52results about "Target-seeking control" patented technology

An autonomous unmanned aerial vehicle (10) comprising an airframe body; at least one flight system mounted to the airframe body(12); an onboard flight controller (18) which is adapted to control the or each flight system; a memory storage unit having machine-readable flight control instructions which are implementable by the onboard flight controller; an onboard feedback system which is communicatively coupled with the or each flight system to provide real-time internal flight characteristic data to the onboard flight controller(18); and an external feedback system adapted to receive and provide to the onboard flight controller (18) real-time external flight characteristic data; wherein the onboard flight controller (18) is arranged to receive mission parameter data from an external source, determine a pre- take-off flight plan in accordance with the mission parameter data, and dynamically implement the machine-readable flight control instructions to adapt the pre-take-off flight plan to control the or each flight system based on the real-time internal flight characteristic data and real-time external flight characteristic data.

A system comprising an unmanned aerial vehicle (UAV) (100) configured to transition (520) from a terminal homing mode (510) to a target search mode (530), responsive to an uplink signal (451) and / or an autonomous determination of scene change.

This invention provides a method for UAV obstacle avoidance trajectory planning based on digital elevation maps, including digital elevation map conversion, initial trajectory loading, trajectory pre-planning, and trajectory re-planning. Using this invention, UAV obstacle avoidance trajectory planning can fully consider terrain variations during low-altitude flight missions to achieve ground-hugging flight. Simultaneously, this method first performs offline trajectory pre-planning and uploads the planned trajectory to the flight controller to provide flight guidance, effectively solving the problem of low real-time performance. When obstacles or threat areas appear on the pre-planned trajectory, the method provided by this invention can find the vertices of obstacles with complex shapes, handle obstacle overlap issues, and thus quickly plan a flight path.

There is provided a toy, including a ball chasing robot including: a controller configured to receive power from a battery a plurality of infrared sensors configured to detect infrared radiation emitted by a satellite object, the plurality of infrared sensors connected to the controller; a motor configured to drive a wheel arrangement, the wheel arrangement including at least two drive wheels, wherein: the controller is configured to control the motor and wheel arrangement to drive the ball chasing robot towards the infrared radiation emitted by the satellite object detected by the plurality of infrared sensors; wherein the ball chasing robot includes a plurality of settings, including: a play setting, wherein the controller is configured to control the motor, such that the ball chasing robot is driven towards the source of the infrared radiation emitted by the satellite object, and a sleep setting wherein the controller is configured to turn off the plurality of sensors, wherein the sleep setting is entered automatically after a period of inactivity; and / or: a hard floor setting, wherein the motor is configured to drive the wheel arrangement at a first power setting, and a carpet setting, wherein the motor is configured to drive the wheel arrangement at a second power setting.

A vehicle to transport a first drone and a second drone includes a controller. The controller is configured to release the first drone from the vehicle with instructions for the first drone to move a package container to a package container reception point. The controller is configured to release the second drone from the vehicle. The second drone is configured to provide data to the first drone. The data is related to a route from the vehicle to the package container reception point.

Systems, methods, and devices are provided herein for effecting autonomous flight of one or more unmanned aerial vehicles (UAV), and modifying the autonomous flight. In some instances, the autonomous flight may be effected via a first user input. The autonomous flight may modified by a second user input while maintaining the autonomous flight. The first and second inputs may be input at different user interfaces. Various parameters of the UAV, including a flight path, may be modified while maintaining the autonomous flight such that user is assisted and maintains a degree of control of the UAV during the autonomous flight.

A system including an image capture module and a handheld module. The image capture module includes a body; an image sensor; and a mechanical stabilization system comprising a first gimbal, a second gimbal, and a third gimbal connected to the body and configured to control an orientation of the image sensor of the image capture module relative to the body. The handheld module defines a slot that is keyed to the body of the image capture module. The image capture module, when located within the handheld module, has a low profile so that the third gimbal is protected from damage by the handheld module.

Systems and methods for detection of potential obstructions in a specified space. In some embodiments, the system and / or method comprises a robotic vehicle having one or more sensors configured to collect point cloud data from locations at or near a target drop zone. At least one processor performs an object detection analysis using the point cloud data to determine if there is an object in the target drop zone. To determine if there is an object or obstruction at the target drop zone, a volume of interest that is about the size of a payload can be generated for the target drop zone. If point cloud data indicates an object within the VOI, an obstruction exists and the payload is held rather than dropped at the target drop location.

The application discloses a differential AGV path following method and system based on improved pure tracking control, and the method comprises the following steps: obtaining a path trajectory by using cubic B-spline curve fitting; taking the midpoint of an axle as an AGV reference point; selecting a current trajectory reference point according to the AGV reference point; calculating a turning radius and planning a set speed according to the trajectory reference point; calculating a preview mark length according to the current speed difference between the left and right wheels of the differential AGV; calculating the coordinates of a preview point according to the path length from the starting point of the current path segment to the trajectory reference point, the current path segment length, the relationship between the current path segment and the path trajectory and the preview mark length; calculating the preview distance and the included angle between the AGV reference point and the trajectory reference point according to the coordinates of the preview point and the AGV reference point; and calculating the speeds of the left and right wheels of the differential AGV by using a kinematic model of the differential AGV. The method solves the problems that pure tracking control causes oscillation due to too short preview distance during straight-line driving and poor following due to too long preview distance during curve driving.

Systems and methods for modifying autonomous flight of one or more unmanned aerial vehicles (UAVs) are provided herein. In some instances, autonomous flight of a UAV can be implemented by first user input received at a first user interface. While autonomous flight is maintained, second user input received at a second user interface can modify autonomous flight of the UAV. Wherein the second user input modifies autonomous flight of the UAV during a duration of the second user input to assist a user and modify autonomous flight of the UAV during autonomous flight.

Guidance systems and methods for a material handling vehicle are provided. A guidance system can include a sensor and a processor unit. The sensor can be configured to measure a distance to an external object within a field of view of the sensor and output distance information corresponding to the measured distance. The processor can be configured to receive the distance information from the sensor, transform the distance information from a sensor coordinate system to a material handling vehicle coordinate system, perform pattern recognition on the transformed distance information to identify an aisle feature, create an aisle model based on the identified aisle feature, and determine a guidance instruction for the material handling vehicle based on the aisle model. The aisle model can include information corresponding to the aisle feature. The guidance instruction may be configured to align the material handling vehicle within an aisle.

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.

Disclosed is a process for collaborating between a fixed camera and an unmanned mobile device to enhance identification of an object of interest. A first point of view (POV) of a detected object is obtained, and it is determined, with a first level of confidence, that the detected first POV of the object matches a stored object of interest. Then, one or more camera-equipped unmanned mobile vehicles are identified in a particular direction of travel of the first detected object, and dispatch instructions and intercept information are then transmitted to the one or more camera-equipped unmanned mobile vehicles. Subsequently, a detected second POV of the first detected object is received via the one or more camera-equipped unmanned mobile vehicles.The captured second POV of the detected object is used to determine with a second level of confidence that the detected object matches the stored object of interest.

A system and method for patrol inspection of a drone (33) on a meshed dock (3, 34) including a plurality of docks (3, 34) arranged in a mesh and each used to accommodate at least one drone (33), wherein the dock (3, 34) includes a dock controller communicating with a control terminal, the dock controller communicating with a drone remote control, the drone remote control communicating with the drone (33), and the control terminal is used to generate an optimal patrol inspection route for the drone (33) based on the current flight range of the drone (33) and the distance from the patrol inspection target to each dock (3, 34) with the optimization goal of minimizing the patrol inspection time, and send it to the dock controller, with beneficial effects being to realize efficient cooperative patrol inspection of the drone (33) based on the drone docks (3, 34) arranged in a mesh, reduce labor costs, and meet the needs of regular or emergency patrol inspection of a plurality of patrol inspection targets across a plurality of different fields.

The system comprises a plurality of sensor systems, a counter drone, and a processor. A sensor system of the plurality of sensor systems comprises one or more sensors that are connected to a network. The counter drone is connected to the network. The processor is configured to receive an indication of a potential target from the plurality of sensor systems; generate a fused data set for the potential target, determine whether the potential target comprises the threat drone based at least in part on the fused data set; and in response to determining that the potential target comprises the threat drone, provide counter drone instructions to the counter drone.

A method for defending a predetermined area from an autonomously moving Unmanned Aerial Vehicle (UAV) is provided. The method includes generating one or more adversarial example adapted to disrupt a machine-learning based vision system of the UAV. Additionally, the method includes determining, based on geographical information about at least one of the predetermined area and a surrounding area of the predetermined area, a respective position for the one or more adversarial example in at least one of the predetermined area and the surrounding area of the predetermined area.

The present disclosure relates to a flight obstacle detection avoidance method and device, electronic equipment and storage medium. The method comprises receiving cooperative obstacle information sent by an information center of a flight vehicle, determining non-cooperative obstacle information other than the cooperative obstacle information through a sensing device of the flight vehicle or the information center, determining corresponding obstacle motion trajectory information and collision warning information according to the cooperative obstacle information and the non-cooperative obstacle information, and displaying the obstacle motion trajectory information and the collision warning information. The embodiment of the present disclosure can simultaneously detect cooperative obstacles and non-cooperative obstacles near the flight vehicle through the sensing device installed on the flight vehicle and the information center connected through communication, thereby improving the comprehensiveness and accuracy of obstacle detection. Meanwhile, the embodiment of the present disclosure can generate and display obstacle motion trajectory information and collision warning information according to the obstacle information, so as to generate avoidance guidance and automatically or manually avoid the obstacles in time, thereby reducing the collision probability of the flight vehicle.

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 present application relates to remote sensing control technical field, especially unmanned aerial vehicle path planning method, device, equipment and storage medium are provided.The present application obtains initial point cloud image according to the unmanned aerial vehicle path planning request, and determines initial unmanned aerial vehicle path according to initial path point position, collects target point cloud image of each initial path point in initial unmanned aerial vehicle path, finally compares the target point cloud image collected with preset point cloud image, so as to adjust initial unmanned aerial vehicle path, reach the purpose of planning unmanned aerial vehicle path, avoid the technical problem that the traditional unmanned aerial vehicle path planning process in prior art mainly relies on artificial completion on two-dimensional map, is easy to receive interference, improve the efficiency of unmanned aerial vehicle path planning.

An unmanned aerial vehicle riding route processing method, apparatus and device, and a readable storage medium, the method includes: determining candidate ride vehicles according to an autonomous flight route of an unmanned aerial vehicle from a flight start point to a flight destination (S101); determining a riding flight route of the unmanned aerial vehicle according to current locations of the candidate ride vehicles (S102); and controlling the unmanned aerial vehicle to ride at least one of the candidate ride vehicles to travel from the flight start point to the flight end point according to the riding flight route (S103). The unmanned aerial vehicle rides at least one vehicle when traveling from the flight start point to the flight end point, thus greatly reducing a distance that the unmanned aerial vehicle flies relying on its own power, thereby saving power consumption of the unmanned aerial vehicle and extending a delivery distance of the unmanned aerial vehicle.

A method includes navigating, by a UAV, to a delivery location in an environment; capturing, by at least one sensor on the UAV, sensor data representative of the delivery location; determining, based on the sensor data, a segmented point cloud of the delivery location, wherein the segmented point cloud defines a plurality of point cloud areas with corresponding semantic classifications; determining, based on the segmented point cloud, that a pre-selected delivery point at the delivery location satisfies a condition indicating that a descent path through a cylinder, the cylinder being centered above the pre-selected delivery point and having a radius of a particular lateral distance, does not intersect with any point cloud areas having semantic classifications indicative of an obstacle at the delivery location; and based on determining that the pre-selected delivery point satisfies the condition, initiating, by the UAV, a payload delivery operation towards the pre-selected delivery point.

An estimated target position is generated based on agent data received from an agent sensor system of a reinforcement learning (RL) agent and teammate data received from a plurality of teammate RL agents. An information metric reward is generated based on a confidence level associated with the estimated target position. The confidence level is based on the estimated target position and historical estimated target positions. A distance metric reward is generated based on an agent position and the estimated target position. A combined reward is generated based on the information metric reward and the distance metric reward. A movement action is generated for the RL agent based on the agent position, the estimated target position, and the combined reward in accordance with a multi-agent reinforcement learning (MARL) algorithm.

The application provides a robot tracking method, a control terminal, a robot and a storage medium. The method comprises the following steps: if the confidence of a target tracker is less than a first preset value, determining that an initial target is lost; if the confidence of the target tracker is not less than the first preset value, acquiring an HSV feature of a current frame image, a previous frame image and an initial target, and determining a tracking probability based on the HSV feature of the current frame image, the previous frame image and the initial target; if the tracking probability is less than a second preset value, determining that the initial target is lost; and if the tracking probability is not less than the second preset value, determining that the initial target is not lost. According to the application, whether the target is lost is preliminarily determined according to the confidence, and whether the target is lost is determined again through the HSV feature, so that whether the tracking target is lost can be accurately determined, and the accuracy of the determination is effectively improved.

The present invention relates to a golf cart operating by recognizing a specific behavior of a user as a routine, and a golf cart control method therefor, and, more specifically, comprises: at least one remote controller (100) carried by a user, and a cart (200) capable of performing autonomous driving, stopping, and re-starting by exchanging signals with the remote controller (100), wherein the cart (200) recognizes the user behind the user and performs autonomous driving while keeping a predetermined distance from the user, and, if a specific behavior of the user is recognized, the cart (200) can stop or re-start in response to the specific behavior so that the user can conveniently use the cart even without issuing a command by using the remote controller (100) during stopping or re-starting of the cart.

To provide a sensor arrangement recommendation device with which it is possible to recommend efficient arrangement positions of a plurality of sensors when searching for a target.SOLUTION: A sensor arrangement recommendation device 20 comprises: a prediction unit 21 that predicts, for an object which moves in an underwater search area and which is the target searched by a sensor of a search system, a target presence zone in the search area where the target is present after elapse of a prescribed time; a first determination unit 22 that determines a sensor arrangement position at which a probability of searching for the target is highest, using the predicted target presence zone, after the elapse of the prescribed time; and a second determination unit 23 that determines to locate a new sensor in an area at which the arranged sensor does not satisfy a prescribed condition for searching for the target in the predicted target presence zone, when the sensor is located at the determined arrangement position.SELECTED DRAWING: Figure 11

The embodiment of the application discloses a kind of offshore oil spill detection method based on image segmentation, unmanned plane, storage medium, wherein, method includes: determining inspection area, inspection area includes multiple inspection nodes;Determine initial inspection path according to multiple inspection nodes, initial inspection path passes each inspection node once;With initial inspection path as the initialization population of artificial bee colony algorithm, determine the target path of optimal energy consumption by artificial bee colony algorithm;According to target path, inspection is carried out, and the image to be detected is photographed at each inspection node by SAR image imager;The image to be detected is input to pre-trained oil spill dark spot segmentation model and is subjected to image segmentation processing, to obtain segmented image;Determine offshore oil spill detection result of inspection node according to segmented image.The embodiment of the application can be combined with the unmanned plane provided with SAR image imager and oil spill dark spot segmentation model to carry out offshore oil spill detection on inspection area, to effectively improve the efficiency of offshore oil spill detection.

Disclosed herein are apparatuses and methods for operating an unmanned mobile machine (UMM) to capture video of potential incidents, including obtaining, from a UMM capturing video of a potential incident, an indication of a charge level of a battery of the UMM, and determining, based at least in part on the charge level, whether to dispatch a second UMM to a location associated with the UMM.

Provided are a device and a method for efficiently creating a following path of the target to be followed by a mobile device to enable reliable follow-up. A movement path creation unit is included that creates a path that is a following path on which a target is to be followed by a mobile device. The movement path creation unit sequentially sets a piece of path position information (waypoint) near the target, and sequentially connects the set pieces of path position information to create the path. The movement path creation unit sets the piece of path position information at a position determined in accordance with a predetermined rule based on a position and a travel direction of the target. When the target has moved a predetermined distance, a new piece of path position information is set.