1014results about "Target-seeking control" patented technology

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.

The present invention provides systems, methods, and devices related to target tracking by UAVs. The UAV may be configured to receive target information from a control terminal related to a target to be tracked by an imaging device coupled to the UAV. The target information may be used by the UAV to automatically track the target so as to maintain predetermined position and/or size of the target within one or more images captured by the imaging device. The control terminal may be configured to display images from the imaging device as well as allowing user input related to the target information.

The illustrative embodiments provide a method and apparatus for controlling movement of a vehicle. User input selecting a path for the vehicle is received from an operator. The vehicle responds to the user input by moving along the selected path in a manner that maintains the operator on a side of the vehicle. The illustrative embodiments further provide a method and apparatus for improved machine control. The vehicle receives a power-up command, instructions to execute a planned path, and information identifying a leader. The leader is an operator. The vehicle then executes the planned path using the information identifying the leader in order to follow a path of the leader. The vehicle moves along the path in a manner that maintains the leader in a position proximate to a side of the vehicle.

An apparatus for tracking and identifying objects includes an audio likelihood module which determines corresponding audio likelihoods for each of a plurality of sounds received from corresponding different directions, each audio likelihood indicating a likelihood a sound is an object to be tracked; a video likelihood module which receives a video and determines video likelihoods for each of a plurality of images disposed in corresponding different directions in the video, each video likelihood indicating a likelihood that the image is an object to be tracked; and an identification and tracking module which determines correspondences between the audio likelihoods and the video likelihoods, if a correspondence is determined to exist between one of the audio likelihoods and one of the video likelihoods, identifies and tracks a corresponding one of the objects using each determined pair of audio and video likelihoods.

A closed-loop LRF pointing technology to measure the range of a target satellite from a chaser satellite for rendezvous is provided that includes several component technologies: LOS angle measurements of the target satellite on a visible sensor focal plane and the angles' relationships with the relative position of the target in inertial or LVLH frame, a relative navigation Kalman filter, attitude determination of the visible sensor with gyros, star trackers and a Kalman filter, pointing and rate commands for tracking the target, and an attitude controller. An analytical, steady-state, three-axis, six-state Kalman filter is provided for attitude determination. The system and its component technologies provide improved functionality and precision for relative navigation, attitude determination, pointing, and tracking for rendezvous. Kalman filters are designed specifically for the architecture of the closed-loop system to allow for pointing the laser rangefinder to a target even if a visible sensor, a laser rangefinder, gyros and a star tracker are misaligned and the LOS angle measurements from the visible sensor are interrupted.

In a mobile robot, a user position data acquisition unit acquires user position data representing a user's location. A user movable space generation unit generates user movable space data representing a space in which the user moves based on the user position data. A position relationship control unit controls a position relationship between the user and the mobile robot based on the user movable space data.

Embodiments provide a strategy for computing the motions of a mobile robot operating in an obstacle-laden environment without requiring prior knowledge of the distribution of obstacles in the environment or knowing the trajectory of a target tracked by the robot. Embodiments provide an algorithm that governs the motion of the observer robot based on measurements of the target's position and the location of obstacles in the environment. The algorithm computes a description of the geometric arrangement between the target and the observer's visibility region produced by the obstacles and computes a continuous control rule using this description. Embodiments employ an escape-path tree data structure to categorize the target's possible modes of escaping from the observer robot's sensors and use the escape-path tree to determine the target's shortest escape path.

A joint search method for UAV multiobjective path planning in an urban low altitude environment first constructs a static safety index map based on static known obstacles. Meanwhile, based on proactively detected obstacles that are not marked on a geographic map by a UAV, the method constructs online a dynamic safety index map. Second, a multiobjective path planning problem is solved using a joint offline and online search method. Moreover, this method first plans offline the least cost path from a starting point to an end point and then invokes the online search scheme to replan online a changed path when the UAV detects unknown obstacles. Thus, the UAV can avoid dynamic obstacles effectively. The online search scheme has a small search space and can quickly replan a safe path for the UAV, thus satisfying the requirement of UAV on the real-time path planning.

A system operates to guide an aircraft to or along a route designed to maintain the aircraft within a safe glide distance of an acceptable emergency landing area. The system uses a database of emergency landing areas with glide characteristics of an aircraft to determine a route that minimizes travel time or other specified parameter, while keeping the aircraft within a safe glide distance of a landing area in the database meeting the landing requirements for the aircraft.

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.

An autonomous obstacle avoidance and navigation method for unmanned aerial vehicle (UAV)-based field search and rescue provided by the invention includes global navigation based on GPS signals and anautonomous obstacle avoidance and navigation algorithm based on binocular vision and ultrasound. First, a UAV carries out search under the navigation of GPS signals according to a planned path. Aftera target is found, the autonomous obstacle avoidance and navigation algorithm based on binocular vision and ultrasound is adopted. A binocular camera acquires a disparity map containing depth information. The disparity map is re-projected to a 3D space to get a point cloud. A cost map is established according to the point cloud. The path is re-planned using the cost map to avoid obstacles. When there is no GPS signal, a visual inertial odometry (VIO) is used to ensure that the UAV flies according to the planned path, and an ultrasonic sensor is used to detect and avoid obstacles below the UAVto approach the rescued target. UAV autonomous obstacle avoidance and navigation in a field search and rescue scene is realized.

The present invention relates to a robot automatic charging method and its automatic charging device. The invented method includes the following steps: (1), when the robot has need of supplementing power supply, the infrared signal receiving device positioned in the front portion of said robot is switched on, said robot can be randomly walked to search infrared charging guide signal on the charging socket; (2), after the guide signal is found, the infrared receiving tube by which the guide signal is received can be defined, and the infrared receiving tube can be utilized to calculate the distance and angle from the charging socket to robot; (3), the position of said robot can be regulated to the right in robot's ahead; (4), the robot can be walked toward the right ahead, the butt electrode of said robot is contacted with charging electrode on the charging socket to implement butt-joint; and (5), the infrared signal receiving device can be switched off.

The invention provides an automatic taking-off and landing system, comprising a flying object and a taking-off and landing target, wherein the flying object has an image pickup device 21 for taking images found in downward direction, navigation means 4, 5, 6, 8, 9, 10 and 11, and a control unit for processing images acquired by the image pickup device and for controlling the navigation means, and wherein the control unit calculates a positional relation between the taking-off and landing target and the flying object based on the image of the taking-off and landing target as acquired by the image pickup device and controls taking-off and landing operations of the flying object based on a result of the calculation.

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.

The invention relates to a machinery automatic steering control method which comprises the following steps that: positional deviation and course deviation are determined; according to the variance in the practical front wheel rotating angle of an agricultural machine, online setting of PID parameter is completed by means of parameter self-setting PID control algorithm; moreover, the expected front wheel rotating angle of the next moment is calculated, thereby realizing automatic steering control of the agricultural machine. Based on conventional PID navigation control method, the invention makes full use of the fuzzy control method; according to the variance in the practical front wheel rotating angle of an agricultural machine, the invention meets the different requirements of the machine on PID control parameter under different errors and error change rate, thereby realizing online setting of PID parameter. The invention not only has the advantages of fuzzy control such as flexibility and adaptability, but also has the characteristics of higher precision of PID control; therefore, the invention can increase the stability and precision of agricultural machine automatic steering control and the robustness of a control system.

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.

A unmanned aerial vehicle (UAV) visual tracking method comprises the steps of obtaining image information of a region where a target object is located; identifying the target object, including acquiring target object information in the image information and determining the target object according to the target object information; positioning and visual tracking the successfully identified target object; and generating an abnormal prompt signal when the target object is not successfully identified or the positioning or visual tracking is abnormal, and actuating a prompt device according to the abnormal prompt signal to prompt the abnormality. The invention further relates to a UAV visual tracking apparatus and a UAV. The UAV visual tracking method can feed back clear and concise results of a visual tracking algorithm and a flight control system in time, thereby improving the visual tracking effect and user experience.

A flight control operation of a reference aerial vehicle is performed. For example, an image captured by an image sensor of the reference aerial vehicle is received. A target is detected in the image. A three-dimensional relative location of the target with respect to the reference aerial vehicle is determined based on the image. The flight control operation is performed based on the three-dimensional relative location of the target with respect to the reference aerial 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.

A navigation system for an aircraft, including a calculator of an estimated flight path for the aircraft; a device for determining an estimated value of a flight parameter that corresponds to an estimated speed for the extension of at least a portion of the high-lift devices of the aircraft; and a display of an indication for such estimated value. A command process for such a system and an aircraft equipped with such a system are also disclosed.

The invention discloses a method for detecting a moving target in an area to be monitored based on an unmanned aerial vehicle. In the method, the moving target is tracked and positioned through cooperative detection among multiple unmanned aerial vehicles. The method comprises the following steps of: detecting a corresponding subarea by using the unmanned aerial vehicle; making other unmanned aerial vehicles enter the subarea for cooperative detection; and predicting and tracking the moving track of the moving target. By the method for cooperatively detecting the moving target by using the multiple unmanned aerial vehicles, based on the cooperation among the multiple unmanned aerial vehicles, the moving target in a detection area can be detected, positioned and tracked, and the detect of low positioning and tracking accuracy of the moving target in the prior art is overcome; and the target can be accurately positioned, and the positioning accuracy of the target is improved.

An automated docking system for space vehicles that includes a plurality of antennas on each of a target vehicle and a chase vehicle. A pseudo random code is transmitted via one of the antennas located on one of the vehicles and received by one of the antennas located on the other vehicle. The pseudo random code is then sent back to the original vehicle via transmissions from the plurality of antennas on the second vehicle and received by the plurality of antennas on the original vehicle. The distance from each of the antennas on the other vehicle to each of the antennas on the originating vehicle can be measured in this fashion. The antennas on each of the vehicles are located in a spaced-apart arrangement so that the angular orientation or attitude of each of the vehicles to each other can also be determined. A plurality of video cameras is provided on the exterior of one of the vehicles and video information from these cameras is transmitted to the other vehicle for display to operators in that vehicle. Commands between the vehicles can also be communicated.

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.

Control handover planning for an unmanned aerial vehicle (UAV) is provided that includes determining, by a planning system, a current location of a mobile control system and a current location of the UAV. A target location is identified. A control handover zone is determined based on a communication range constraint between the mobile control system and the UAV. The control handover zone is located between the current location of the mobile control system, the current location of the UAV, and the target location. A mobile control system path plan and a UAV path plan are created that each includes a control handover waypoint in the control handover zone at the same time. The control handover waypoint defines a planned location to place the mobile control system in control of the UAV.

A method for making a mobile unit accompany an objective person moves the mobile unit in correspondence with movement of the objective person and executes control so that the mobile unit moves along the walking direction of the objective person by detecting the foot landing of the objective person, detecting the walking speed and the walking direction of the objective person, and predicting the predictive walking range of the objective person on the basis of the detected information of the walking direction and the foot landing.

The invention relates to a vision-based target tracking system using an unmanned helicopter, which relates to intelligent control technology. The vision-based target tracking system using the unmannedhelicopter comprises the unmanned helicopter, a visual acquisition and processing unit, a central processing unit, a GPS unit and a gyroscope inertial navigation unit, wherein the unmanned helicopteris transformed from a remote-control helicopter Thunder Tiger 90 for a model aircraft competition, and can be switched between an automatic driving mode and a manual driving mode; the visual acquisition and processing unit consists of an ICETEK DM642-B embedded DSP board of realtime company; the central processing unit consists of an H9200F ARM board of Hengyi High-tech company; the GPS unit consists of a superstar receiver and an antenna which are produced by America; and the inertial navigation unit consists of a 3DM-GX1 of MicroStrain company. The vision-based target tracking system usingthe unmanned helicopter realizes the functions of vision navigation control and target tracking of the unmanned helicopter within a local scope.

A system for aerial neutralization of a detected target aerial vehicle comprises a plurality of counter-attack unmanned aerial vehicles (UAVs), and an aerial vehicle capture countermeasure coupling together the plurality of counter-attack UAVs, to intercept and capture a detected target aerial vehicle in a coordinated manner. The system comprises an aerial vehicle detection system comprising at least one detection sensor operable to detect the target aerial vehicle, and operable to provide command data to at least one counter-attack UAV for tracking and neutralizing the target aerial vehicle. The counter-attack UAVs and a net can be deployed from a movable base station, and the net can be carried in a low-drag configuration until the counter-attack UAVs operate to deploy or open the net. The counter-attack UAVs and systems may be autonomously operated. Associated systems and methods are provided.

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.

A fire control system for a boost phase threat missile includes sensors for generating target-missile representative signals, and a multi-hypothesis track filter, which estimates the states of various target hypotheses. The estimated states are typed to generate hypotheses and their likelihoods. The states, hypotheses and likelihoods are applied to a multihypothesis track filter, and the resulting propagated states are applied to an engagement planner, together with the hypotheses and likelihoods. The engagement planner initializes the interceptor(s). Interceptor guidance uses the initialization and the propagated states and typing information to command the interceptor.

The invention discloses an unmanned aerial vehicle flight control method. The method includes the following steps that: the current latitude-longitude coordinates of an unmanned aerial vehicle and the current latitude-longitude coordinates of a target object are acquired; an azimuth angle between the unmanned aerial vehicle and the target object is calculated according to the current latitude-longitude coordinates of the unmanned aerial vehicle and the current latitude-longitude coordinates of the target object; and the course angle of the unmanned aerial vehicle is adjusted according to the azimuth angle, so that the unmanned aerial vehicle can face the target object. The invention also discloses an unmanned aerial vehicle flight control device. With the unmanned aerial vehicle flight control method and device of the invention adopted, the unmanned aerial vehicle can automatically follow the target object.