A method of delivering heavy payload using an autonomous UAV able to deliver supply by way of airdrop with more precision and at a lower cost. The UAV is equipped with two movable wing systems that rotate from a stowed position to a deployed position upon jettison of the UAV from a mothership. The UAV can be controlled remotely or it can operate autonomously and the movable wings can include ailerons to effectuate flight control of the UAV. The UAV can be reusable or can be an expendable UAV.

A remote health care apparatus is disclosed that incorporates a drone device and a health kit. The drone device includes one or more communication devices, and the drone device is capable of two-way auditory and visual communication. The health kit is capable of being transported by the drone device and can be detached from the drone device. In one embodiment, the health kit contains one or more medical devices selected from the group consisting of biometric measuring devices, specimen collection devices and lab testing tools.

A method of fire-fighting is provided based on unmanned aerial vehicles “UAV(s)” launched from transporter aircrafts to deliver water or fire-retardants or any other fire-fighting materials to a location selected by the fire-fighting personnel. A capability of putting-off high intensity forest fires is provided that stems from the precision and the quantity of material that can be delivered per unit surface per unit time. After releasing the fire-fighting material(s), the UAV reaches a safe altitude from which it flies on autopilot to intercept and then proceed on a pre-programmed route to land per pre-programmed instructions on an airfield from which fire-fighting transporter(s) operate, allowing a high efficiency along the line, from loading the transporter airplanes to maximizing the quantity of material that reach the target, to minimizing the remote-pilot time and up to the recovery system that minimizes the recovery cost and it maximizes UAVs' utilization by a quick turnaround.

Methods, systems and apparatus, including computer programs encoded on computer storage media for generation of autonomous unmanned aerial vehicle flight plans based on triggered sensor information. One of the methods includes accessing information correlated from sensors monitoring features of weather events, and determining an upcoming weather event, the determination comprising one or more areas expected to be affected by the weather event. A likelihood of damage associated with the weather event is determined to be greater than a threshold in the areas. The weather event is monitored while areas in which the likelihood is greater than the threshold are updated accordingly. Subsequent to the weather event, properties to be inspected by unmanned aerial vehicles are determined based on severity information associated with the weather event. Job information is generated, the job information being associated with inspecting the determined properties, the job information including jobs each assignable to operators for implementation.

The unmanned flying vaccine administration system comprises a drone, a vaccine delivery system, an interaction system. The drone is a vaccine injection flying robot that avoids the dangers of in-person vaccination. The vaccine delivery system is an electronic system that harnesses the power of technology to vaccinate people safely and efficiently. The interaction system is an electronic system armed with an Artificial Intelligence infrastructure. The present invention gathers energy by solar power, administers vaccines with a vaccine injection arm, and properly stores vaccines at the desired temperature with a storage container. The computing device controls the main modules that are designed for vaccine delivery and administration. The interaction system has a patient interface camera, a patient interface display, and a temperature sensor that monitor the state of the patient after receiving a vaccination to ensure the health and safety of the patient.

Disclosed is route planning using a worst-case risk analysis and, if needed, a best-case risk analysis of GNSS coverage. The worst-case risk analysis identifies cuboids or 2d regions through which a vehicle can be routed with assurance that adequate GNSS coverage will be available regardless of the time of day that the vehicle travels. The best-case risk analysis identifies cuboids or 2d regions through which there is adequate coverage at some times during the day. In case path finding using the worst-case risk analysis fails, a best-case risk analysis can be requested and used to find alternate potential path(s). Time dependent forecast data that covers regions along the alternate potential path(s) can be requested and used to route vehicles, including autonomous drones, from starting points to destinations. This includes generation, distribution and use of risk analysis data, implemented as methods, systems and articles of manufacture.

In some examples, an unmanned aerial vehicle (UAV) may determine, based on a three-dimensional (3D) model including a plurality of points corresponding to a scan target, a scan plan for scanning at least a portion of the scan target. For instance, the scan plan may include a plurality of poses for the UAV to assume to capture images of the scan target. The UAV may capture with one or more image sensors, one or more images of the scan target from one or more poses of the plurality of poses. Further, the UAV may determine an update to the 3D model based at least in part on the one or more images. Additionally, the UAV may update the scan plan based at least in part on the update to the 3D model.

Systems and methods are provided for managing air traffic and tolling for a plurality of unmanned aircraft system. Various embodiments include terrestrial and unmanned aircraft system based tracking modules for tracking a plurality of unmanned aircraft systems and reporting flight data to a tolling entity for collection from operators.

An autonomous vehicle system includes a body and a plurality of sensors coupled to the body and configured to generate a plurality of sensor measurements corresponding to the plurality of sensors. The system also includes a control unit configured to: receive inputs from a plurality of sources wherein the plurality sources comprise the plurality of sensors, the inputs comprise the plurality of sensor measurements; determine a confidence level of each input based on other inputs; prioritize, based on the confidence level associated with each input, the inputs; generate, based on the prioritization of the inputs and the confidence level, a combined input with a combined confidence level; and determine, based on the combined input and the combined confidence level, a mission task to be performed.

One or more potential drone and/or flying car (DFC) corridors are identified based on the topology of a road network. Trajectories traveled by vehicles are determined from a plurality of instances of probe data received from a plurality of vehicle apparatuses onboard the vehicles. A volume of traffic for a path through the road network and corresponding to a potential DFC corridor is determined based on the trajectories. A delay metric for the path through the road network and corresponding to the potential DFC corridor is determined based on the trajectories. A traffic metric is then determined for the path based on a combination of the volume of traffic, the delay metric and a measure of the topology of the road network. The one or more potential DFC corridors are ranked by their corresponding traffic metrics.