171 results about "No-fly zone" patented technology

Systems, methods, and devices are provided for providing flight response to flight-restricted regions. The location of an unmanned aerial vehicle (UAV) may be compared with a location of a flight-restricted region. If needed a flight-response measure may be taken by the UAV to prevent the UAV from flying in a no-fly zone. Different flight-response measures may be taken based on the distance between the UAV and the flight-restricted region and the rules of a jurisdiction within which the UAV falls.

The invention provides an unmanned aerial vehicle controlling method, a controlling device and an unmanned aerial vehicle. The method comprises the steps that the position information, which is transmitted by the unmanned aerial vehicle, of the unmanned aerial vehicle is received; whether a no-fly zone exists within the range of the preset distance from the position information of the unmanned aerial vehicle is determined; if the no-fly zone exists within the range of the preset distance from the position information of the unmanned aerial vehicle, whether the unmanned aerial vehicle is in the no-fly zone is determined; if the unmanned aerial vehicle is in the no-fly zone, a flight-prohibited instruction is generated; and the flight-prohibited instruction is sent to the unmanned aerial vehicle, so that the unmanned aerial vehicle executes an operation indicated by the flight-prohibited instruction. According to the invention, the flight operation of the unmanned aerial vehicle can be regulated to avoid safety damage caused by the fact that the unmanned aerial vehicle enters the no-fly zone.

The invention belongs to the technical field of unmanned aerial vehicle regulation and particularly relates to a no-fly zone unmanned aerial vehicle early-warning method. The method comprises the steps of firstly, acquiring a certain number of longitude and latitude coordinate points of an unmanned aerial vehicle flying in a current time period, through coordinate transformation, transforming the points to corresponding plane coordinate points in a rectangular plane coordinate system, then, using an improved least square method to conduct curve fitting on corresponding discrete plane coordinate points, fitting a flight path of the unmanned aerial vehicle in this period, finally, obtaining a tangent line of a current point of a fitting curve, and judging whether or not the unmanned aerial vehicle can enter a no-fly zone by judging whether or not the tangent line is intersected with an electronic fence for describing the no-fly zone. If yes, real-time distance between the current point and the no-fly zone is obtained and an alarm is given in time, and if not, prediction for the next moment is carried out. The no-fly zone unmanned aerial vehicle early-warning method has a wide application prospect in the field of civilian unmanned aerial vehicle regulation.

The invention discloses an unmanned aerial vehicle pseudo path planning method based on deep reinforcement learning, which comprises the steps of: dividing boundary coordinates of a no-fly area on a flight map and marking a start coordinate position and an end coordinate position of a flight task of an unmanned aerial vehicle; sensing the current environment state of the unmanned aerial vehicle before executing the flight task, and selecting a deflection angle and a flight action in the current environment according to an obtained Q function value by using a deep reinforcement learning algorithm; updating a Q function by the unmanned aerial vehicle according to reward return obtained by interaction between flight position data and the environment, wherein the flight position data is continuously received by the unmanned aerial vehicle in the flight process and is provided by ground base station transmitting equipment; judging whether the unmanned aerial vehicle flies according to a preset air route by using the no-fly area as a virtual barrier in the flight process; and if the unmanned aerial vehicle approaches the edge of the no-fly area, guiding the unmanned aerial vehicle to plan a pseudo navigation path through a reward function so as to avoid the no-fly area. The unmanned aerial vehicle pseudo path planning method of the invention realizes the pseudo path planning of the unmanned aerial vehicle in an unknown environment, and improves the intelligence and the safety of the flight of the unmanned aerial vehicle.

The invention provides an unmanned aerial vehicle, comprising a communication module, wherein the unmanned aerial vehicle communicates with a control terminal through the communication module; a positioning module for detecting the position information of the unmanned aerial vehicle; a storage module for storing pre-warning zone information and no-fly zone information; a control module which is used for judging whether the unmanned aerial vehicle enters into a pre-warning zone or a no-fly zone according to the position information of the unmanned aerial vehicle, sending a warning signal to the control terminal if the unmanned aerial vehicle is judged to enter into the pre-warning zone, and controlling the unmanned aerial vehicle to return to base or land if the unmanned aerial vehicle is judged to enter into the no-fly zone. Correspondingly, the invention also provides a control method of the unmanned aerial vehicle. The pre-warning zone information and the no-fly zone information are pre-stored in the unmanned aerial vehicle of the invention, whether the unmanned aerial vehicle enters into the pre-warning zone or the no-fly zone can be judged according to the position information of the unmanned aerial vehicle, targeted warning, returning or landing strategies are sent out, and the problem in the prior art that the unmanned aerial vehicle bursting into the no-fly zone is incapable of being effectively monitored and managed is solved.

The invention discloses an unmanned aerial vehicle route dynamic planning method based on A* search, which is used for solving the technical problem that the existing method has poor adaptability for the situation that long-strip shaped polygonal threatening regions or no-fly zones exist in the complex environment. The unmanned aerial vehicle route dynamic planning method comprises the steps of: constructing circumscribed circles of polygonal threatening regions or no-fly zones at first, and judging whether a line segment formed by a starting point and a target point intersects with each polygonal circumscribed circle; further judging whether the line segment and the polygonal threatening regions or no-fly zones have an intersection point or not if the line segment intersects with the polygonal circumscribed circles; and if so, planning and generating a route directly avoiding the polygonal threatening regions or no-fly zones based on an A* search algorithm, wherein a dead zone escaping and two-step optimizing strategies is adopted during the route planning process for planning and generating route points in sequence. The unmanned aerial vehicle route dynamic planning method can adapt to the situation that the long-strip shaped polygonal threatening regions or no-fly zones exist in the complex environment, and can adapt to the situation that the circular or polygonal threatening regions or no-fly zones are overlapped with one another.

The invention relates to a rotor unmanned aerial vehicle coverage search flight path planning method for a polygonal region having a no-fly zone, wherein the method comprises the steps: step 1, determining a coverage direction, wherein the direction from a polygon width-corresponding top point to edge is a coverage direction for realizing shortest-path polygon region coverage searching; step 2, transforming a coordinate, taking an X axis of a new coordinate system (OX'Y') as a coverage direction, and making an origin point of the new coordinate system (OX'Y') the same as an origin point of an original coordinate system; step 3, according to the transformed to-be-covered polygon region and obstacle, carrying out Boustrophedon searching unit decomposition; step 4, determining an alternative optical grating line search flight path set of each searching unit; step 5, determining the sequence and the flight path of each searching unit of the unmanned aerial vehicle based on a greedy strategy; and step 6, carrying out coordinate inverse transformation, transforming flight path points into flight path points in the original coordinate system through coordinate inverse transformation, and thus obtaining an original task searching flight path. The method ensures optimal property of understanding while reducing the time consuming of calculation.

The present invention relates to a flight forbidding control method and device of an unmanned aerial vehicle. The method includes the following steps that: when it is detected that an unmanned aerial vehicle application is started, a flight forbidding data group is obtained from a server end; the flight forbidding data group is sent to the unmanned aerial vehicle; the current location information of the unmanned aerial vehicle sent by the unmanned aerial vehicle is received; the flight range of the unmanned aerial vehicle is determined according to the current location information of the unmanned aerial vehicle; a target no-fly zone within the flight range is determined according to the flight range and the flight forbidding data group; and the identifier of the target no-fly zone is sent to the unmanned aerial vehicle; and the unmanned aerial vehicle carries out flight forbidding operation according to the identifier of the target no-fly zone and the flight forbidding data group. When a user starts the unmanned aerial vehicle application each time, a terminal device can obtain the latest flight forbidding data group, and therefore, the temporary control of a state on unmanned aerial vehicles can be responded quickly, and an entire flight forbidding process can be completed automatically without the manual setting of the user required, and therefore, better experience can be brought to the user.

The invention discloses a rapid three-dimensional track planning method of an unmanned aerial vehicle taking into account complex environment constraints. The rapid three-dimensional track planning method of the unmanned aerial vehicle taking into account the complex environment constraints comprises the steps that firstly, a three-dimensional track planning environment model is constructed by analyzing various constraint information of typical complex environments such as topography, meteorology, no-fly zones and dangerous substances on an unmanned aerial vehicle track, and then a multilayerexpansion A* algorithm is designed to perform three-dimensional expansion according to the performance constraints and flight characteristics of the unmanned aerial vehicle so as to search and obtaina reference track of the unmanned aerial vehicle; and finally, the reference track is simplified and optimized in line simplification and line smoothing modes to obtain an optimal flight track for theunmanned aerial vehicle. The unmanned aerial vehicle track can be efficiently and accurately generated, threats can be safely avoided, and the speed and accuracy of track planning are increased.

The invention provides an unmanned aerial vehicle, which comprises a communication module, a positioning module, a storage module and a control module, wherein the unmanned aerial vehicle communicates with an operation control terminal through the communication module; the positioning module is used for detecting positional information of the unmanned aerial vehicle; the positional information which is detected by the positioning module is sent to the operation control terminal through the communication module; a no-fly zone and an early warning zone are pre-stored in the storage module; the control module is used for calling the positional information which is detected by the positioning module and the no-fly zone and the early warning zone information which are stored by the storage module, judging whether the unmanned aerial vehicle enters the early warning zone or the no-fly zone according to the positional information of the unmanned aerial vehicle, sending a caution signal to the operation control terminal if judging that the unmanned aerial vehicle enters the early warning zone, and controlling the unmanned aerial vehicle to land if judging that the unmanned aerial vehicle enters the no-fly zone. The unmanned aerial vehicle solves a problem that the existing technology cannot effectively supervise the unmanned aerial vehicle intruding the no-fly zone.

The invention relates to a collaborative analysis reentry guidance method considering multiple no-fly zone constraint, which comprises the steps of 1, describing a reentry guidance problem; 2, providing an analytic solution of flight time based on a rotational globe model; 3, carrying out an analytic reentry guidance process considering multiple no-fly zone and arrival time constraint; 4, providing a multi-aircraft arrival time collaboration scheme. The method herein has the advantages that an exact analytic solution of hypersonic gliding trajectory flight time based on the rotational globe model, and a prediction error is kept less than 3% to adapt to conditions with non-constant vertical lift-drag ratio profile; the problem with collaborative flight allowing multiple hypersonic gliding aircrafts to be guided to arrive at a target at the same time in a multiple no-fly zone environment is solved on the basis of the analytic solution of three-dimensional reentry trajectory and the analytic solution of flight time; a multi-target numeric iteration plan based on online trajectory simulation considering terminal time, speed and height requirements is designed. A directional derivativeis sued to modify the quasi-newton method, and the quantity of trajectory simulations is decreased.

Systems and methods for preventing flight of one or more Unmanned Aerial Vehicles (UAVs) in no-fly zones include receiving one or more no-fly zones each defined as geo-fences with associated coordinates; preventing one or more UAVs from entering the one or more no-fly zones by one or more of: transmitting the geo-fences to the one or more UAVs, transmitting avoidance commands to the one or more UAVs from an avoidance device located at a no-fly zone, and disrupting radio communication to the one or more UAVs from the avoidance device.

The invention provides a wide-range cruising path planning model for a UAV (unmanned aerial vehicle) and a method. Firstly, according to problem description of a UAV cruising path in wide-range seas, determining a path planning model with the maximum effective cruising range and the shortest cruising operation time as goals, determining constraint condition models containing a cruising ability constraint condition model, a constraint condition model under environment influence such as weather and the like and a constraint condition model under influence of a no-fly zone according to objective constraint conditions of the UAV, and readjusting and determining a path planning model according to the constraint conditions; selecting the optimal path for cruising of the UAV in the wide-range seas from a search water area network according to the determined path planning model in combination with the various constraint conditions. The maximum key supervision water area range covered by the UAV can be realized, meanwhile, the shortest cruising distance is guaranteed in the cruising ability range of the UAV, and the optimal planning scheme is provided for wide-range path search of the UAV for maritime affairs.

The invention belongs to the technical field of UAV control, and provides a UAV flight avoidance control device, system and method that solve the technical problem of avoiding non-"no-fly zone" aircraft; the control device includes: an information sensing device, used To receive the broadcast information sent by the surrounding target equipment, and based on the broadcast information and the current position information of the drone, output the distance between the drone and the surrounding target equipment; the control device, when the When the distance between the drone and the surrounding target equipment is greater than the first threshold, a first early warning signal is output; when the distance between the drone and the surrounding target equipment is greater than the second threshold, a second early warning signal is output; wherein The content of the first early warning signal includes information that prompts the drone to avoid surrounding target equipment, and the content of the second early warning signal includes information that forces the drone to land; thereby avoiding collision accidents and flight safety problems caused by deliberate tracking .

The invention discloses an unmanned aerial vehicle autonomous flight method for constructing a three-dimensional real scene based on LiDAR data. The unmanned aerial vehicle autonomous flight method comprises the steps of constructing an autonomous flight no-fly zone, carrying out route autonomous planning and flying, and autonomously performing route correction and obstacle avoidance, wherein theno-fly zone is in the three-dimensional real scene constructed on the basis of LiDAR point cloud data, and a flyable block and a no-fly block are divided by using an algorithm; the route autonomous planning flight is the process of combining differential GPS with the LiDAR data and autonomously planning the optical flight route of unmanned aerial vehicle electric power inspection according to a specified principle and realizing autonomous flying; and the route correction and obstacle avoidance regards a planned flight path and airborne radar data as judgment standards, conducts flight path correction on the situation that flight deviates from the flight path due to internal and external factors, and can realize autonomous original-path return can be achieved when necessary. The unmanned aerial vehicle autonomous flight method solves problems of large line patrol potential safety hazards, low line patrol efficiency and redundant personnel allocation of an existing unmanned aerial vehicle.

The invention relates to an airspace management method and system. The method comprises: a pilot and an unmanned aerial vehicle are verified; if user verification is completed, airspace application is made in a non-no-fly zone; and if the airspace application is done successfully, flight recording is carried out. According to the airspace management method, any unmanned aerial vehicle flying into the airspace is controlled during the whole process and the flight plan and the flight range can be verified in advance, so that the need of entering the fused airspace by the unmanned aerial vehicle can be met and unverified flight can be prevented.

A system for tracking and counteracting flying insects includes an imager, a unmanned aerial vehicle with an embedded counteracting device and a processor.

The present invention discloses a control method for controlling the flight region of an unmanned plane. The method comprises an access method and an operation method for the flight no-fly zone of an unmanned plane. The no-fly zone has the following properties: number, type, height, speed-limiting factor, maximum constant speed line speed, and characteristic parameter. Based on the method, the no-fly zone can be drawn on a ground station and then transmitted to the flight controller of the unmanned plane. The no-fly zone may be a practical obstacle, and can also be a manually defined region. Meanwhile, a speed-limiting region running method is provided for preventing the unmanned plane to enter the no-fly zone. As a result, the unmanned plane is ensured not to enter the no-fly zone.

Embodiments of the invention relate to the technical field of electric power inspection, and disclose an electric power inspection method and system based on an unmanned aerial vehicle. The method comprises the following steps of importing an initial inspection route into an electronic map, wherein the electronic map comprises no-fly zone information; removing a no-fly zone in the initial inspection route according to the no-fly zone information in order to obtain a target inspection route, wherein the target inspection route comprises a plurality of power equipment; sending a flight control instruction to the unmanned aerial vehicle in order to control the unmanned aerial vehicle to carry out electric power inspection according to the target inspection route; receiving infrared temperature video images of the plurality of power equipment, which are shot by a camera carried by the unmanned aerial vehicle; and generating an inspection report according to the infrared temperature video images, wherein the inspection report at least comprises positions of defects of the plurality of power equipment and defect description. Therefore, the unmanned aerial vehicle is used for carrying outelectric power inspection and obtaining the more comprehensive defect information of the power equipment through the infrared temperature video images, so that the accuracy of an electric power inspection result can be improved.

The invention discloses an analysis reentry guidance method considering the restraint of multiple no-fly zones. To overcome the influences from earth rotation, conventional aerodynamic force and inertia force caused by earth rotation are combined into equivalent aerodynamic force. Then, the longitudinal range analytical solution is utilized for planning the equivalent longitudinal lift-drag ratioprofile, and accordingly, the longitudinal range and energy management requirements are met; the transverse range analytical solution is utilized for planning the equivalent transverse lift-drag ratioprofile meeting the no-fly zone restraint and transverse range requirements. An inverted point gradual increasing type analysis iteration planning strategy is designed and comprises a plurality of analysis methods which are used for solving the flight track point, closest to the no-fly zones, on the track, adjusting existing inverted points for avoiding the no-fly zones, increasing the inverted points to avoid the no-fly zones and planning the inverted points to eliminate the terminal course error. Iteration starts from two times of inversion, and then the number of inversion times is gradually increased according to the no-fly zone avoiding requirement. In addition, since the planning strategy is completely based on the analytical solutions, the planning strategy is extremely high in planning efficiency and can be executed online.

The invention discloses a robust optimization method for power and interference unmanned aerial vehicle tracks in a communication system, and belongs to the technical field of wireless communication.According to the method, a safety communication network model containing an artificial interference unmanned aerial vehicle is established; an information source node S sends confidential informationto an information sink node D, meanwhile, an eavesdropping node E eavesdrops the confidential information, and an unmanned aerial vehicle U serves as an artificial interference source to send an interference signal to an eavesdropping user in the flight process to prevent eavesdropping so as to improve the safety communication rate of the system. Meanwhile, the communication model contains a plurality of no-fly zones J, the system can only obtain part of position information of eavesdropping nodes, and the method can perform robust optimization on the secure communication system under the condition of incomplete CSI (channel state information), bypasses a specific area in the flight process of the unmanned aerial vehicle, and is closer to a practical application occasion.

The invention discloses a black zone-introduced express unmanned aerial vehicle track planning method based on an A<*> and ant colony hybrid algorithm. Firstly, the coordinates of an obstacle and thecoordinates of a site express cabinet are determined, a no-fly zone and the obstacle are ignored, and an ant colony algorithm is used to make a track; the starting point, the transit point and the endpoint of the route are confirmed, and an initial route is determined; net points are built, the length and the width of the net are both set to be S m, the margin of at least three net points is built along the edge of the obstacle or the no-fly zone, and the obstacle net and the surrounding margin net are defined as black zones; whether the section of each route passes through the obstacle is judged, if the route passes through the black zone, judgment is made starting from the starting point, an A<*> algorithm is used sequentially until the route bypasses all obstacles or no-fly zones, andexpress unmanned aerial vehicle track planning is completed. The black zones are introduced, the advantages of hybrid use of the A<*> and ant colony hybrid algorithm are integrated, the planned unmanned aerial vehicle track is more reasonable, the route is shorter, and the unmanned aerial vehicle consumes less energy.

The invention discloses an unmanned aerial vehicle multi-data-link intelligent switching method, and relates to the field of unmanned aerial vehicle data communication. Aiming at the defect of mechanical preset decision making under the condition that main communication links of a data transfer radio station, mobile communication and the like are disconnected in existing unmanned aerial vehicle multi-link communication. Beidou short message communication is utilized, and under the condition that the main communication links are completely disconnected, a disconnection information message is constructed and transmitted to a ground station end, and the ground station end receives a lost information message, recovers the flight dynamic state of the unmanned aerial vehicle before the unmannedaerial vehicle is lost, analyzes the reason of the unmanned aerial vehicle loss in combination with a signal distribution three-dimensional diagram, constructs a no-fly zone, and evaluates and decidesa flight control instruction to realize the recovery of the main communication link of the unmanned aerial vehicle.

The invention discloses an intelligent hypersonic aircraft reentry maneuver guidance method and system. The method comprises the following steps: establishing a hypersonic gliding aircraft reentry kinetic model based on a geocentric rectangular coordinate system; according to the hypersonic gliding aircraft reentry kinetic model, establishing a hypersonic gliding aircraft reentry maneuvering guidance problem model under multiple constraint conditions based on endpoint constraints and path constraints; for different no-fly zones, solving the hypersonic gliding aircraft reentry maneuvering guidance problem model under the multi-constraint condition by using a GPOPS II toolkit to obtain optimized trajectory data sets under different no-fly zone conditions; learning the optimized trajectory data set by using a deep learning algorithm to obtain an intelligent road waypoint model; and designing a maneuvering guidance law based on the intelligent road waypoint model, multi-antenna detection and sideslip angle transient. By means of the method and system, the expected maneuvering guidance effect of avoiding the no-fly zone can be achieved.