Abstract: In one example embodiment, a computer-implemented method includes receiving data representing a motion plan of the autonomous vehicle via a plurality of control lanes configured to implement the motion plan to control a motion of the autonomous vehicle, the plurality of control lanes including at least a first control lane and a second control lane, and controlling the first control lane to implement the motion plan. The method includes detecting one or more faults associated with implementation of the motion plan by the first control lane or the second control lane, or in generation of the motion plan, and in response to one or more faults, controlling the first control lane or the second control lane to adjust the motion of the autonomous vehicle based at least in part on one or more fault reaction parameters associated with the one or more faults.

Abstract: The present disclosure is directed to controlling state transitions in an autonomous vehicle. In particular, a computing system can initiate the autonomous vehicle into a no-authorization state upon startup. The computing system can receive an authorization request. The computing system determines whether the authorization request includes a request to enter the first or second mode of operations, wherein the first mode of operations is associated with the autonomous vehicle being operated without a human operator and the second mode of operations is associated with the autonomous vehicle being operable by a human operator. The computing system can transition the autonomous vehicle from the no-authorization state into a standby state in response to determining the authorization request includes a request to enter the first mode of operations or into a manual-controlled state in response to determining the authorization request is a request to enter the second mode of operations.

Abstract: The present disclosure is directed to controlling state transitions in an autonomous vehicle. In particular, a computing system can initiate the autonomous vehicle into a no-authorization state upon startup. The computing system can receive an authorization request. The computing system determines whether the authorization request includes a request to enter the first or second mode of operations, wherein the first mode of operations is associated with the autonomous vehicle being operated without a human operator and the second mode of operations is associated with the autonomous vehicle being operable by a human operator. The computing system can transition the autonomous vehicle from the no-authorization state into a standby state in response to determining the authorization request includes a request to enter the first mode of operations or into a manual-controlled state in response to determining the authorization request is a request to enter the second mode of operations.

Abstract: The present disclosure is directed to controlling state transitions in an autonomous vehicle. In particular, a computing system can initiate the autonomous vehicle into a no-authorization state upon startup. The computing system can receive an authorization request. The computing system determines whether the authorization request includes a request to enter the first or second mode of operations, wherein the first mode of operations is associated with the autonomous vehicle being operated without a human operator and the second mode of operations is associated with the autonomous vehicle being operable by a human operator. The computing system can transition the autonomous vehicle from the no-authorization state into a standby state in response to determining the authorization request includes a request to enter the first mode of operations or into a manual-controlled state in response to determining the authorization request is a request to enter the second mode of operations.

Abstract: In one example embodiment, a computer-implemented method includes receiving data representing a motion plan of the autonomous vehicle via a plurality of control lanes configured to implement the motion plan to control a motion of the autonomous vehicle, the plurality of control lanes including at least a first control lane and a second control lane, and controlling the first control lane to implement the motion plan. The method includes detecting one or more faults associated with implementation of the motion plan by the first control lane or the second control lane, or in generation of the motion plan, and in response to one or more faults, controlling the first control lane or the second control lane to adjust the motion of the autonomous vehicle based at least in part on one or more fault reaction parameters associated with the one or more faults.

Abstract: In one example embodiment, a computer-implemented method includes receiving data representing a motion plan of the autonomous vehicle via a plurality of control lanes configured to implement the motion plan to control a motion of the autonomous vehicle, the plurality of control lanes including at least a first control lane and a second control lane, and controlling the first control lane to implement the motion plan. The method includes detecting one or more faults associated with implementation of the motion plan by the first control lane or the second control lane, or in generation of the motion plan, and in response to one or more faults, controlling the first control lane or the second control lane to adjust the motion of the autonomous vehicle based at least in part on one or more fault reaction parameters associated with the one or more faults.

Abstract: Systems and methods are directed to motion planning for an autonomous vehicle. In one example, a computer-implemented method for road surface dependent motion planning includes obtaining, by a computing system comprising one or more computing devices, surface friction data. The method further includes determining, by the computing system, one or more constraints for motion planning based at least in part on the surface friction data. The method further includes generating, by the computing system, a motion plan for an autonomous vehicle based at least in part on the one or more constraints.

Abstract: The present disclosure is directed to controlling state transitions in an autonomous vehicle. In particular, a computing system can initiate the autonomous vehicle into a no-authorization state upon startup. The computing system can receive an authorization request. The computing system determines whether the authorization request includes a request to enter the first or second mode of operations, wherein the first mode of operations is associated with the autonomous vehicle being operated without a human operator and the second mode of operations is associated with the autonomous vehicle being operable by a human operator. The computing system can transition the autonomous vehicle from the no-authorization state into a standby state in response to determining the authorization request includes a request to enter the first mode of operations or into a manual-controlled state in response to determining the authorization request is a request to enter the second mode of operations.

Abstract: In one example embodiment, a computer-implemented method includes receiving data representing a motion plan of the autonomous vehicle via a plurality of control lanes configured to implement the motion plan to control a motion of the autonomous vehicle, the plurality of control lanes including at least a first control lane and a second control lane, and controlling the first control lane to implement the motion plan. The method includes detecting one or more faults associated with implementation of the motion plan by the first control lane or the second control lane, or in generation of the motion plan, and in response to one or more faults, controlling the first control lane or the second control lane to adjust the motion of the autonomous vehicle based at least in part on one or more fault reaction parameters associated with the one or more faults.

Abstract: In one example embodiment, a computer-implemented method includes receiving data representing a motion plan of the autonomous vehicle via a plurality of control lanes configured to implement the motion plan to control a motion of the autonomous vehicle, the plurality of control lanes including at least a first control lane and a second control lane, and controlling the first control lane to implement the motion plan. The method includes detecting one or more faults associated with implementation of the motion plan by the first control lane or the second control lane, or in generation of the motion plan, and in response to one or more faults, controlling the first control lane or the second control lane to adjust the motion of the autonomous vehicle based at least in part on one or more fault reaction parameters associated with the one or more faults.

Abstract: The present disclosure is directed to a vehicle dynamics monitor for an autonomous vehicle. In particular, the systems and methods of the present disclosure can determine, based on data received from one or more sensors of an autonomous vehicle, that an anomaly exists in an interface between at least one tire of the autonomous vehicle and a road surface. Responsive to determining that the anomaly exists: a motion plan for the autonomous vehicle that takes into account the anomaly can be determined; and one or more controls of the autonomous vehicle can be interfaced with to implement the motion plan.

Abstract: A system and method of controlling flight of an aircraft is disclosed. The system includes a first inceptor that provides a direct mode command for controlling a control axis of the aircraft according to a direct mode and a second inceptor that provides a stable mode command for controlling the control axis of the aircraft according to a stable mode. A processor receives the direct mode command and the stable mode command, forms a combined command for controlling the control axis of the aircraft based on a combination of the direct mode command and the stable mode command, and controls the control axis of the aircraft according to the combined command.

Abstract: The present disclosure provides a vehicle interface for an autonomous vehicle. In particular, the systems and methods of the present disclosure can, responsive to receiving, from an autonomy computing system of an autonomous vehicle, a time-based trajectory for the autonomous vehicle, verify that execution of the time-based trajectory is within parameters of the autonomous vehicle. Responsive to verifying that execution of the time-based trajectory is within the parameters of the autonomous vehicle, the time-based trajectory can be converted into a spatial path for the autonomous vehicle, and one or more controls of the autonomous vehicle can be interfaced with such that the autonomous vehicle tracks the spatial path.

Abstract: The present disclosure provides systems and methods that employ tolerance values defining a level of vehicle control precision for motion control of an autonomous vehicle. More particularly, a vehicle controller can obtain a trajectory that describes a proposed motion path for the autonomous vehicle. A constraint set of one or more tolerance values (e.g., a longitudinal tolerance value and/or lateral tolerance value) defining a level of vehicle control precision can be determined or otherwise obtained. Motion of the autonomous vehicle can be controlled to follow the trajectory within the one or more tolerance values (e.g., longitudinal tolerance value(s) and/or a lateral tolerance value(s)) identified by the constraint set. By creating a motion control framework for autonomous vehicles that includes an adjustable constraint set of tolerance values, autonomous vehicles can more effectively implement different precision requirements for different driving situations.

Abstract: The present disclosure provides systems and methods that employ tolerance values defining a level of vehicle control precision for motion control of an autonomous vehicle. More particularly, a vehicle controller can obtain a trajectory that describes a proposed motion path for the autonomous vehicle. A constraint set of one or more tolerance values (e.g., a longitudinal tolerance value and/or lateral tolerance value) defining a level of vehicle control precision can be determined or otherwise obtained. Motion of the autonomous vehicle can be controlled to follow the trajectory within the one or more tolerance values (e.g., longitudinal tolerance value(s) and/or a lateral tolerance value(s)) identified by the constraint set. By creating a motion control framework for autonomous vehicles that includes an adjustable constraint set of tolerance values, autonomous vehicles can more effectively implement different precision requirements for different driving situations.

Abstract: One aspect is a flight control system for a coaxial rotary wing aircraft including a main rotor system and an active elevator. The flight control system includes a flight control computer with processing circuitry that executes control logic. The control logic includes a gust detector that produces a gust error indicative of a wind gust encountered by the coaxial rotary wing aircraft. The control logic also includes a gust alleviation control that reduces lift on the main rotor system with collective, based on the gust error, and mixes a collective command to a main rotor cyclic and a differential cyclic to reduce an aircraft pitch response and a lift-offset change. The gust alleviation control also reduces a main rotor pitching moment with the main rotor cyclic, based on the gust error, and mixes a main rotor cyclic command to the active elevator to reduce the aircraft pitch response.

Abstract: The present disclosure provides systems and methods that employ tolerance values defining a level of vehicle control precision for motion control of an autonomous vehicle. More particularly, a vehicle controller can obtain a trajectory that describes a proposed motion path for the autonomous vehicle. A constraint set of one or more tolerance values (e.g., a longitudinal tolerance value and/or lateral tolerance value) defining a level of vehicle control precision can be determined or otherwise obtained. Motion of the autonomous vehicle can be controlled to follow the trajectory within the one or more tolerance values (e.g., longitudinal tolerance value(s) and/or a lateral tolerance value(s)) identified by the constraint set. By creating a motion control framework for autonomous vehicles that includes an adjustable constraint set of tolerance values, autonomous vehicles can more effectively implement different precision requirements for different driving situations.

Abstract: A method for controlling a propeller of an aircraft, comprises receiving, with a processor, one or more signals indicative of commanded collective pitch of the propeller; receiving, with the processor, one or more sensed signals indicative of propeller axial speed, propeller rotational speed, and air density; estimating, with the processor, a propeller torque and propeller thrust from one or more of the propeller axial speed, the propeller rotational speed, and the air density; determining, with the processor, information indicative of an error value between a desired torque and a measured torque in the propeller; determining, with the processor, information indicative of a corrected pitch command in response to the determining of the error value; combining, with the processor, the corrected pitch command with the propeller rotational speed into an adjustment solution; providing, with the processor, the propeller with the adjustment solution.

Abstract: A method for controlling a differential rotor roll moment for a coaxial helicopter with rigid rotors, the method including receiving, with a processor, a signal indicative of a displacement command from a controller; receiving, with the processor via a sensor, one or more signals indicative of a longitudinal velocity, an angular velocity of one or more rotors and an air density ratio for the helicopter; determining, with the processor, a ganged collective mixing command in response to the receiving of the displacement command; determining, with the processor, a rotor advance ratio as a function of the longitudinal velocity and the angular velocity; and determining, with the processor, a corrective differential lateral cyclic command for the rigid rotors that controls the differential rotor roll moment to a desired value.

Abstract: Systems and methods are directed to motion planning for an autonomous vehicle. In one example, a computer-implemented method for road surface dependent motion planning includes obtaining, by a computing system comprising one or more computing devices, surface friction data. The method further includes determining, by the computing system, one or more constraints for motion planning based at least in part on the surface friction data. The method further includes generating, by the computing system, a motion plan for an autonomous vehicle based at least in part on the one or more constraints.