An automated aircraft recovery system is disclosed. In various embodiments, the system includes an interface configured to receive sensor data; and a control mechanism configured to: perform automatically a recovery action that is determined based at least in part on the sensor data. In various embodiments, the control mechanism may determine an expected state of an aircraft, determine whether a state of the aircraft matches the expected state, and in the event the state of the aircraft does not match the expected state, perform the recovery action.

Various embodiments of the present technology generally relate to unmanned aerial vehicle (UAV) scale rendered analysis, orthomosaic, and 3D mapping and landing platform systems. More specifically, some embodiments relate to systems, methods, and means for the collection and processing of images captured during a UAV flight sequence. In some embodiments, the UAV landing platform retrieves flight information and initial map information over a unidirectional virtual private network from a multitenant cloud-based scheduling application. The UAV landing platform sends the initial map information to a UAV over a WiFi, Bluetooth, or radio frequency network and initiates a drone flight sequence once the drone flight sequence has been approved by a local user. The UAV landing platform receives property image data from a UAV after a UAV flight sequence has ended and transmits the received property image data back to the cloud application.

Using power line rights of way for UAV routing provides a direct, uninterrupted, aerially clear path to the vast majority of lots and buildings from nearby substations and generating stations. Segmenting or separating the UAV traffic by airframe glide ratio improves safety for people on the ground and utilization of the limited airspace. Further segmenting UAV traffic by airframe speed and size allows greater traffic throughput.

A maintenance system is provided for an environment conditioning element of an environmental control system (ECS) of a vehicle. The system includes a data collection module configured to receive geographical areas of travel for the vehicle over respective periods of time. The data collection module is configured to determine a pollution value and a time value for each of the geographic areas of travel. The system further includes a pollution count module coupled to the data collection module and receiving the pollution values and the time values. The pollution count module is configured to determine a pollution count for the environment conditioning element based on the pollution values and the time values. The system further includes a reporting module coupled to the pollution count module and receiving the pollution count. The reporting module is configured to generate a report for a user that includes the pollution count.

According to the present invention there is provided a drone (1) comprising one or more propellers (2) and one or more actuators (3) for actuating said one or more propellers (2) to generating a thrust force which enables the drone (1) to fly; a controller (4) which is configured such that it can control the flight of the drone (1), wherein the controller (4) comprises a memory (6) having stored therein a plurality of predefined sets of positions which define a virtual rail which can be used to guide the flight of the drone (1) so that the drone can avoid collision with an subject; and wherein the controller further comprises a mathematical model (7) of the drone; wherein the controller (4) is configured to control the flight of the drone by performing at least the following steps, (a) approximating lag error based on the position of the drone (1) measured by a sensor (5) and the virtual rail, wherein the lag error is the distance between a point along the virtual rail which is closest to the drone (1) and an estimate of said point along the virtual rail which is closest to the drone (1); (b) approximating a contour error based on the position of the drone (1) as measured by a sensor (5) and the virtual rail, wherein the contour error is the distance between a point along the virtual rail which is closest to the drone (1) and the position of the drone (1); (c) defining a cost function which comprises at least said approximation of the lag error and said approximation of the contour error; (d) minimizing the defined cost function, while also respecting at least limitations of the drone which are defined in said mathematical model, to determine a plurality of control inputs over a predefined time period into the future, and (e) applying the first control input only to the one or more actuators (3). There is further provided a corresponding method for controlling the flight of a drone.

In some implementations, a UAV flight system can dynamically adjust UAV flight operations based on radio frequency (RF) signal data. For example, the flight system can determine an initial flight plan for inspecting a RF transmitter and configure a UAV to perform an aerial inspection of the RF transmitter. Once airborne, the UAV can collect RF signal data and the flight system can automatically adjust the flight plan to avoid RF signal interference and/or damage to the UAV based on the collected RF signal data. In some implementations, the UAV can collect RF signal data and generate a three-dimensional received signal strength map that describes the received signal strength at various locations within a volumetric area around the RF transmitter. In some implementations, the UAV can collect RF signal data and determine whether a RF signal transmitter is properly aligned.

Aspects relate to a system for flight plan generation of an electric vertical takeoff and landing (eVTOL) aircraft. An exemplary system for flight plan generation includes a flight controller mounted on an eVTOL aircraft. The flight controller may be configured to receive a plurality of flight plan data and generate a flight plan for the aircraft as a function of the plurality of flight plan data.

A method for monitoring an unmanned aerial vehicle (UAV) includes a processor obtaining a datagram based on monitoring data for a UAV-detector communication between the UAV and one or more detectors. The monitoring data indicates at least one of a location of the UAV or a location of a control station in communication with the UAV. The method further includes determining a risk level by retrieving pre-stored risk information associated with the UAV based on the datagram.

Boundary information associated with a three-dimensional (3D) flying space is obtained, including a boundary of the 3D flying space. Location information associated with an aircraft is obtained, including a location of the aircraft. Information is presented based at least in part on the boundary information associated with the 3D flying space and the location information associated with the aircraft, including by presenting, in a display, the boundary of the 3D flying space and an avatar representing the aircraft at the location of the aircraft.

Apparatuses, methods, and systems are disclosed for managing a C2 communication mode of operation. One apparatus includes a transceiver supporting a network interface that receives an application request to manage an operation mode of C2 communication for a first UAS. Here, the first UAS comprises a first UAV and a first UAV-C. The network interface receives a first report from an application of the first UAS, where the first application is located in one of the first UAV and the first UAV-C. The apparatus includes a processor that determines to switch the operation mode of C2 communication for the first UAS based on the received first report and transmits, via the network interface, a C2 communication switching instruction to the first UAS.