118 results about "Flight distance" patented technology

The invention discloses a method for generating an autonomous inspection orthoimage of an unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicle surveying and mapping and autonomous navigation. Global optimization of an air route is realized through a path optimization strategy fused by a genetic algorithm in combination with grid characteristics of an inspection area and image parameter constraints, and candidate waypoints are used as nodes; redundant waypoints are screened through leg smoothness factors, route complexity is reduced, a genetic algorithm takes flight height and waypoint spacing as constraints, a fitness function containing flight distance, turning times and overlapping rate is constructed, a global optimal path is found through population iteration, multi-target requirements are optimized and balanced, and it is ensured that the route meets the image acquisition precision requirement and also meets the requirement of image acquisition. The flight distance can be shortened, the turning frequency is reduced, the cruising ability of the unmanned aerial vehicle is adapted, efficient propelling of the inspection task is guaranteed, meanwhile, waypoint coordinates output through simulation directly adapt to a flight control system, and it is guaranteed that actual flight parameters are consistent with planning parameters.

The invention discloses a double-layer and double-stage heterogeneous unmanned aerial vehicle task allocation and flight path planning method, and relates to the technical field of unmanned aerial vehicles. The method comprises a dual-stage task allocation method and a dual-stage path planning method. The beneficial effects of the invention are that the dual-stage task allocation method and the dual-stage path planning method are provided for improving the efficiency of the multi-unmanned aerial vehicle cooperative execution of the search rescue task; according to the dual-stage task allocation method, a joint optimization framework combining mixed integer linear programming and an improved ant colony algorithm is provided, so that the task load balance and the total flight distance can be optimized, and a better task allocation effect is realized; the dual-stage path planning method provides a dual-stage path planning scheme in which global task sequence optimization and local obstacle avoidance planning are coordinated, and a better path planning effect is obtained by combining the global and local dual-stage optimization scheme.

The invention provides an unmanned aerial vehicle distribution task scheduling optimization method considering an endurance state, and belongs to the field of intelligent manufacturing. The method comprises the following steps: aiming at the problems of takeoff sequence, task allocation and endurance state change of an unmanned aerial vehicle in a distribution task, constructing a power consumption model based on flight stage division; under constraint conditions of unmanned aerial vehicle electric quantity, loading capacity, safe flight height and the like, converting the scheduling scheme into a three-layer integer sequence including a take-off sequence, a task allocation sequence and an execution sequence for expression and optimization; a task interruption and battery replacement decision-making mechanism and a battery replacement station selection cost function under the condition of insufficient electric quantity are introduced, and a swarm intelligence algorithm is adopted for solving, so that the optimization target of minimizing the total task completion time, the total battery replacement frequency and the flight distance standard deviation is achieved.

The invention discloses an air-ground task intersection prediction method for emergency rescue, and belongs to the technical field of unmanned aerial vehicle control. In order to solve the problem of accurately predicting an air-ground task intersection with reachability and energy efficiency requirements, the method comprises the following steps: predicting an unmanned aerial vehicle path; predicting a ground vehicle path; generating candidate intersection points; calculating the time cost of the rescue task, the energy consumption cost of flight and the environmental risk cost; constructing a comprehensive cost function; constructing a confidence factor through the terrain height difference, the path node complexity and the communication shielding score, and carrying out risk correction on the comprehensive cost function to obtain a corrected comprehensive cost function; and introducing factors such as a ground slope angle, a wind speed value, a ground trafficability score and an unmanned aerial vehicle flight distance to carry out punishment correction, and extracting an optimal air-ground task intersection point coordinate for emergency rescue. The method is suitable for various emergency rescue, multi-platform cooperation and intelligent task scheduling, and has wide application prospect and popularization value.

The invention discloses a transformer substation flood prevention intelligent early warning method and system, and relates to the technical field of flood prevention intelligent early warning, and the method comprises the following steps: obtaining transformer substation ponding historical data and an unmanned plane observation image, building a ponding risk state set based on the ponding historical data, and carrying out the correlation analysis, performing feature fusion on the ponding state vector and an unmanned aerial vehicle observation image to obtain a first ponding state sequence, and mapping the first ponding state sequence to an unmanned aerial vehicle observable area to obtain a dynamic target area set; according to the dynamic target area set and a preset unmanned aerial vehicle safety distance constraint, dynamically adjusting an unmanned aerial vehicle inspection path to obtain a first inspection path; according to the method, the unmanned aerial vehicle is subjected to routing inspection along the first routing inspection path, intelligent substation flood prevention early warning is performed, and the problem that local key ponding points are difficult to observe in real time in a traditional flight mode due to the fact that the unmanned aerial vehicle needs to keep a safe flight distance in the routing inspection process and cannot be directly close to high-voltage equipment or other key facilities is solved.

The invention relates to the technical field of unmanned aerial vehicle path planning in a complex environment, and provides a complex environment unmanned aerial vehicle path planning method based on Chebyshev chaotic mapping sparrow optimization. Under the constraint of flight distance and height, a total cost function is constructed, Chebyshev chaotic mapping is utilized to initialize a sparrow population, population diversity is improved, global search and local development capabilities in an exploration rate balance position updating process are introduced, a producer and follower step length adjustment strategy based on the exploration rate is adopted, and a sparrow swarm optimization algorithm is established. According to the method, the self-adaptability of the step length in the position updating process of the producer and the follower is improved, the minimization of the total cost function is realized in the process from the early global search of the minimization of the total cost function to the later focusing local search, the optimal solution of the total cost function is sought, and the flight path which is short in distance, low in energy consumption and capable of avoiding the threat source is planned for the unmanned aerial vehicle.

The invention relates to a multi-unmanned aerial vehicle cooperative path planning and scheduling method and system. The method comprises the following steps: acquiring task requirements, and determining a plurality of task areas needing aerial survey, coverage requirements and unmanned aerial vehicle parking positions; constructing a coverage path with the minimum turning cost for each task area according to coverage requirements; calculating the flight path cost of the unmanned aerial vehicle according to the parking position and the coverage path of the unmanned aerial vehicle; and according to the flight path cost, planning an access area and an access sequence for each unmanned aerial vehicle by using a genetic algorithm, and generating an unmanned aerial vehicle flight path. According to the method, the task demand is acquired, the unmanned aerial vehicle observation coverage path with turning optimization is generated in the task area, and the optimized unmanned aerial vehicle scheduling scheme and flight path are generated by using the genetic algorithm based on the coverage path cost, so that the surveying and mapping demand can be met while the quality and quantity are guaranteed; the total flight distance is reduced to the maximum extent, the workload distribution among the unmanned aerial vehicles is balanced, and a data acquisition window for large-scale surveying and mapping is shortened.

A shot identifying module 220 identifies a flight distance of the golf ball and an angle of a direction from the measurement terminal 10 toward the golf ball with respect to a direction toward which the user terminal 10 is directed at a time of the measurement. A terminal position and orientation identifying module 222 identifies a terminal position and a terminal orientation. A ball position identifying module 224 identifies a ball position based on the terminal position, the terminal orientation, the flight distance of the golf ball, and the angle.

The invention provides an unmanned aerial vehicle task allocation method based on an improved South goose optimization algorithm, and the method comprises the steps: generating a plurality of initial allocation schemes in a three-dimensional environment according to the starting points and target points of a plurality of unmanned aerial vehicles; calculating the distance from the starting point to the target point of all unmanned aerial vehicles, and establishing a task allocation model fusing the longest single-aerial-vehicle flight distance and the shortest total flight distance; calculating the comprehensive flight cost of all the initial schemes; and performing iterative optimization on the initial allocation scheme by using an improved South goose optimization algorithm to obtain an optimal allocation scheme, so that the longest single-machine flight distance and the total flight distance are both effectively reduced. In the iterative optimization process, a new region search mechanism is designed, a mixed strategy with development as a main part and exploration as an auxiliary part is formed, an adjustment coefficient is added in the development stage, and the search range is further expanded. According to the method, the real-time performance and the task completion efficiency of multi-unmanned aerial vehicle task allocation are remarkably improved, and the method is suitable for scenes with strict task allocation requirements such as disaster rescue and logistics distribution.

A time-of-flight distance measuring method and TOF distance measuring system. The distance measuring method includes: intermittently transmitting a plurality of pulses from a pulse generation unit; controlling a TOF sensor to allow each pixel of a plurality of pixels in the TOF sensor to continuously perform a first signal sampling first proportion for a first predetermined time on a first proportion of a plurality of reflected signals and performs a second signal sampling second proportion for a second predetermined time on a second proportion of a plurality of reflected signals, so as to generate a plurality of sampling results corresponding to the plurality of pixels; obtaining a plurality of depth information and a plurality of luminance information corresponding to the plurality of pixels according to the plurality of sampling results; and adjusting the first proportion and the second proportion according to the plurality of depth information and the plurality of luminance information.

An object of the present disclosure is to provide a golf ball having improved flight distance on driver shots and excellent spin performance on approach shots (particularly under the condition that there is grass between the golf ball and the club face) and on middle iron shots. The present disclosure provides a golf ball comprising a spherical core and a cover disposed outside the spherical core, wherein when a center hardness of the spherical core (Shore C hardness), hardnesses at 2.5 mm, 5 mm, 7.5 mm, 10 mm, 12.5 mm and 15 mm points from a center of the spherical core toward a surface of the spherical core (Shore C hardness), and a surface hardness of the spherical core (Shore C hardness) are represented by H0, H2.5, H5, H7.5, H10, H12.5, H15 and Hs respectively, the following relation is satisfied:(H2.5−H10)>(H12.5−H10)>(Hs−H15).

The present application relates to the technical field of low-altitude aircraft, in particular to a pre-turning flight ball car control method and a pre-turning flight ball car, wherein the pre-turning flight ball car control method comprises the following steps: S1, obtaining a pre-turning direction and a current direction; S2, calculating a pre-turning angle and a pre-turning adjustment direction based on the pre-turning direction and the current direction; S3, controlling the pre-turning device to drive the flight driving device or the combination of the flight driving device and the cabin to turn relative to the landing gear to face the pre-turning direction according to the pre-turning angle and the pre-turning direction. In this way, the problems of the existing golf cart, such as limitation, and the traditional aircraft, such as increased flight distance and poor passenger experience caused by adjusting the flight direction after take-off, are solved.

The application discloses a kind of unmanned aerial vehicle autonomous inspection orthographic image generation method, it is related to unmanned aerial vehicle surveying and mapping and autonomous navigation technical field, the path optimization strategy of the present application is fused by genetic algorithm, global optimization is realized to route in combination with the image parameter constraint of inspection area grid feature, with candidate navigation point as node, through the smoothness factor of navigation section, redundant navigation point is filtered, and the complexity of route is reduced, genetic algorithm is constrained with flight height, navigation point spacing, constructs fitness function including flight distance, number of turns, overlap rate, finds global optimal path by population iteration, optimizes and balances multi-objective demand, ensures that route meets image acquisition accuracy requirement, can also shorten flight distance, reduce number of turns, adapt to the endurance of unmanned aerial vehicle, guarantee that inspection task is efficiently promoted, simultaneously, the navigation point coordinates of simulation output are directly adapted to flight control system, ensure that actual flight parameter is consistent with planning parameter.

The invention discloses an intelligent attendance checking method, device and equipment based on multi-dimensional characteristics and a medium, and the method comprises the following steps: collecting original signal data of network equipment in an attendance checking area, and carrying out characteristic extraction on the original signal data to respectively obtain a received signal strength characteristic, a channel state characteristic and a flight distance characteristic; performing feature fusion on the received signal strength feature, the channel state feature and the flight distance feature to obtain a multi-source feature vector, and inputting the multi-source feature vector into a preset random forest model to enable the random forest model to output an event judgment result of the target user terminal; determining an attendance checking result of the user according to the event judgment result, pre-acquired event occurrence time and an attendance mode of the corresponding user; wherein the attendance mode is determined through a preset Gaussian mixture model. The problem that attendance checking judgment is inaccurate due to the fact that attendance checking rules are rigid in the prior art can be solved.

The application provides a flapping-wing aircraft formation flight energy-saving method and device, the method comprising: inputting geometric parameters of a required control flapping-wing aircraft; calculating a required relative flight distance to be kept when the flapping-wing aircraft flies in formation according to the input geometric parameters of the flapping-wing aircraft; determining a lead aircraft and wingman according to an evaluation function selection to form a formation flight; detecting a residual power of a current lead aircraft, and replacing the lead aircraft when the current lead aircraft consumes energy exceeding a set value; and continuously replacing the lead aircraft before the end of a flight task, and selecting a new lead aircraft according to the evaluation function. The method can keep the energy distribution of the flapping-wing aircraft group balanced when the flapping-wing aircraft flies in formation, effectively saves the energy of the flapping-wing aircraft, and improves the endurance of the flapping-wing aircraft.

The application relates to a UAV attitude stabilizing control system in a mountainous high-altitude environment. The system comprises a screening module, which obtains a matching coefficient according to the distance required for each candidate route to converge into a preset route, wind power information and wind direction information on the candidate route, and residual power and residual flight distance, and screens a target route from multiple candidate routes; a calculation module, which calculates reverse compensation forces corresponding to multiple force directions according to the force intensity of the UAV in the multiple force directions in a first historical period; a determination module, which determines the target force of each motor of the UAV according to the wind power at the current position, the distance between the current position and each obstacle, the frequency of the UAV producing deviation in a second historical period, and the reverse compensation forces; and a control module, which determines the target speed of the UAV according to the angle between the total force vector of the UAV at each position of the target route and the target route. The application can make the UAV control more stable when flying in a mountainous high-altitude environment.

The invention discloses an intelligent route planning method and system for an unmanned aerial vehicle. The method comprises the following steps: acquiring mark point data and real-time hotspot data; scoring each mark point according to the mark point data and the real-time hotspot data, and determining the priority of the mark points by sorting the scoring results; constructing a multi-objective optimization function which meets a preset constraint condition and takes flight distance minimization, hotspot coverage rate maximization, priority satisfaction degree maximization and risk minimization as objectives; wherein the constraint conditions comprise a battery endurance constraint, an airspace limitation constraint, a weather condition constraint and a time window constraint; and scoring each randomly generated route through the multi-target optimization function, and determining the route with the minimum score as a target optimal route. According to the method, the response capability to a real-time dynamic environment can be effectively improved, multiple targets such as distance, coverage, priority and risk are balanced, and dynamic, efficient and safe unmanned aerial vehicle optimal route planning is realized.

The application belongs to the technical field of unmanned aerial vehicle information security, and particularly relates to an anti-deception interference data protection method and system for power inspection unmanned aerial vehicle, which first acquires the flight speed and position information of the unmanned aerial vehicle in real time, and dynamically adjusts the actual flight constraint radius of the unmanned aerial vehicle according to the comparison result of the flight speed and a preset threshold value. The actual flight distance between the unmanned aerial vehicle and the nearest route point is calculated, and compared with the actual flight constraint radius to determine whether the unmanned aerial vehicle is subjected to position deception interference. In the case that the position of the unmanned aerial vehicle is safe, a token transmission mechanism is used to receive the inspection data transmitted by the unmanned aerial vehicle, and the time window of data transmission is dynamically adjusted according to the flight speed. Through real-time acquisition of the flight speed and position information of the unmanned aerial vehicle, and dynamic adjustment of the flight constraint radius and the data transmission strategy according to the flight speed and position information, dynamic identification and response to deception interference are realized.

The invention provides a brake device and a fishing reel, which not only can achieve miniaturization and low cost, but also can supply optimal braking force in a wider speed range, and can more practically realize improvement of flight distance and prevention of recoil. Specifically, the fishing reel according to one embodiment of the present invention is provided with: a spool on which a fishing line can be wound; a reel body that rotatably supports the spool; a winding unit capable of operating the spool in a winding direction; a switching unit capable of switching whether or not the reel can be unwound; the electromagnetic braking part is used for carrying out electromagnetic braking on the winding drum; a friction brake unit for imparting a frictional force to the spool; and an adjustment member that adjusts a braking force on the spool, the adjustment member being configured so as to be able to adjust both the electromagnetic brake unit and the friction brake unit.

The invention relates to the technical field of wildlife monitoring and ecological protection, and discloses an unmanned aerial vehicle flight parameter adaptive optimization waterfowl population investigation method, which comprises the following steps: firstly, constructing a multi-dimensional aerial image interpretation mark library, carrying out a flight parameter and waterfowl disturbance response correlation test, establishing a quantitative behavior response spectrum, and then, carrying out unmanned aerial vehicle flight parameter adaptive optimization on the quantitative behavior response spectrum. According to the method, a frightened flight distance probability model is constructed according to a response spectrum, a minimum safety monitoring distance threshold value is calculated, an optimal monitoring flight parameter combination is determined in combination with statistical accuracy and environmental factors, and then an aerial photography task is executed to obtain population data. A dominant limiting factor is identified to generate a repair countermeasure, and a dynamic protection partition is delimited. According to the method, the contradiction between the image resolution and the safety of wild animals in unmanned aerial vehicle monitoring is effectively solved, and accurate decision-making of waterfowl protection and habitat restoration is realized.

The utility model provides a kind of Apli leg type impact energy-absorbing protective device for automobile pedestrian protection, including box, box inside hollow formation accommodating cavity, the side of box is equipped with the impact port that is communicated with accommodating cavity, the hook in accommodating cavity is set to the top of box, the side wall of box is set to first protective pad back to impact port, the bottom of box is set to second protective pad, the top of box is set to third protective pad. By setting box, the flight distance of Apli leg type after being impacted can be limited, which is limited in accommodating cavity, after the flight distance is reduced, the landing point deviation rate caused by flight angle is reduced, at the same time, by setting first protective pad on the side wall back to impact port, second protective pad and third protective pad are respectively set on the bottom and top of box, different angle protective measures can be formed in the direction away from impact port, after Apli leg type is impacted and flies, kinetic energy is absorbed, Apli leg type is protected from damage.

The application provides a UAV recovery path adaptive planning method and system based on air-ground cooperation, relates to the technical field of robot motion planning, and comprises the following steps: constructing a three-dimensional grid map of a task environment, acquiring the positions of a UAV and an unmanned vehicle and the maximum flight distance of the UAV; determining the responsibility distribution of the UAV and the unmanned vehicle in a recovery task based on the positions of the UAV and the unmanned vehicle and the maximum flight distance of the UAV, and obtaining an optimal UAV recovery scheme; selecting a corresponding path planning mode according to the optimal UAV recovery scheme, and determining the starting point, the ending point and the dimension of the search space of path planning based on the positions of the UAV and the unmanned vehicle; executing a path planning algorithm according to the selected path planning mode, and generating a final recovery path; and through air-ground cooperative decision and cross-dimension bidirectional joint path planning algorithm, the application realizes safe and efficient recovery of the UAV under the constraint of electric quantity, and significantly improves the task success rate and the system cooperation efficiency.

The application discloses a kind of control method and device of inspection system, equipment and storage medium.The scheme includes: inspection system is provided with unmanned vehicle accompanying unmanned plane movement and can provide additional power for unmanned plane.According to the current power of unmanned plane and first current position, the target inspection point to which the next inspection task of unmanned plane is directed is determined from the set of points to be inspected;The power required to complete the inspection task for the target inspection point is not greater than the current power;If the target inspection point of the unmanned plane cannot be determined from the set of points to be inspected, the unmanned plane is instructed to stop at the unmanned vehicle.Thereby, when the power of the unmanned plane is insufficient to complete the inspection task, the unmanned plane airport on the unmanned vehicle provides power support for the unmanned plane;Thus, not only does it break the geographical limitations of fixed airports, but also increases the inspection range of the unmanned plane, while reducing the flight distance of the unmanned plane when refueling, improving the inspection efficiency of the unmanned plane.

The invention relates to a method and a system for predicting the flight distance of a diving platform skiing project based on self-adaptive pneumatic coupling integral, and a storage medium, and belongs to the field of diving platform skiing sports engineering. Comprises: establishing a diving platform skiing human body model; performing computational fluid mechanics simulation on the human body model based on the relative airflow velocity at the current moment and the human body total attack angle to obtain lift force and resistance at the current moment; obtaining the acceleration at the current moment based on the lift force and the resistance; updating the centroid speed and position of the human body by using the acceleration at the current moment based on the self-adaptive pneumatic coupling integral; and judging whether the human body falls to the ground according to the updated position, if so, obtaining a flight distance, otherwise, continuously iterating until the flight distance is obtained. According to the method, the lift force and the resistance are dynamically calculated at each time step through real-time calculation fluid mechanics simulation, and the mass center speed and the position of the human body are updated by utilizing the acceleration based on the self-adaptive pneumatic coupling integral, so that the flight distance is obtained, and the prediction precision of the flight distance is improved.

The application discloses a multi-station dynamic resource allocation method and system based on edge computing and related devices, belonging to the technical field of unmanned aerial vehicles. The method comprises the following steps: determining a target distribution station from a plurality of distribution stations; obtaining the historical task quantity of the target distribution station within a preset period, and determining a target load trend value representing the task load change trend based on the historical task quantity; determining at least one adjacent distribution station and obtaining the corresponding adjacent load trend value; based on the target load trend value and the adjacent load trend value, determining a resource migration vector of the target distribution station, which indicates the recommended moving direction and amplitude; determining the target position based on the resource migration vector and the current position, and generating a moving scheme. By comparing the historical task load trend of the distribution station with the adjacent station, the physical movement of the station is autonomously driven, the spatial layout of the distribution network is dynamically evolved according to the demand, the unmanned aerial vehicle flight distance is reduced, and the multi-station load balancing and overall resource utilization efficiency are improved.

The application relates to the technical field of emergency response and the technical field of energy scheduling, and discloses an emergency scheduling method and device based on a UAV, equipment and a storage medium, the method comprising the following steps: acquiring each UAV path from a starting point coordinate to a terminal point coordinate; generating the cost of each UAV path according to the risk degree of each grid in each UAV path, the flight distance of each voyage in each UAV path and a preset cost generation model, selecting the UAV path with the minimum cost as an optimal UAV path, and acquiring a UAV scheduling scheme corresponding to the optimal UAV path; generating the total loss cost corresponding to the UAV scheduling scheme according to the user loss cost, the load recovery cost and the deployment cost of the energy storage battery; and selecting the UAV scheduling scheme as an emergency scheduling scheme of a power failure area when the total loss cost is less than a preset loss cost. The application is beneficial to improving the timeliness of emergency response.

The application discloses an optimization method and device for multi-unmanned aerial vehicle assisted ocean communication, equipment and medium, relates to the technical field of ocean communication, and the method comprises the following steps: first, a beam forming and trajectory optimization model under a multi-unmanned aerial vehicle assisted ocean communication environment is constructed, the beam forming and trajectory optimization model comprises a target function and a constraint condition, the target function is the sum of the achievable rates of the ships at sea in the service period, and the constraint condition comprises amplitude and phase constraints of a reconfigurable intelligent surface, communication connection constraints of the unmanned aerial vehicles and the ships at sea, and flight distance, distance apart, flight route and energy constraints of the unmanned aerial vehicles; and then the beam forming and trajectory optimization model is solved to obtain a beam forming and trajectory optimization scheme under the multi-unmanned aerial vehicle assisted ocean communication environment. The application can realize efficient operation and stable transmission of the ocean communication network.

The invention discloses a photovoltaic inspection method based on aircraft-boat cooperation. The photovoltaic inspection method comprises the following steps: dividing a photovoltaic panel in an inspection sea area into k inspection areas based on the number k of unmanned aerial vehicles for inspection; determining the inspection sequence of each photovoltaic panel in the inspection area; respectively determining a take-off point and a recovery point of the unmanned aerial vehicle based on the inspection starting point and the inspection terminal point of each inspection area; an unmanned plane is carried on an unmanned ship, the unmanned ship runs from a starting position to a take-off point based on a planned path, the unmanned plane is released at the take-off point, the unmanned plane flies from the take-off point to an inspection starting point, flies from the inspection starting point to an inspection terminal point and flies from the inspection terminal point to a recovery point based on a planned flight path, and the unmanned ship sails from the take-off point to the recovery point at the same time. The release point and the recovery point of the unmanned ship are dynamically determined based on the inspection start point and the inspection end point of the unmanned aerial vehicle group, the flight distance of the unmanned aerial vehicle group flying back to the parking point is reduced as much as possible, and the task execution efficiency of the unmanned aerial vehicle group is greatly improved.

The application provides a multi-missile two-dimensional cooperative guidance method based on a Bezier curve, and is characterized in that the method comprises the following steps: determining a Bezier initial attack trajectory of each missile in a missile group, determining a Bezier curve tracking point of each missile in the missile group, calculating a lateral acceleration of each missile in the missile group, calculating a remaining flight distance of each missile in the missile group, calculating a time of arrival of each missile in the missile group, adjusting the Bezier curve of each missile according to the predicted time of arrival of surrounding missiles, and checking whether the missiles hit the target or not. The application is based on the Bezier curve, the time of arrival is calculated by using a difference method, and the application can better cope with the situation that there is resistance in the flight process. The method has the advantages of small calculation amount and easiness in realization on a missile-borne computer.

The application relates to the field of unmanned aerial vehicles, and discloses a gimbal control method, a controller, an unmanned aerial vehicle and an unmanned aerial vehicle inspection system. The gimbal control method is applied to an unmanned aerial vehicle, and the inspection information of the unmanned aerial vehicle is acquired. The inspection information comprises an observation flight section, an observation interval corresponding to the observation flight section, a total flight distance corresponding to the observation interval and a current flight distance. Then, the observation progress of the unmanned aerial vehicle is determined according to the current flight distance and the total flight distance. A first position is determined according to the observation progress and the observation interval, and the first position is the position of the center point of the field of view of the nacelle of the unmanned aerial vehicle. Finally, the angle of the gimbal of the unmanned aerial vehicle is controlled according to the first position and the current position of the unmanned aerial vehicle. In the above manner, the current shooting field of view can be adapted to the observation interval. When the inspection is completed, the entire shooting field of view can completely cover the inspection object, the inspection object is adaptively observed, the inspection object is completely observed, the risk of missing inspection is effectively avoided, and the inspection efficiency is improved.