Abstract: An aircraft includes a fuselage that includes a nose portion, a cabin portion, an underwing portion, and an aft portion. The nose portion includes sensors that generate sensor data. The cabin portion is aft of the nose portion and includes a passenger cabin. The underwing portion is aft of the cabin portion and includes a wing attachment region and a battery bay. The aft portion is aft of the underwing portion. A wing assembly including motor mounts and control surfaces is attached to the wing attachment region such that the underwing portion of the fuselage is located under the wing assembly. A tail assembly is attached to the aft portion. The electric motors are attached to motor mounts such that the propellers are in a pusher configuration facing rearward. An autonomous control system controls the electric motors and control surfaces based on the sensor data.

Abstract: A non-transitory computer-readable medium includes computer-executable instructions that cause one or more processing units to acquire an airport map that includes a plurality of edge data structures. Each of the edge data structures includes a plurality of waypoints and membership data that indicates a location at an airport. The instructions further cause the one or more processing units to receive a starting location and a starting heading of an aircraft at the airport, determine a destination location and a destination heading for the aircraft at the airport, and generate a taxiing path plan for the aircraft based on the airport map, the starting location, the starting heading, the destination location, and the destination heading. The taxiing path plan includes a sequence of waypoints from the edge data structures. The instructions further cause the one or more processing units to send the taxiing path plan to the aircraft.

Abstract: A system includes one or more cameras configured to attach to an aircraft and capture a plurality of images. The plurality of images includes a first image including a runway and a subsequently captured second image including the runway. The system includes an aircraft computing system configured to identify common features in the first and second images, determine changes in locations of the common features between the first and second images, and determine a predicted landing location of the aircraft in the second image based on the changes in locations of the common features. The aircraft computing system is configured to abort landing on the runway based on the predicted landing location relative to the runway.

Abstract: A method includes determining initial load distribution data for a cargo area in an aircraft based on an estimated total weight of unloaded items. The cargo area includes a plurality of zones. The initial load distribution data includes a recommended weight for each of the zones and an estimated center of gravity (CG) for the aircraft as loaded with the items. The method includes displaying the initial load distribution data, acquiring a first weight value for a first set of items, and determining that the first set of items has been loaded into a first zone. The method includes determining updated load distribution data based on the first weight value and the location of the first zone in the aircraft. The updated load distribution data includes an updated recommended weight for each of the zones and an updated CG. The method includes displaying the updated load distribution data.

Abstract: A method includes selecting a landing waypoint on a runway and selecting a starting waypoint based on a location/heading of an aircraft relative to the runway. The method includes selecting additional waypoints between the starting waypoint and the landing waypoint. The starting and additional waypoints include latitude, longitude, and altitude variables. A sequence of waypoints from the starting waypoint to the landing waypoint via the additional waypoints indicates a desired location for the aircraft to traverse. The method includes generating location constraints for the starting and additional waypoints and generating an objective function for optimizing at least one of the variables. Additionally, the method includes generating a solution for the objective function subject to the location constraints. The solution includes latitude, longitude, and altitude values for the variables.