Methods, systems and apparatus, including computer programs encoded on computer storage media for unmanned aerial vehicle visual line of sight flight operations. A UAV computer system may be configured to ensure the UAV is operating in visual line of sight of one or more ground operators. The UAV may confirm that it has a visual line of sight with the one or more user devices, such as a ground control station, or the UAV may ensure that the UAV does not fly behind or below a structure such that the ground operator would not be able to visually spot the UAV. The UAV computer system may be configured in such a way that UAV operation will maintain the UAV in visual line of sight of a base location.

A remotely-controlled (RC) and/or autonomously operated inspection device, such as a ground vehicle or drone, may capture one or more sets of imaging data indicative of at least a portion of an automotive vehicle, such as all or a portion of the undercarriage. The one or more sets of imaging data may be analyzed based upon data indicative of at least one of vehicle damage or a vehicle defect being shown in the one or more sets of imaging data. Based upon the analyzing of the one or more sets of imaging data, damage to the vehicle or a defect of the vehicle may be identified. The identified damage or defect may be compared to a claimed damage or defect to determine whether the claimed damage or defect occurred.

A common command and control architecture (alternatively termed herein as a universal control architecture) is disclosed that allows different unmanned systems, including different types of unmanned systems (e.g., air, ground, and/or maritime unmanned systems), to be controlled simultaneously through a common control device (e.g., a controller that can be an input and/or output device). The universal control architecture brings significant efficiency gains in engineering, deployment, training, maintenance, and future upgrades of unmanned systems. In addition, the disclosed common command and control architecture breaks the traditional stovepipe development involving deployment models and thus reducing hardware and software maintenance, creating a streamlined training/proficiency initiative, reducing physical space requirements for transport, and creating a scalable, more connected interoperable approach to control of unmanned systems over existing unmanned systems technology.

Autonomous aerial navigation in low-light and no-light conditions includes using night mode obstacle avoidance intelligence, training, and mechanisms for vision-based unmanned aerial vehicle (UAV) navigation to enable autonomous flight operations of a UAV in low-light and no-light environments using infrared data.

A method and system for performing painting using an unmanned aerial vehicle is disclosed herein. The system comprises an unmanned aerial vehicle and a user computing device. The unmanned aerial vehicle comprises a plurality of nozzles, at least one camera, at least one sensor, at least one software module, a plurality of paint containers, lidar, and a plurality of blades.

Systems and methods for performing insurance damage inspection by an unmanned aerial vehicle (UAV) are provided. A computing device may receive a request to inspect a vehicle, the request comprising a location of the vehicle. The computing device may identify a UAV from a plurality of UAVs that is located closest to the location of the vehicle from other UAVs in the plurality of UAVs. The computing device may instruct the UAV to travel to the location of the vehicle. The computing device may instruct the UAV to collect damage information on the vehicle using one or more onboard sensors of the UAV. The computing device may determine an amount of insurance payout to approve for repairs to the vehicle based on the damage information collected by the UAV.

A method is provided for controlling a movable imaging assembly having a movable platform and an imaging device coupled to and movable relative to the movable platform. The method includes receiving user inputs that define an MIA position relative to a target and a frame position of the target within image frames captured by the imaging device. The user inputs include a horizontal distance, a circumferential position, and a horizontal distance that define the MIA position, and include a horizontal frame position and a vertical frame position that define the frame position. The method further includes predicting a future position of the target for a future time, and moving the MIA to be in the MIA position at the future time and moving the imaging device for the target to be in the frame position for an image frame captured at the future time.

A method for filming an event by an autonomous drone, the method may include acquiring, by the autonomous drone, a current set of images of the event; generating signatures of the current set of images to provide current signatures; searching for one or more relevant concept structures out of a group of concept structures; wherein each relevant concept structure comprises at least one signature that matches at least one of first signatures; wherein each concept structure is associated with filming parameters; and determining, at least in part, based on the filming parameters associated with at least one of the one or more relevant concept structures, next filming parameters to be applied during an acquisition of one or more next sets of images.

The disclosure is generally directed to systems and methods for facilitating travel over a last leg of a journey. In one example embodiment, an unmanned aerial vehicle (UAV) captures an image of an area to be traversed by an individual during the final leg of the journey. The image is evaluated to identify a hindrance that may be encountered by the individual in the area. A pre-emptive action may be taken to address the hindrance before reaching the area. The pre-emptive action, may, for example, include using the UAV and/or a terrestrial robot to assist the individual traverse the area. The assistance may be provided in various ways such as by use of the terrestrial robot to transport the individual and/or a personal item of the individual through the area, and/or by use of the UAV to provide instructions to guide the individual through the area.

An unmanned aerial vehicle (UAV) solar irradiation assessment system may automate several design parameters of solar panel design, cost and payoff estimations, and installation. The system determines the irradiance at various locations on a roof during various time periods. The system accounts for the effects of various existing, potential, or future obstacles proximate the structure. In some embodiments, a visual model of the roof may be shown with a heatmap of irradiance values and/or graphical placement of solar panels. In other embodiments, the data may be analyzed and reported without visual presentation.