The present disclosure describes a system that includes: an unmanned aerial vehicle (UAV) comprising one or more on-board batteries, and a self-powering payload coupled to the UAV, wherein the self-powering payload comprises: one or more transformable devices configured to alter an orientation of the one or more transformable devices with respect to a transmission line in proximity to the UAV when the UAV is air-borne; one or more coil devices mounted on the one or more transformable devices and configured to capture a magnetic flux of an electromagnetic field generated by the transmission line; and an electric circuit configured to generate, based on the captured magnetic flux, charging currents for the one or more on-board batteries on the UAV while the UAV is in air-borne.

A method for performing property surveillance via an unmanned aerial vehicle is provided. Surveillance of one or more properties via an unmanned aerial vehicle is obtained. A geofence that determines boundaries of one of the properties is created using the surveillance. Surveillance outside the geofence for the property is blurred. The blurred surveillance is provided to an owner of that property.

An unmanned aerial vehicle (UAV) system for maintaining roadway personnel safety includes a monitoring device configured to be worn by a user and to provide a notification, a UAV including a sensor configured to sense a signal indicating a railway condition and/or a railway event, and a ground station configured to house the UAV when the UAV is not in use. The ground station is further configured to be mounted on a train or vehicle. The system further includes a processor and a memory that contains instructions, which, when executed by the processor, cause the system to selectively deploy the UAV from the ground station when the ground station is supported on the train or the vehicle, receive the signal from the sensor, detect the railway condition and/or the railway event based on the received signal, and transmit the notification to the monitoring device based on the based on the sensed signal.

A system for the remote care of an item of vegetation used as a potential source of food for an animal, to include at least one of a proximity tag, RFID tag, transponder device, a predetermined machine-readable pattern, and geo-location device associated with the item of vegetation, and an aerial drone. The aerial drone includes a microprocessor, a sensor coupled to the microprocessor and configured to detect the at least one of the proximity tag, RFID tag, transponder device, predetermined machine-readable pattern, and the geo-location device, and a carrier configured to carry a substance comprising at least one of a solid, gas, and liquid. The aerial drone is configured to act in response to instructions issued by the microprocessor.

An aerial vehicle including a main body having a leading edge. An inlet is recessed aft from the leading edge. Forward protrusions extend from the main body on opposite sides of the inlet. An outlet nozzle is proximate to an aft end. The inlet is in fluid communication with the outlet nozzle. Wings extend from the main body.

System and method for a golf cart to automatically proceed to the location of a struck golf ball. The system and method allow a golfer to play golf without the need to keep diligent track of the ball. The golf cart determines the location of the struck ball and the proceeds to the location of the ball. This reduces the time needed to find a struck ball and reduces the instances of lost balls.

A method for monitoring a surveillance area includes receiving a plurality of video streams that are captured by a plurality of video cameras in the surveillance area, each of the plurality of video streams capturing one or more objects in the surveillance area that are within a camera field of view (FOV) of the corresponding video camera and from a camera vantage point of the corresponding video camera. A vantage point transformation is applied to each of two or more objects captured in two or more of the plurality of video streams, the vantage point transformation transforming a view of each of the two or more objects from the camera vantage point of the corresponding video camera to a common vantage point. The transformed view of each of the two or more objects from the common vantage point is rendered on a display.

An unmanned aerial vehicle capable of vertical takeoff and landing carries a remote sensing platform to a high altitude cruising altitude and flies over a target area, collecting remote sensing imagery before returning to earth. Instead of being piloted remotely, the vehicle employs an autonomous flight control system.

Embodiments of the invention provide an image sensor image sensor including an image sensor structure. The image sensor structure includes a multitude of image elements arranged in a grid-shaped manner in a first direction and in a second direction orthogonal to the first direction. An image element of the multitude of image elements includes a plurality of filter elements spatially arranged side by side. The plurality of filter elements includes at least one color filter and at least one additional filter from a filter group. The filter group includes a first absorption filter with a first optical bandwidth, a second absorption filter with a second optical bandwidth different from the first optical bandwidth, a first polarization filter with a first polarization characteristic, a second polarization filter with a second polarization characteristic different from the first polarization characteristic, and a filter element without absorption effect or polarization effect.

Embodiments of the present disclosure may include a method to augment pilot control of a drone, the method including receiving a planned flight route. Embodiments may also include receiving sensor information from an at least one environment sensor along the planned flight route. In some embodiments, the at least one environment sensor may be located at a predefined location. Embodiments may also include estimating a drone location from the sensor information. Embodiments may also include receiving a speed vector of the drone. Embodiments may also include comparing the drone location to an expected drone location along the planned flight route. Embodiments may also include deriving a flight control command and a speed vector command to return the drone to a point along the planned flight route.