An aircraft is disclosed having a structure at least part of which is capable of generating aerodynamic lift. A body having a mass is movably mounted to a portion of the structure by an active support. The active support includes an actuator to move the body relative to the portion of the structure, and a controller for controlling movement of the actuator in response to a dynamic input. The active support provides a range of movement for the body in at least one degree of freedom. The actuator moves the body across the entire range of movement in that one degree of freedom in a time period of less than 3 seconds. The actuator moves the body sufficiently rapidly to generate an inertial force that is equal to or greater than any aerodynamic force generated by the body during that movement of the body. The active support may be used to reduce loads on the aircraft structure.

The present disclosure provides systems and methods that control the motion of an autonomous vehicle by rewarding or otherwise encouraging progress toward a goal, rather than simply rewarding distance traveled. In particular, the systems and methods of the present disclosure can project a candidate motion plan that describes a proposed motion path for the autonomous vehicle onto a nominal pathway to determine a projected distance associated with the candidate motion plan. The systems and methods of the present disclosure can use the projected distance to evaluate a reward function that provides a reward that is positively correlated to the magnitude of the projected distance. The motion of the vehicle can be controlled based on the reward value provided by the reward function. For example, the candidate motion plan can be selected for implementation or revised based at least in part on the determined reward value.

A system for path control for a mobile unmanned vehicle in an environment is provided. The system includes: a sensor connected to the mobile unmanned vehicle; the mobile unmanned vehicle configured to initiate a first fail-safe routine responsive to detection of an object in a first sensor region adjacent to the sensor;

and a processor connected to the mobile unmanned vehicle. The processor is configured to: generate a current path based on a map of the environment; based on the current path, issue velocity commands to cause the mobile unmanned vehicle to execute the current path; responsive to detection of an obstacle in a second sensor region, initiate a second fail-safe routine in the mobile unmanned vehicle to avoid entry of the obstacle into the first sensor region and initiation of the first fail-safe routine.

An example method includes receiving pitch angle sensor information indicative of a pitch angle of a vehicle, wherein the vehicle comprises a main landing gear having a strut and a pitch control surface configured to control the pitch angle of the vehicle; determining a trailing-edge-up limit for upward movement of the pitch control surface to control a de-rotation rate of the vehicle as the vehicle lands; receiving load sensor information indicative of a load on the strut of the main landing gear of the vehicle; based on the pitch angle of the vehicle being below a pitch angle threshold, determining an updated trailing-edge-up limit based on the load on the strut; and controlling the pitch control surface based on the updated trailing-edge-up limit.

A yaw control system for a helicopter having an airframe that includes a tailboom includes one or more tail rotors rotatably coupled to the tailboom and a flight control computer implementing an airframe protection module. The airframe protection module includes an airframe protection monitoring module configured to monitor one or more flight parameters of the helicopter and an airframe protection command module configured to modify one or more operating parameters of the one or more tail rotors based on the one or more flight parameters of the helicopter, thereby protecting the airframe of the helicopter.

In some examples, systems and methods are described for output biasing maneuvers recommendations provided by at least one machine learning maneuver-recommendation (MLM) engine executing on an aerial vehicle. In some examples, output biasing data can be received that includes at least one risk tuning parameter that can influence which of the maneuver recommendations are selected by a maneuver decision engine executing on the aerial vehicle based on a maneuver confidence threshold for implementation by the aerial vehicle. The maneuver confidence threshold can be updated based on the at least one risk tuning parameter to provide an updated maneuver confidence threshold for the output biasing of the maneuvers recommendation provided by the at least one MLM engine. Vehicle command data for implementing a given maneuver recommendation can be outputted based on an evaluation of the updated maneuver confidence threshold.

A method executed by an information processing apparatus configured to communicate with a vehicle that transports one or more packages stored in a storage space includes acquiring a transportation condition for a package to be stored in the storage space and controlling an environment of the storage space during transportation of the package, or limiting acceleration magnitude of the vehicle during transportation of the package, based on the acquired transportation condition.

Methods, devices and systems enable controlling an autonomous vehicle by identifying vehicles that are within a threshold distance of the autonomous vehicle, determining an autonomous capability metric of each of the identified vehicles, and adjusting a driving parameter of the autonomous vehicle based on the determined autonomous capability metric of each of the identified vehicles. Embodiments may further include determining, based on the determined ACMs, whether one or more identified vehicles would provide an operational advantage to the autonomous vehicle in a cooperative driving engagement, and initiating a cooperative driving engagement with the one or more identified vehicles in response to determining that the one or more identified vehicles would provide an operational advantage to the autonomous vehicle in a cooperative driving engagement.

A yaw control system for a helicopter having an airframe that includes a tailboom includes one or more tail rotors rotatably coupled to the tailboom and a flight control computer implementing an airframe protection module. The airframe protection module includes an airframe protection monitoring module configured to monitor one or more flight parameters of the helicopter and an airframe protection command module configured to modify one or more operating parameters of the one or more tail rotors based on the one or more flight parameters of the helicopter, thereby protecting the airframe of the helicopter.

An autonomous mobile device (AMD) may move around while performing tasks. If the AMD is lifted, the AMD responds to ensure safety of the user, modify ongoing operation, and so forth. For example, the AMD may stop the wheels, retract a mast, or suspend navigation tasks. To accurately determine whether or not the AMD has been lifted, the AMD uses one or more sensors to determine a vertical lift distance and rotation of the AMD with respect to one or more axes. For example, while the AMD is stationary, the sensors used may include an accelerometer or a gyrometer. While the AMD is moving, data from time-of-flight sensors or one or more cameras may also be used. If the AMD is stationary low-level sensor thresholds are used. If the AMD is moving steadily, medium-level sensor thresholds are used. If the AMD is suddenly decelerating, high-level sensor thresholds are used.