corresponding PV array subsections 512b of the PV array 40, deriving respective image quality parameters from at least some of the captured EL images, dynamically adjusting a flight speed of the aerial vehicle along the flight path, based on the respective image quality parameters for capturing the EL images 1910 of the PV array subsections 512b, extracting a plurality of frames 1500 of the PV array subsection 512b from the EL images 1910; determining a reference frame having a highest image quality of the PV array subsection 512b from among the extracted frames 2100; performing image alignment of the extracted frames 2100 to the reference frame to generate image aligned frames 2130, and processing the image aligned frames 2130 to produce an enhanced image 2140 of the PV array subsection 512b having a higher resolution than the reference frame. A system, image processing device, and aerial vehicle for the method thereof are also disclosed.

The invention relates to the technical field of trailing vortex dynamic monitoring, in particular to a short-distance parallel runway wake flow dynamic prediction method based on deep learning, which comprises the following steps of: acquiring disturbance energy level and coordinate direction and fitting propagation state, extracting disturbance source features and combining with spatial direction constraint, fitting boundaries according to propagation included angle and speed change rate and classifying, and obtaining a dynamic prediction result. Acquiring a track coordinate course angle and a flight speed, calculating a track vector and a boundary normal included angle, analyzing an interference risk factor in combination with a ratio of the speed to a propagation direction, dividing disturbance levels, and outputting a preliminary prediction result. According to the method, the state vector and the disturbance data cooperatively extract features and constrain the direction, the disturbance source positioning precision is improved, the boundary is described by combining the speed change rate and the propagation included angle, the recognition robustness is enhanced, the risk level is evaluated by fusing multi-dimensional parameters, the disturbance intensity is dynamically corrected, the track deviation is introduced, and the prediction accuracy and the response efficiency are improved.

The invention relates to the technical field of unmanned aerial vehicle path planning, in particular to an unmanned aerial vehicle path planning method and system based on laser ranging, and the method comprises the steps: generating point cloud data for the flight environment of an unmanned aerial vehicle in real time, obtaining the distance between each point cloud and the unmanned aerial vehicle at each moment in each control period, and determining the distance between each point cloud and the unmanned aerial vehicle; the point cloud resolution and the point cloud signal-to-noise ratio of each moment, and the flight speed of the unmanned aerial vehicle at each moment; calculating the environment complexity of each moment; calculating a flight margin of each moment to obtain a flight risk value of each moment; determining an attenuation evaluation value at each moment, and determining an environment interference degree at each moment; and the adjustment coefficient of each moment is obtained, the step length of the fast expansion random tree algorithm corresponding to the next control period is adjusted, and the flight path of the unmanned aerial vehicle is planned by using the fast expansion random tree algorithm. By dynamically adjusting the step length of the fast expansion random tree algorithm, efficient and safe path planning can be realized in a complex and dynamic flight environment.

The invention provides a single microwave radiation driven unmanned aerial vehicle flight planning method with optimal energy utilization, and the method comprises the following steps: 1, obtaining unmanned aerial vehicle basic design parameters, state initial parameters, and an unmanned aerial vehicle lift-drag ratio relational expression for a given unmanned aerial vehicle; calculating a climbing angle boundary and a flight speed boundary of the unmanned aerial vehicle by adopting an aircraft performance estimation method; 2, establishing a mathematical relationship between the position, the roll angle, the pitch angle and the yaw angle of the unmanned aerial vehicle and the energy consumption of the unmanned aerial vehicle at the current time step; 3, calculating the position, the yaw angle and the flight speed of the unmanned aerial vehicle in the next time step from the initial state based on the basic design parameters and the initial state parameters of the unmanned aerial vehicle; based on the mathematical relation established in the second step and the minimum energy consumption of the current time step, the roll angle and the pitch angle of the unmanned aerial vehicle in the next time step are obtained, and the position, the attitude angle, the flight speed and the energy consumption of the unmanned aerial vehicle in each time step are recorded until the flight task is completed finally.

The invention relates to an unmanned aerial vehicle control method considering weather risks, and the method comprises the steps: collecting the flight environment data of an unmanned aerial vehicle in real time, synchronously receiving a cloud evolution map and a wind shear early warning signal of a ground radar, and carrying out the processing, thereby obtaining multi-mode meteorological data; predicting storm intensity distribution in the track area in 2-5 minutes in the future, and generating a three-dimensional threat level grid map; aiming at the conventional meteorological risk, constructing a threat level-evasion strategy mapping table, judging the threat level, calling a corresponding evasion strategy, and guiding the unmanned aerial vehicle to execute a corresponding evasion action; when encountering an extreme condition, activating an anti-interference navigation mode, enhancing flight stability through rotating speed-pitch angle cooperative control, and triggering a standby landing emergency protocol; after the avoidance action is completed, the flight path is re-planned in combination with space-time constraints, and the flight speed is reasonably adjusted. The invention also relates to an unmanned aerial vehicle control system considering the weather risk. According to the invention, the flight safety and stability of the unmanned aerial vehicle under complex weather conditions can be improved.

The invention relates to an unmanned aerial vehicle aerial photography flight parameter optimization method, device and equipment and a medium, and the method comprises the steps: constructing a multi-target optimization model according to an aerial photography quality evaluation function and an aerial photography efficiency evaluation function, initializing each particle in a particle swarm through employing a preset flight parameter optimization algorithm, according to boundary conditions and target output conditions, the multi-target optimization model is solved to determine a pareto optimal solution set, the boundary conditions comprise flight height constraints and flight speed constraints, and the target output conditions represent the maximum value of an aerial photography quality evaluation function and the minimum value of an aerial photography efficiency evaluation function; the pareto optimal solution set comprises a plurality of optimal flight parameter combinations; and controlling the unmanned aerial vehicle to carry out aerial photography according to the optimal flight parameter combination in the pareto optimal solution set so as to complete flight parameter optimization of aerial photography of the unmanned aerial vehicle. According to the invention, by optimizing the flight parameters and the aerial photography path, the precision and efficiency of image data acquisition can be improved.

The invention discloses an unmanned aerial vehicle autonomous route planning method and system capable of adapting to environmental changes. The method comprises the steps of obtaining a dynamic three-dimensional situation map of a flight area of an unmanned aerial vehicle based on multi-source data of the flight area of the unmanned aerial vehicle; acquiring a current no-fly zone and weather forecast information of the flight area of the unmanned aerial vehicle, and analyzing and acquiring a flight risk distribution condition of the flight area of the unmanned aerial vehicle based on the dynamic three-dimensional situation map; considering the flight risk distribution condition of the unmanned aerial vehicle flight area, the unmanned aerial vehicle flight task and the unmanned aerial vehicle self-state information to generate an unmanned aerial vehicle reference route; and automatically adjusting the flight speed, the flight height and the flight course of the unmanned aerial vehicle according to a preset flight parameter adjustment model based on the acquired multi-source data and the state information of the unmanned aerial vehicle in the process of flying along the reference route. According to the invention, the reliability and safety of flight task execution of the unmanned aerial vehicle in a complex dynamic environment can be improved.

Embodiments of the present invention provide a flight control method and apparatus for an unmanned aerial vehicle, a storage medium and an electronic apparatus. The method includes: acquiring a target control request of the unmanned aerial vehicle during execution of target flight tasks; detecting current energy consumption data of the unmanned aerial vehicle in response to the target control request, the current energy consumption data indicating a current energy consumption condition of the unmanned aerial vehicle; calculating remaining navigation parameters of the unmanned aerial vehicle according to the current energy consumption data and current ground speed, the remaining navigation parameters indicating a navigation time of the unmanned aerial vehicle before landing, the current ground speed indicating a current flight speed of the unmanned aerial vehicle relative to ground; and controlling the unmanned aerial vehicle to execute remaining flight tasks in the target flight tasks according to the remaining navigation parameters.

The present application relates to a kind of unmanned aerial vehicle control method considering weather risk, including the following steps: real-time acquisition unmanned aerial vehicle flight environment data, synchronous receipt cloud layer evolution map and ground radar's wind shear early warning signal, process to obtain multimodal meteorological data;Predict the storm intensity distribution in future 2-5 minutes track area, and generate three-dimensional threat level grid map;For conventional meteorological risk, build threat level-avoidance strategy mapping table, judge threat level, and call corresponding avoidance strategy, guide unmanned aerial vehicle to execute corresponding avoidance action;When encountering extreme situation, activate anti-interference navigation mode, enhance flight stability by rotating speed-pitch angle collaborative control, trigger emergency protocol of alternate landing;After completing avoidance action, re-plan flight path in combination with space-time constraint, reasonably adjust flight speed.The present application also relates to a kind of unmanned aerial vehicle control system considering weather risk.The present application can improve the flight safety and stability of unmanned aerial vehicle under complex weather conditions.

The invention relates to the field of RCS testing, in particular to a dynamic RCS testing system and method based on an unmanned aerial vehicle. The system comprises an unmanned aerial vehicle platform subsystem, a ground measurement radar subsystem and a comprehensive control and data processing subsystem. The unmanned aerial vehicle platform subsystem is composed of an unmanned aerial vehicle carrier, a target installation mechanism and a first differential GNSS module. The first differential GNSS module is used for acquiring space three-dimensional coordinates, flight speed and course of the unmanned aerial vehicle and a target; the ground measurement radar subsystem is composed of an RCS measurement radar, a transmitting antenna, a receiving antenna and a second differential GNSS module; the ground measurement radar subsystem is composed of an RCS measurement radar, a transmitting antenna, a receiving antenna and a second differential GNSS module. The comprehensive control and data processing subsystem is composed of a flight control unit, a radar control unit, a data synchronization unit and a data processing and inversion unit. The method is suitable for the RCS test.

The invention belongs to the technical field of unmanned aerial vehicle line simulation, and provides a power transmission line unmanned aerial vehicle line simulation flight navigation control method and system. The method comprises the following steps: pre-planning a reference flight path according to a power transmission line coordinate of a to-be-simulated line of an unmanned aerial vehicle; on the reference flight path, determining a detection frame coordinate center position of the power transmission line according to the infrared image and the visible light image of the power transmission line, and dynamically predicting the position of the unmanned aerial vehicle according to a change trend of the detection frame coordinate center position of the power transmission line and a slope change trend of a fitting straight line of the power transmission line; extracting temperature gradient field characteristics in a detection frame of the power transmission line, calculating a main direction angle of the power transmission line according to the temperature gradient field characteristics, and calculating a direction angle of the power transmission line by combining with a slope of a fitting straight line of the power transmission line; and controlling the flight speed on each coordinate axis of the unmanned aerial vehicle coordinate system according to the direction angle of the power transmission line, the dynamically predicted unmanned aerial vehicle position and the preset flight speed.

The application is suitable for the field of flying cars, and provides a vertical take-off and landing flying car, which comprises a car body, a main lift assembly arranged on the car body and placed inside the car body, a folding wing assembly arranged on the car body and placed on the top of the car body, and a back thrust power assembly arranged on the car body and placed on the top of the tail end of the car body. The application aims to solve the technical problems in the prior art that one kind of flying car is vertically taken off and landed, but does not have a fixed-wing flight mode, provides lift by a rotor, is more like a variant of a helicopter, has a small flight speed, a short flight range, and a complex control; another kind of flying car is taken off by sliding, has a main wing for generating lift, but has a long sliding distance for taking off and landing, has a high requirement for a landing site, and greatly limits the use scene.

The invention relates to an aircraft conflict detection method based on high-precision data. The method comprises the following steps: firstly, acquiring high-precision operation data such as real-time three-dimensional coordinates and flight speed of an aircraft; the overlapping risk of the spatial dimension is identified through a three-step method including line segment intersection judgment, height difference comparison and actual spatial distance accounting; the time dimension is combined with the flight speed, the course deviation correction coefficient and the airspace environment resistance coefficient to calculate the estimated time for reaching the potential conflict point, and early warning is triggered through a time difference threshold value; according to the method, a space-time detection result is fused to generate a conflict level, then a calculation priority is adjusted based on a computing power optimization strategy, a conflict avoidance instruction is output, and the method aims at solving the problems that a traditional method in a million-level aircraft data scene is large in calculation amount and insufficient in computing power, and existing airspace gridding detection is poor in dynamic trajectory adaptability and low in precision.