A system for voice recognition in autonomous flight of an electric aircraft that includes a computing device communicatively connected to the electric aircraft configured to receive at least a voice datum from a remote device, wherein the voice datum is configured to include at least an expression datum, generate, using a first machine-learning process, a transcription datum as a function of the at least a voice datum, extract at least a query as a function of the transcription datum, generate, using a second machine-learning process, a communication output as a function of the at least a query, and adjust a flight plan as a function of the communication output.

A system may include a processor configured to: (a) obtain parameters; (b) based on the parameters, update flight-state data associated with an aircraft; (c) obtain a trained machine learning (ML) model; (d) based at least on the updated flight-state data and the trained ML model, infer a direction from a current cell for a reroute; (e) based on the inferred direction and the updated flight-state data, set the current cell and identify neighboring cells; (f) calculate an optimal next cell by using a shortest path finding (SPF) algorithm to select the optimal next cell from the neighboring cells; (g) iteratively repeat steps (d) through (f) such that the current cell is set as the optimal next cell until a goal state is reached; (h) construct a re-route using optimal cells iteratively calculated in step (f); and (i) output the re-route.

A system may include a processor configured to: (a) obtain parameters; (b) based on the parameters, update flight-state data associated with an aircraft; (c) obtain a trained machine learning (ML) model; (d) based at least on the updated flight-state data and the trained ML model, infer a direction from a current cell for a reroute; (e) based on the inferred direction and the updated flight-state data, set the current cell and identify neighboring cells; (f) calculate an optimal next cell by using a shortest path finding (SPF) algorithm to select the optimal next cell from the neighboring cells; (g) iteratively repeat steps (d) through (f) such that the current cell is set as the optimal next cell until a goal state is reached; (h) construct a re-route using optimal cells iteratively calculated in step (f); and (i) output the re-route.

A method for an Unmanned Aerial Vehicle, UAV, for generating a temporary flight-plan for a region is provided. The method includes determining whether any air traffic control station is emitting a flight-plan for the region. If it is determined that no air traffic control station is emitting a flight-plan for the region, the method further includes determining a score for the UAV based on properties of the UAV and receiving scores from other UAVs within the region. Additionally, the method includes determining whether the UAV is a master UAV or a slave UAV for generating the temporary flight-plan based on the calculated score for the UAV and the received scores from the other UAVs. If it is determined that the UAV is a master UAV, the method includes performing a first task in generating the temporary flight-plan.

In an embodiment, a method includes receiving, at a compute device of a unmanned aerial vehicle (UAV) that is included within a plurality of unmanned aerial vehicles (UAVs) associated with a site, a representation of a flight plan (1) between a start location at a start time and an end location at an end time, and (2) for the UAV. The method further includes causing the UAV to autonomously fly based on the flight plan for the UAV in response to the UAV receiving the signal with the representation of the flight plan for the UAV.

A unmanned aerial vehicle (UAV) includes a flight management system (FMS) that is in communication with other FMSs on other UAVs. The FMS includes a route manager that retrieves data from an avoidance source and determines an avoidance importance score for a route adjustment request based on the data. The route manager also determines a route complexity score for the request. A rerouter within the FMS compares the route complexity score for the request to a complexity threshold for the UAV. Based on the comparison, the rerouter forwards to the request to a rerouting client on the UAV or to the other UAVs via inter-application communication. If the request is sent to the other UAVs, then the processed new route is provided by the originating UAV.

Techniques for generating flight path recommendations for an aircraft. In operation, an alternative flight path indication for generating a flight path recommendation may be received. Upon receiving the alternative flight path indication, avionics data for the aircraft, contextual flight operations data for the aircraft, and flight operation modelled data associated with a plurality of flight paths corresponding to the current flight operation of the aircraft are obtained. The avionics data, the contextual flight operation data, and the flight operation modelled data are then combined to generate a fused aviation data set. The fused aviation data set is then analyzed using a flight path recommendation model to generate a flight path recommendation. The flight path recommendation is then applied to the current flight operation of the aircraft.

A method for protection of inflight aircraft during approaching-to-landing and takeoff/climbout phases of flight against handheld laser attacks includes two different drone types: a skeining drone and a swarming drone. One or more skeining drones are deployed close to the aircraft and/or one or more swarming drones are deployed further from the aircraft and closer to the beam source. Prior to the aircraft's traversal of a determinable approach point, a plurality of swarming drones are pre-deployed in loitering mode or else launched, and are subsequently directed toward the reckoned source of a trained beam while skeining drones are pre-deployed in a patrol mode or else launched, and fly closer to the aircraft. The skein classically shields the cockpit by flying a controlled interference pattern roughly parallel to the aircraft flightpath while the swarm saturates one or more determined and dynamically redetermined regions athwart the beam source location.

A method for protection of inflight aircraft during approaching-to-landing and takeoff/climbout phases of flight against handheld laser attacks includes two different drone types: a skeining drone and a swarming drone. One or more skeining drones are deployed close to the aircraft and/or one or more swarming drones are deployed further from the aircraft and closer to the beam source. Prior to the aircraft's traversal of a determinable approach point, a plurality of swarming drones are pre-deployed in loitering mode or else launched, and are subsequently directed toward the reckoned source of a trained beam while skeining drones are pre-deployed in a patrol mode or else launched, and fly closer to the aircraft. The skein classically shields the cockpit by flying a controlled interference pattern roughly parallel to the aircraft flightpath while the swarm saturates one or more determined and dynamically redetermined regions athwart the beam source location.

Embodiments of the present disclosure relate to a system and device for generating and providing a list of possible options for re-routing or changing the flights based on user-specific query. The system identifies if there are one or more empty legs in one more flight between a departure location and an arrival location provided by a user at the user device. The system also identifies if there are one or more alternative flights available to re-route to reach the departure location and pick passengers. Based on identification, the system accepts or rejects the user query and accordingly generates a list of possible options to which said user may with a selection of a best-suited flight.