An unmanned aerial vehicle according to certain embodiments generally includes a chassis, a power supply mounted to the chassis, a control system operable to receive power from the power supply, at least one rotor operable to generate lift under control of the control system, and a winch mounted to the chassis. The winch includes a reel and a motor. The reel has a line wound thereon, the line having a free end. The reel includes a circumferential channel in which a wound portion of the line is wound onto the reel. The circumferential channel includes an inner portion, an outer portion, and a passage connecting the inner portion and the outer portion. The motor is operable to rotate the reel under control of the control system to thereby cause the line to wind onto and off of the reel, thereby causing the free end of the line to raise and lower.

Disclosed herein is a mobile device including a communication section that performs communication with a controller which selectively transmits control signals to a plurality of mobile devices, and a data processing section that performs movement control of the own device. The data processing section confirms whether or not an own-device selection signal which indicates that the own device is selected as a control target device has been received from the controller and, upon confirming reception of the own-device selection signal, performs movement control to cause the own device to move in accordance with a selected-device identification track which indicates that the own device is selected as the control target device.

Systems, methods, and computer-readable media are disclosed for drone vehicle integration and controls. A vehicle device for controlling an unmanned aerial vehicle (UAV) may receive an input indicating a request to deploy the UAV from a vehicle. The vehicle device may determine that one or more deployment conditions are satisfied. The vehicle device may cause deployment of the UAV. The vehicle device may determine a control command for the UAV and a vehicle instruction associated with operating the UAV. The vehicle device may determine that the vehicle instruction has been satisfied, and may send the control command once the vehicle instruction is satisfied.

A method includes receiving, by machine-learning logic, observations indicative of a states associated with a first and second group of vehicles arranged within an engagement zone during a first interval of an engagement between the first and the second group of vehicles. The machine-learning logic determines actions based on the observations that, when taken simultaneously by the first group of vehicles during the first interval, are predicted by the machine-learning logic to result in removal of one or more vehicles of the second group of vehicles from the engagement zone during the engagement. The machine-learning logic is trained using a reinforcement learning technique and on simulated engagements between the first and second group of vehicles to determine sequences of actions that are predicted to result in one or more vehicles of the second group being removed from the engagement zone. The machine-learning logic communicates the plurality of actions to the first group of vehicles.

Systems, methods, and devices are provided for controlling an unmanned aerial vehicle (UAV) associated with flight response measures. The flight response measure may be generated by assessing one or more flight-restriction strips, assessing at least one of a location or a movement characteristic of the UAV relative to the one or more flight-restriction strips, and directing, with aid of one or more processors, the UAV to take one or more flight response measures based on at least one of the location or movement characteristic of the UAV relative to the one or more flight-restriction strips.

Various embodiments of a vision-guided unmanned aerial vehicle (UAV) system to identify and collect foreign objects from the surface of a body of water are disclosed herein. A vision system and methodology has been developed to reduce reflections and glare from a water surface to better identify an object for removal. A linearized polarization filter and a specularity-removal algorithm is used to eliminate excessive reflection and glare. A contour-based detection algorithm is implemented for detecting the targeted objects on water surface. Further, the system includes a boundary layer sliding mode control (BLSMC) methodology to reduce and minimize position and velocity errors between the UAV and object in the presence of modeling and parameter uncertainties due to variation in a moving water surface.

A method and a system for establishing a route of an unmanned aerial vehicle are provided. The method includes identifying an object from surface scanning data and shaping a space, which facilitates autonomous flight, as a layer, collecting surface image data for a flight path from the shaped layer, and analyzing a change in image resolution according to a distance from the object through the collected surface image data and extracting an altitude value on a flight route.

An unmanned aerial vehicle (“UAV”), the UAV including an electronic speed controller and a flight controller. The electric speed controller is interfaced with thrust motors of the UAV. The flight controller is configured to: determine a geographic location and a velocity of the UAV. The flight controller configured to: determine a distance between the geographic location of the UAV and a closest segment of a no-fly-zone. The flight controller in response to the distance being less than a threshold distance, control a speed and thrust applied by the thrust motors through the electric speed controller to reduce both the first component and the second component of the velocity of the UAV based on the distance. The flight controller configured to: override a user input received via a user interface so that the UAV is moved relative to the closest segment of a no-fly-zone according to instructions from the flight controller.

A security monitoring system may implement a method for surveilling an outdoor area using a drone. The method involves receiving an input to initiate a pre-surveillance operation. The input indicates a type of pre-surveillance operation to be performed in the outdoor area. The drone may be configured according to the input and may then perform the pre-surveillance operation to obtain data indicative of environmental features in the outdoor area. A flight trajectory path for the drone is generated based on the data indicative of the environmental features in the outdoor area. The flight trajectory path includes a path for the drone to move within the outdoor area. The drone then performs a detailed surveillance of the outdoor area according to the flight trajectory path. A graphical representation of the outdoor area is generated based on data obtained from performing the surveillance of the outdoor area.

Image data is used to determine wind speed and wind direction during takeoff and landing by an unmanned aerial vehicle (UAV). The flight data, including image data, may be received using sensors onboard the UAV and/or the flight data may be received from other sources, such as nearby anemometer, cameras, other UAVs, other vehicles, and/or local weather stations. Machine learning models may train using the flight data gathered by the UAVs to determine the wind velocity based on image data. The UAV may adjust flight control settings to generate side forces to overcome the predicted wind velocity.