A method is provided for supporting a robot in response to a contingency event. The method includes detecting the contingency event during travel of the robot on a route to a destination. In response, the method includes determining a position of the robot, and accessing information about alternate destinations associated with the route. The method includes selecting an alternate destination from the alternate destinations based on a time to travel from the position of the robot to the alternate destination, and the information. And the method includes outputting an indication of the alternate destination for use in at least one of guidance, navigation or control of the robot to the alternate destination.

A computer-implemented method of enabling optimisation of derate for a propulsion system of a vehicle, the method comprising: determining a derate for the propulsion system of the vehicle using: an algorithm; a vehicle model defining path constraints for the vehicle through space; a propulsion system model defining parameters of the propulsion system; an objective function defining one or more objectives; and controlling output of the determined derate.

According to the present invention, a drone control system includes: a flying drone; a cloud server configured to transmit and receive information to and from the drone by wireless communication; and a ground control system configured to establish a flight plan of the drone by connecting the drone and the cloud server by the wireless communication.

A system includes a first sensor configured to detect an object within an air space, and output an observation signal indicative of the object within the air space. A second sensor is configured to track the object within the air space, and output a tracking signal of the object within the air space. A tracking control unit is in communication with the first sensor and the second sensor. The tracking control unit is configured to receive the observation signal from the first sensor. In response to receiving the observation signal from the first sensor, the tracking control unit is configured to operate the second sensor to track the object within the air space relative to an aircraft within the air space. The tracking control unit is also configured to determine a priority of actions to take in relation to the object based, at least in part, on the tracking signal received from the second sensor.

Described are various embodiments of a flight management method and system using same. In one embodiment, a digital flight management system comprises: a digital processing environment comprising instructions to access: flight request data related to a flight plan; aircraft parameter data; a flight risk data source; and geographical data. The instructions are executable to: calculate a predicted flight path; digitally compare the predicted flight path with flight risk data from the flight risk data source to assess a flight risk associated with the predicted flight path; and display via a user interface the predicted fight path in accordance with the flight risk.

Ray-tracing for terrain mapping is provided. A system of an aerial vehicle can identify points generated from data captured by a sensor of the aerial vehicle. The points can each indicate a respective altitude value of a portion of terrain. The system can determine, based on the altitude values of the points, a threshold altitude of the terrain, and can identify a boundary defined in part based on the threshold altitude of the terrain. The system can generate a terrain map for the terrain based on applying a ray-tracing process to the points. The ray-tracing process can be performed within the boundary, using the points as respective sources and the aerial vehicle as a destination. The system can present a graphical representation of the terrain map in a graphical user interface of the aerial vehicle.

In some embodiments, a non-transitory computer-readable medium having logic stored thereon is provided. The logic, in response to execution by one or more processors of an unmanned aerial vehicle (UAV), causes the UAV to perform actions comprising receiving at least one ADS-B message from an intruder aircraft; generating a intruder location prediction based on the at least one ADS-B message; comparing the intruder location prediction to an ownship location prediction to detect conflicts; and in response to detecting a conflict between the intruder location prediction and the ownship location prediction, determining a safe landing location along a planned route for the UAV and descending to land at the safe landing location.

A response system may be provided. The response system may include an autonomous drone. The autonomous drone may include a processor, a memory in communication with the processor, and a sensor. The processor may be programmed to build a virtual map of a coverage area, store the virtual map in the memory, receive a deployment signal, deploy the drone in response to the deployment signal, control movement of the drone within the coverage area using the virtual map, collect sensor data of the coverage area using the sensor, and/or analyze the sensor data to generate an inventory list of the coverage area, the inventory list including a personal article within the coverage area.

An airborne coordinate measuring machine (CMM) includes a noncontact 3D scanner constructed to obtain measurement data of an object under scrutiny and a drone aircraft mechanically coupled to the 3D scanner and constructed to traverse a flight path that is specific to the object under scrutiny.

Techniques for generating flight operation recommendation for an aircraft are described. In operation, a flight safety hazard is detected on a flight path corresponding to a current flight operation of an aircraft. In response, the current flight operation is modified to generate a modified flight operation. A plurality of avionics parameters is then obtained from avionics systems available onboard the aircraft. A variation in the flight safety hazard is then detected based on at least one avionics parameter from the plurality of avionics parameters. Upon ascertaining the variation to be detrimental to the modified flight operation of the aircraft, the plurality of avionics parameters is analysed using a flight operation recommendation model to generate a plurality of flight operation recommendations. A flight operation recommendation from the plurality of flight operation recommendations is then applied to the modified flight operation.