54 results about "Uncrewed vehicle" patented technology

The invention provides an unmanned aerial vehicle route planning method based on an improved bat algorithm. According to the method, the optimization success rate is introduced to change the speed updating mode of the individual bats based on the conventional bat algorithm; meanwhile, the chaotic method is applied to initialize the distribution of the individual bats in the search space and the concept of the artificial potential field is utilized to simulate the gravitational field of the ending point and the repulsive field of the starting point and the obstacle so as to accelerate the speedof the individual bats to the optimal solution; and finally the improved bat algorithm based on the chaotic artificial potential field is proposed. Compared with the conventional bat algorithm, the track length is shortened for 36.56%, the planning time is shortened for 56.05% and the obstacle avoidance fitness value is reduced for 49.53% by the method; and compared with the differential evolutionary bat algorithm, the track length is shortened for 27.16%, the planning time is shortened for 27.30% and the obstacle avoidance fitness value is reduced for 42.46% by the method in the unmanned aerial vehicle route planning task so that the method is a route planning algorithm with practical significance.

The invention discloses a method for evaluating the quality of an image shot by an unmanned aerial vehicle, which is characterized in that for an image shot by the unmanned aerial vehicle in patrol inspection, the quality of the image is analyzed from the whole image and local details; for blurring generated by focusing misalignment caused by motions or shakes of the unmanned aerial vehicle, a frequency domain sharpness index and the distribution density and orientation features of edge segments in a space domain are firstly fused so as to judge whether an image captured by the unmanned aerial vehicle image is blur or not on the whole; under the condition that the image quality is acceptable on the whole, salient edge regions in the image are further searched, and the local detail blur degree of image edges is judged through analyzing the average width of difference salient edges. The method is applied to images shot by the unmanned aerial vehicle under outdoor conditions, the image quality can be effectively evaluated under various complex backgrounds without being influenced by the image content, and the evaluation is consistent with subjective evaluation of people in grade.

The present invention discloses a multi-shaft power source unmanned flight equipment, and belongs to the technical field of unmanned aerial vehicles. The multi-shaft power source unmanned flight equipment comprises a frame (1), a plurality of rotor sets (2) and a power device (3). The plurality of rotor sets (2) are rotatably fixed on the frame (1), and the power device (3) is correspondingly movably connected with each rotor set (2) respectively. Power is provided for flight of the unmanned flight equipment by the power device (3) with oil drive characteristics, mechanical kinetic energy is generated by burning a combustion material pre-injected in the power device (3), and rotors (21) in each rotor set (2) correspondingly connected with the power device are driven to rotate, thereby replacing the traditional electric multi-rotor unmanned aerial vehicle structure adopting electric modes such as batteries, electronic speed controllers and the like to supply power and provide power for the rotation of the rotors (21); and the unmanned flight equipment has the characteristics of long duration and strong loading capacity.

The invention discloses a multi-machine distributed time sequence task allocation method based on a non-deadlock contract net algorithm, which belongs to the technical field of task allocation. The implementation method comprises the following steps that a time sequence task allocation model isestablished aiming at a time sequence constraint problem of multi-unmanned aerial vehicle cooperative task allocation, and a time sequence task deadlock criterion is proposed; in the task sorting process of a contract net algorithm, the influence of a coupling time sequence task is fully considered, a nearest neighbor task is preferentially selected, a task sorting scheme meeting deadlock constraints is generated in combination with deadlock criterion recursive backtracking used for eliminating an infeasible sorting scheme, a bid invitation unmanned aerial vehicle selection mechanism and algorithm convergence conditions are improved, a non-deadlock contract net (DF-CNP) algorithm based on a competition mechanism is further proposed, the non-deadlock contract net (DF-CNP) algorithm is parallel under a distributed architecture, and a multi-unmanned aerial vehicle non-deadlock time sequence task allocation result is efficiently output. The method is based on a competition mechanism, so that the method has the characteristic of high optimization efficiency.

The invention provides a Bayesian network-based combat effectiveness evaluation method of the surveillance unmanned aerial vehicles. The method comprises three parts of establishing an effectiveness evaluation index system, establishing a Bayesian network effectiveness evaluation model and performing the Bayesian network reasoning. By analyzing a combat system structure and a basic combat process,utilizing an entropy weight method to screen an efficiency evaluation index set, and constructing an index system for the combat efficiency evaluation of the surveillance unmanned aerial vehicles, the index system is simpler and more reasonable; a Monte Carlo algorithm is used for parameter learning of the Bayesian network, so that a conditional probability table is determined, the trouble of manually inputting the conditional probability table is avoided, and the efficiency is greatly improved; and the accurate inference of the Bayesian network is carried out by utilizing a cluster tree propagation algorithm, so that the reasoning time is saved, and the reasoning accuracy is improved.

Embodiments of the invention disclose an unmanned aerial vehicle sharing method and apparatus, a server, a mobile terminal and a storage medium. The method comprises the steps of obtaining unmanned aerial vehicle identifier information and sharer identifier information; determining whether an unmanned aerial vehicle corresponding to the unmanned aerial vehicle identifier information has a sharingpermission or not, and if the unmanned aerial vehicle has the sharing permission, determining whether a sharer account meets sharing conditions or not according to the sharer identifier information; if the sharer account meets the sharing conditions, generating and sending a sharing feedback; obtaining a flight record, wherein the flight record comprises a flight line record and a plant protectionoperation record of the unmanned aerial vehicle; and according to the flight record, updating the sharer account, and sending a balance feedback to the mobile terminal. According to the technical scheme, unmanned aerial vehicle sharing can be realized; while the usage demands are met, the purchase and maintenance costs of the unmanned aerial vehicle can be reduced; the cost of using the unmannedaerial vehicle to perform plant protection operation is reduced; and the user experience is improved.

The embodiment of the invention relates to the technical field of unmanned aerial vehicles, and particularly discloses a method and device for adjusting the main detection direction of an airborne radar and an unmanned aerial vehicle. The unmanned aerial vehicle comprises a holder and the airborne radar arranged on the holder, and the method comprises the steps: acquiring the flight information ofthe unmanned aerial vehicle in real time, wherein the flight information comprises at least one of the current pitch angle and the flight height of the unmanned aerial vehicle; and adjusting the maindetection direction of the airborne radar through the holder according to the flight information, so that the airborne radar can detect obstacles in the flight direction of the unmanned aerial vehicle. According to the technical scheme, the effective detection effect of the airborne radar in the obstacle avoidance system can be improved, and the obstacle avoidance function can be completed more safely.

The invention provides a method and device for determining the collision risk of an unmanned aerial vehicle, and the method comprises the steps: obtaining an unmanned aerial vehicle with the longitude and latitude and/or height information changing, and determining the unmanned aerial vehicle as a target unmanned aerial vehicle; determining a target geocode corresponding to the longitude and latitude, and determining a height code corresponding to the height information, the target geocode being used for uniquely identifying the plane area range of the target unmanned aerial vehicle, and the height code being used for uniquely identifying the height range of the target unmanned aerial vehicle; and determining whether the target unmanned aerial vehicle has a collision risk or not according to a target stereo code composed of the target geocode and the height code and/or the target geocode. By comparing the stereo code or the geocode of the unmanned aerial vehicle, the technical problems of large calculation amount and low efficiency of identifying whether the unmanned aerial vehicle has the collision risk are solved, and the technical effect of improving the efficiency of identifying whether the unmanned aerial vehicle has the collision risk is achieved.

The invention discloses an unmanned aerial vehicle air combat threat assessment method based on deep learning. The method comprises the following steps: firstly training a support vector machine network structure through a battlefield environment database to achieve the division of different air combat environments, and calibrating the different air combat environments into N modes; then, trainingN deep learning network structures based on a deep belief network by using air combat situation databases in different air combat environments; during evaluation, inputting actual battlefield environment data, and performing environment classification according to the trained support vector machine network, and determining an air combat situation mode in which the unmanned aerial vehicle is located; and finally, inputting the actual air combat situation data into the trained deep belief network in the corresponding mode to perform threat assessment. A double-layer evaluation network mode is adopted, the evaluation accuracy is improved, and a deep learning method is used for pre-training, so the evaluation rapidity is improved, and the threat evaluation problem in the air combat environment of the unmanned aerial vehicle is solved.

The invention provides a traffic monitoring method and a traffic monitoring system based on unmanned aerial vehicle image recognition, and the traffic monitoring method comprises the following steps: S1, providing an unmanned aerial vehicle aerial traffic picture as a sample picture, and carrying out the vehicle marking of the sample picture through image marking software LabelImg, and forming a sample set; s2, preprocessing the sample set by adopting a data enhancement method to form a training set; s3, constructing a YOLO v3 model, training the YOLO v3 model through the training set, and obtaining the trained YOLO v3 model; and S4, receiving an aerial traffic image of the unmanned aerial vehicle in real time, inputting the aerial traffic image into the trained YOLO v3 model to monitor and analyze the vehicle, and calculating to obtain real-time traffic monitoring data. According to the traffic monitoring method and the traffic monitoring system, macroscopic and accurate monitoring data can be obtained in real time, and the traffic situation can be studied, judged and decided in time.

The invention relates to the technical field of unmanned aerial vehicles, in particular to a light-load long-distance cruising unmanned aerial vehicle group system. The system comprises a ground control base station and at least one unmanned aerial vehicle group. The at least one unmanned aerial vehicle group comprises at least one main control unmanned aerial vehicle and N controlled unmanned aerial vehicles. AAT full-automatic tracking holders and wireless network bridges are carried on the ground control base station and the main control unmanned aerial vehicle. The AAT full-automatic tracking holders and the wireless network bridges are used for point-to-point transmission of control instructions between the ground control base station and the main control unmanned aerial vehicle. A router is carried on the main control unmanned aerial vehicle, wifi modules are carried on the N controlled unmanned aerial vehicles, and signal communication and instruction transmission are carried out between the main control unmanned aerial vehicle and the controlled unmanned aerial vehicles through wifi signals. According to the technology, the whole unmanned aerial vehicle group can fly farther and is higher in cruising ability, the adopted radio wave signals enable the unmanned aerial vehicle group to have the functions of investigation prevention and better concealment, and the unmannedaerial vehicle group can execute tasks in mountainous areas and seismic zones without 4G, 5G or other signals.

The invention discloses an energy-saving path planning method for a rotor unmanned aerial vehicle based on combination of a corner and a distance. According to the method, energy consumption of a rotor unmanned aerial vehicle flying over a winding path is estimated based on the corner and distance features of the winding path, so that an incomprehensive problem caused by only using the distance to estimate path energy consumption in energy-saving path planning of the rotor unmanned aerial vehicle is solved. A three-dimensional weight matrix is constructed and an energy-saving path is planned by combining a greedy strategy. Therefore, the endurance of the rotor unmanned aerial vehicle is enhanced; and the energy limitation in the application of the rotor unmanned aerial vehicle is improved. According to the energy-saving path planning method disclosed by the invention, with simultaneous consideration of the path corner and distance, the energy consumption of the rotor unmanned aerial vehicle flying over the path is evaluated. The method can be applied to UAV-assisted task point traversal, so that the flight energy consumption is reduced and the amount of task completion for single charging is increased.

The invention discloses a course anomaly detection and safety protection method and device, equipment and a storage medium. The method comprises the steps: when the unmanned aerial vehicle is in a flight scene, acquiring magnetic compass associated data, at least one piece of real-time dynamic RTK associated data and gyroscope data; judging whether the magnetic heading of the unmanned aerial vehicle is abnormal or not according to the magnetic compass associated data, judging whether the RTK heading of the unmanned aerial vehicle is abnormal or not according to the at least one piece of RTK associated data, and judging whether the real-time fusion heading of the unmanned aerial vehicle is abnormal or not according to the gyroscope data and the magnetic heading; and if at least one of the magnetic heading, the RTK heading and the real-time fusion heading is determined to be abnormal, executing at least one safety protection measure. By using the technical scheme of the invention, accurate and stable course anomaly detection and safety protection of the unmanned aerial vehicle can be realized, timely and effective safety protection can be performed when the course is abnormal, and the flight safety of the unmanned aerial vehicle is improved.

The invention provides a movable wireless charging system based on automatic cruise of an unmanned aerial vehicle. The system employs the unmanned aerial vehicle with an autonomous path planning function as a moving means of wireless charging equipment, and charges a built-in battery in an application occasion where a wireless sensor network is arranged in a large scale. The system comprises an unmanned aerial vehicle control module, an airborne positioning module, an airborne wireless charging device, an unmanned aerial vehicle start-stop wireless charging platform, a communication module, aplatform built-in control center and a cloud server. By applying the technical scheme, autonomous path planning can be realized, and the wireless charging equipment is carried to be positioned to a specified position to charge the built-in battery of a wireless sensor network node.

The invention relates to a method for monitoring construction stages of an electronic factory building based on the unmanned aerial vehicle aerial photography technology. The method comprises the following steps: firstly, performing aerial photography on the electronic factory building, and taking 16-24 pictures of different directions of the electronic factory building at fixed points, performingpanoramic synthesis through the PTGui software, performing a sky supplementing operation on a panorama by using PS, outputting a 2:1 panoramic picture, uploading the 2:1 panoramic picture to a 720 cloud platform, adding a hotspot to the 2:1 panoramic picture, and finally completing the supervision and control of each stage through the aerial photography of each stage. By adoption of the method provided by the invention, better supervision and control on the construction progress of the buildings onsite can be better achieved.

The invention provides a non-hovering unmanned aerial vehicle inspection system and method, the system comprises an unmanned aerial vehicle body, the unmanned aerial vehicle body is provided with a holder, and the holder is provided with an image acquisition module for acquiring image information; the unmanned aerial vehicle body is further provided with a positioning module used for obtaining three-dimensional coordinate information of the unmanned aerial vehicle body. The processing module is used for fitting flight control data of the unmanned aerial vehicle, issuing a flight control command to control flight of the unmanned aerial vehicle in combination with information of the positioning module and information of the image acquisition module, controlling the holder to adjust a shooting angle and a focal length of the image acquisition module, locking a tower inspection point and photographing; and when the pole and tower inspection point is not located in the set area of the image acquired by the image acquisition module, the holder is controlled to rotate based on a visual movement tracking mode, and the rotation direction of the holder is determined according to the position of the pole and tower inspection point in the image. According to the invention, hovering-free autonomous inspection and image information acquisition are realized, manual participation is not needed, and the electric quantity of the unmanned aerial vehicle is saved.