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1014results about "Target-seeking control" patented technology

Dynamic target tracking and positioning method of unmanned plane based on vision

The invention discloses a dynamic target tracking and positioning method of an unmanned plane based on vision, and belongs to the navigation field of the unmanned planes. The dynamic target tracking and positioning method comprises the following steps of: carrying out video processing, dynamic target detecting and image tracking; carrying out cloud deck servo control; establishing a corresponding relationship between a target in the image and a target in the real environment, and further measuring the distance between a camera and a dynamic target to complete precise positioning of the dynamic target; and enabling an unmanned plane control system to fly by automatically tracking the dynamic target on the ground. The dynamic target tracking and positioning method of the unmanned plane based on the vision can automatically realize the movement target detecting, image tracking and optical axis automatic deflecting without the full participation of the people, so that the dynamic target is always displayed at the center of an image-forming plane; and the distance between the unmanned plane and the dynamic target is measured in real time according to an established model on the basis of obtaining the height information of the unmanned plane. Therefore, the positioning of the dynamic target is realized; closed-loop control is formed by using the positioned dynamic target as a feedback signal, so that the tracking flight of the unmanned plane is guided.
Owner:BEIHANG UNIV

Intelligent multi-mode flying shooting equipment and flying control method thereof

The invention discloses intelligent multi-mode flying shooting equipment and a flying control method thereof, and belongs to the technical field of aviation. The flying shooting equipment comprises a rack, a flying mechanism, a shooting device and a control mechanism; the control mechanism also comprises an automatic tracking module, a shooting action setting module and a shooting strategy setting module, wherein the automatic tracking module is used for recognizing a target object, and realizing track tracing and follow shooting in allusion to the target object; the shooting action setting module is used for predefining at least one shooting action of the flying shooting equipment; the shooting strategy setting module is used for setting a corresponding shooting strategy according to the preset condition of the target object, and executing a corresponding shooting action when the preset condition is met. According to the intelligent multi-mode flying shooting equipment and the flying control method thereof disclosed by the invention, on the technological base of the traditional automatic follow shooting on the target object, intelligent situational shooting is completed with a rich shooting mode; under the condition that artificial control is not needed completely, a shooting effect with a lens sense and a shocking effect can be generated.
Owner:高域(北京)智能科技研究院有限公司

Method and system for cooperatively tracking target by unmanned aerial vehicle cluster

The invention provides a method and a system for cooperatively tracking a target by an unmanned aerial vehicle cluster. The method comprises the following steps of: establishing a wireless communication network for the cooperatively operated unmanned aerial vehicle cluster, and detecting a tracked target by utilizing an onboard sensor; when an optional unmanned aerial vehicle in the unmanned aerial vehicle cluster detects the target, processing detection data of the optional unmanned aerial vehicle, and broadcasting processing information to the other unmanned aerial vehicles; acquiring relative distances and relative angles among each unmanned aerial vehicle, the tracked target and the rest unmanned aerial vehicles in the unmanned aerial vehicle cluster according to the processing information by each unmanned aerial vehicle in the other unmanned aerial vehicles; constructing an environmental space potential field by each unmanned aerial vehicle in the other unmanned aerial vehicles according to the relative distances and the relative angles among each unmanned aerial vehicle, the tracked target and the rest unmanned aerial vehicles in the unmanned aerial vehicle cluster by each unmanned aerial vehicle in the other unmanned aerial vehicles, and calculating a resultant force of the potential field; and when the resultant force of the potential field is smaller than a safety threshold, controlling the corresponding unmanned aerial vehicle to track the tracked target. According to the method provided by the embodiment of the invention, sensor information is shared by utilizing the unmanned aerial vehicle cluster, and the high efficiency and the instantaneity of the cooperative target tracking are guaranteed.
Owner:TSINGHUA UNIV

Accompanying robot path planning method and system based on obstacle virtual expansion

The invention discloses an accompanying robot path planning method and system based on obstacle virtual expansion. The method includes a step of constructing an environment map, namely a step of constructing a two-dimensional occupancy grid map according to an actual scene, with each grid being labeled as an obstacle zone or a walkable zone; a step of setting initial coordinate positions of an accompanying robot and a movable target in the grid map; a step of constructing a sliding window for the robot; a step of subjecting the obstacle zones to expansion processing, namely a step of performing initial expansion on grids where an obstacle is according to a shortest distance between the center of the robot and a body edge, determining the number of grid layers expanded on the basis of the minimum impassable zone, adopting the grids in the expanded zones as obstacle virtual expansion grids, and labeling the grade of danger of obstacle influences on the obstacle virtual expansion grids; and a step of planning a path for the accompanying robot based on an A* algorithm and an incremental path planning process. Incremental path updating is performed by adopting a path of the last moment,thus saving path planning time and increasing the response speed of the accompanying robot.
Owner:QILU UNIV OF TECH

RFID-based online scheduling control system of automatic guided vehicle

The invention relates to an RFID-based online scheduling control system of an automatic guided vehicle (AGV). The system comprises multiple AGVs, multiple production workstation scheduling terminals and a remote control center. All the AGVs are in communication connection with the remote control center through the wireless network. All the production workstation scheduling terminals are in communication connection with the remote control center through the Ethernet network. The AGVs are connected with the production workstation scheduling terminals through navigation magnetic strips laid in a workshop. Each of the AGVs comprises a power supply, a magnetic navigation module, an RFID module, a wireless communication module and a PLC control module, wherein the power supply provides power for the AGV; and the magnetic navigation module, the RFID module and the wireless communication module are electrically connected with the PLC control module. According to the invention, the system is convenient to operate; unmanned navigation and automatic running can be achieved for the AGVs; and the AGVs are scheduled and controlled in an online manner in real time depending on the RFID signals, so the flexibility of scheduling management of the AGV management scheduling system is greatly improved.
Owner:YANCHENG INST OF TECH

Self-organizing method for cooperative scouting and hitting task of heterogeneous multi-unmanned-aerial-vehicle system

The invention discloses a self-organizing method for a cooperative scouting and hitting task of a heterogeneous multi-unmanned-aerial-vehicle system. A heterogeneous multi-unmanned-aerial-vehicle system is decomposed into two isomorphic sub systems and a corresponding cooperative way between the two sub systems is also designed; and the two sub systems carry out task planning respectively and also carry out mutual cooperation. For an isomorphic reconnaissance type unmanned aerial vehicle system and an isomorphic scouting and hitting unmanned aerial vehicle system, a cooperative searching task self-organizing method and a cooperative scouting and hitting autonomous task planning method are designed respectively. According to the cooperative searching task self-organizing method, problem decomposition is carried out by using a method based on distributed model prediction control; and then solution is carried out by using a particle swarm algorithm. And according to the cooperative scouting and hitting autonomous task planning method, a normal flight mode without any detection of a threat and a threat avoiding mode with threat detection are designed during a local problem construction process of each unmanned aerial vehicle based on a distributed ant colony algorithm. Therefore, an on-line requirement can be met well.
Owner:NANJING UNIV OF AERONAUTICS & ASTRONAUTICS

Mobile robot multi-behavior syncretizing automatic navigation method under unknown environment

InactiveCN101354587AReduce redundant rulesOmit the defuzzification processTarget-seeking controlAdaptive controlSupport vector machineSimulation
The invention discloses a multi-behavior combining auto-navigation method for a mobile robot in an unknown environment. The method is characterized in that the method comprises the following steps that (1) current azimuth angle is obtained in real time according to the relative positions of an object and the mobile robot, and a plurality of distance parameters are obtained in real time according to the status of obstacles around the mobile robot; (2) a multi-output support vector machine fuzzy controller outputs a corner value Theta i and a velocity value vi according to the obtained azimuth angle and distance parameters, wherein i is equal to 1, 2 or 3; (3) a multi-output support vector machine environmental-identification controller inputs signals and outputs weight parameters wi of three subbehaviors according to input signals of the azimuth angle and the distance parameters, wherein i is equal to 1, 2 or 3; and (4) current corner value Theta i and velocity value vi of the mobile robot used for navigation are calculated according to the formula. The multi-behavior combining auto-navigation method adopts intelligent control strategy, and has the advantages of strong self adaptation, high navigation reliability and excellent effect.
Owner:HUNAN UNIV
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