Abstract: The present application provides a system for unmanned aerial vehicle (UAV) parachute landing. An exemplary system includes a detector configured to detect at least one of a flight speed, a wind speed, a wind direction, a position, a height, and a voltage of a UAV. The system also includes a memory storing instructions and a processor configured to execute the instructions to cause the system to: determine whether to open a parachute of the UAV in accordance with a criterion, responsive to the determination to open the parachute of the UAV, stop a motor of the UAV that spins a propeller of the UAV, and open the parachute of the UAV after stopping the motor of the UAV for a first period.

Abstract: An unmanned ground vehicle (UGV) includes one or more motors configured to drive one or more wheels of the UGV, a memory storing instructions, and a processor coupled to the one or more motors and the memory. The processor is configured to execute the instructions to cause the UGV to determine location information of a movable target; calculate a direction and a speed for the unmanned ground vehicle based on the determined location information; and drive the one or more motors to move the unmanned ground vehicle in the calculated direction at the calculated speed to follow the movable target when the movable target moves.

Abstract: A method for image-guided agriculture includes receiving images; processing the images to generate reflectance maps respectively corresponding to spectral bands; synthesizing the reflectance maps to generate a multispectral image including vegetation index information of a target area; receiving crop information in regions of the target area; and assessing crop conditions for the regions based on the identified crop information and the vegetation index information.

Abstract: A method for image-guided agriculture includes capturing images based on one or more ground sampling distance values; processing the images to generate an orthophoto image of a target area; performing feature classification of the orthophoto image to identify corresponding crop information in regions of the target area; and assessing crop conditions in the regions based on one or more vegetation indices and the corresponding crop information in the regions.

Abstract: A method for image-guided agriculture includes receiving images; processing the images to generate reflectance maps respectively corresponding to spectral bands; synthesizing the reflectance maps to generate a multispectral image including vegetation index information of a target area; receiving crop information in regions of the target area; and assessing crop conditions for the regions based on the identified crop information and the vegetation index information.

Abstract: An unmanned ground vehicle (UGV) includes one or more motors configured to drive one or more wheels of the UGV, a memory storing instructions, and a processor coupled to the one or more motors and the memory. The processor is configured to execute the instructions to cause the UGV to determine location information of a movable target; calculate a direction and a speed for the unmanned ground vehicle based on the determined location information; and drive the one or more motors to move the unmanned ground vehicle in the calculated direction at the calculated speed to follow the movable target when the movable target moves.

Abstract: The present application provides an unmanned aerial vehicle (UAV) for a long duration flight. An exemplary UAV may include a UAV body assembly. The UAV may also include a flight control system (FCS) coupled to the UAV body assembly. The UAV may further include a motor coupled to the UAV body assembly at one end and coupled to a propeller at the other end. The FCS is communicatively connected to the motor. A center of gravity (CG) of the UAV is at a point between 21% and 25% of a mean aerodynamic chord (MAC) of the UAV.

Abstract: An unmanned ground vehicle (UGV) includes one or more motors configured to drive one or more wheels of the UGV, a memory storing instructions, and a processor coupled to the one or more motors and the memory. The processor is configured to execute the instructions to cause the UGV to determine location information of a movable target; calculate a direction and a speed for the unmanned ground vehicle based on the determined location information; and drive the one or more motors to move the unmanned ground vehicle in the calculated direction at the calculated speed to follow the movable target when the movable target moves.

Abstract: The present application provides an unmanned aerial vehicle (UAV) for a long duration flight. An exemplary UAV may include a UAV body assembly. The UAV may also include a flight control system (FCS) coupled to the UAV body assembly. The UAV may further include a motor coupled to the UAV body assembly at one end and coupled to a propeller at the other end. The FCS is communicatively connected to the motor. A center of gravity (CG) of the UAV is at a point between 21% and 25% of a mean aerodynamic chord (MAC) of the UAV.

Abstract: An unmanned aerial vehicle (UAV) includes one or more processors, and a memory storing instructions. When executed by the one or more processors, the instructions cause the UAV to perform operations including: recognizing a first gesture of a hand; responsive to a recognition of the first gesture, moving the unmanned aerial vehicle to hover above the hand; detecting a distance between the unmanned aerial vehicle and the hand; responsive to a determination that the distance falls in a range, monitoring the hand to recognize a second gesture of the hand; and responsive to a recognition of the second gesture, landing the unmanned aerial vehicle on the hand.

Abstract: The present application provides route planning methods and apparatuses for an unmanned aerial vehicle. An exemplary route planning method may include determining a quadrant angle in accordance with a starting point and an ending point. The route planning method may also include determining a flight shifting distance in accordance with the quadrant angle, a first interval between waypoints, and a second interval between routes. The route planning method may further include generating a plurality of waypoints and a plurality of routes in accordance with the quadrant angle, the flight shifting distance, the first interval between waypoints, and the second interval between routes. In addition, the route planning method include planning a flight path in accordance with the plurality of waypoints and the plurality of routes.

Abstract: The present application provides methods and systems for launching an unmanned aerial vehicle (UAV). An exemplary system for launching a UAV includes a detector configured to detect acceleration of the UAV in a launch mode. The exemplary system may also include a memory storing instructions and a processor configured to execute the instructions to cause the system to: obtain a signal configured to notify the UAV to enter the launch mode, determine whether the acceleration of the UAV satisfies a condition corresponding to threshold acceleration in the launch mode, and responsive to the determination that the acceleration of the UAV satisfies the condition, turn on a motor of the UAV.

Abstract: An unmanned aerial vehicle (UAV) includes one or more motors configured to drive one or more propellers of the UAV, a motion sensor configured to determine a motion parameter of the UAV, a memory storing instructions, and a processor coupled to the one or more motors, the motion sensor, and the memory. The processor is configured to execute the instructions to cause the UAV to determine whether a hand thrown mode is selected for the UAV and whether the one or more motors are turned off; responsive to a determination that the hand thrown mode is selected, receive a motion parameter from the motion sensor; and activate the one or more motors when the motion parameter is greater than a threshold value.

Abstract: An unmanned aerial vehicle (UAV) includes one or more processors, and a memory storing instructions. When executed by the one or more processors, the instructions cause the UAV to perform operations including: recognizing a first gesture of a hand; responsive to a recognition of the first gesture, moving the unmanned aerial vehicle to hover above the hand; detecting a distance between the unmanned aerial vehicle and the hand; responsive to a determination that the distance falls in a range, monitoring the hand to recognize a second gesture of the hand; and responsive to a recognition of the second gesture, landing the unmanned aerial vehicle on the hand.

Abstract: Methods and systems for detecting objects from aerial imagery are disclosed. The method includes obtaining an image of an area, obtaining a plurality of regional aerial images from the image of the area, classifying the plurality of regional aerial images as a first class or a second class by a classifier, wherein: the first class indicates a regional aerial image contains a target object, the second class indicates a regional aerial image does not contain a target object, and the classifier is trained by first and second training data, wherein the first training data include first training images containing target objects, and the second training data include second training images containing target objects obtained by adjusting at least one of brightness, contrast, color saturation, resolution, or a rotation angle of the first training images; and recognizing a target object in a regional aerial image in the first class.

Abstract: Methods and systems for detecting objects from aerial imagery are disclosed. The method includes obtaining an image of an area, obtaining a plurality of regional aerial images from the image of the area, classifying the plurality of regional aerial images as a first class or a second class by a classifier, wherein: the first class indicates a regional aerial image contains a target object, the second class indicates a regional aerial image does not contain a target object, and the classifier is trained by first and second training data, wherein the first training data include first training images containing target objects, and the second training data include second training images containing target objects obtained by adjusting at least one of brightness, contrast, color saturation, resolution, or a rotation angle of the first training images; and recognizing a target object in a regional aerial image in the first class.