Abstract: The present disclosure is directed to systems and methods for trajectory and route planning including obstacle detection and avoidance for an aerial vehicle. For example, an aerial vehicle's flight control system may include a trajectory planner that may use short segments calculated using an iterative Dubins path to find a first path between a start point and an end point that does not avoid obstacles. Then the trajectory planner may use a rapidly exploring random tree algorithm that uses points along the first path as seed points to find a trajectory or route between the start point and end point that avoids known or detected obstacles.

Abstract: In an example, a method of generating flight paths for navigating an aircraft is provided. The method includes hovering the aircraft at a predetermined hover point. The predetermined hover point corresponds to a first takeoff waypoint of a first trajectory of the aircraft. The method includes scanning at least a portion of a first flight path of the first trajectory. The method includes determining that an obstacle obstructs the first flight path of the first trajectory. The first flight path begins at the first takeoff waypoint. The method includes determining a second takeoff waypoint. Determining the second takeoff waypoint includes assigning the first flight path to begin at the second takeoff waypoint. The method includes changing the first flight path of the first trajectory in accordance with the second takeoff waypoint, thereby forming a second flight path of a second trajectory.

Abstract: The present disclosure is directed to systems and methods for trajectory and route planning including obstacle detection and avoidance for an aerial vehicle. For example, an aerial vehicle's flight control system may include a trajectory planner that may use short segments calculated using an iterative Dubins path to find a first path between a start point and an end point that does not avoid obstacles. Then the trajectory planner may use a rapidly exploring random tree algorithm that uses points along the first path as seed points to find a trajectory or route between the start point and end point that avoids known or detected obstacles.

Abstract: The present disclosure is directed to systems and methods for trajectory and route planning including obstacle detection and avoidance for an aerial vehicle. For example, an aerial vehicle's flight control system may include a trajectory planner that may use short segments calculated using an iterative Dubins path to find a first path between a start point and an end point that does not avoid obstacles. Then the trajectory planner may use a rapidly exploring random tree algorithm that uses points along the first path as seed points to find a trajectory or route between the start point and end point that avoids known or detected obstacles.

Abstract: A system and method for tracking non-cooperative obstacles during operation of a vehicle is provided. The system may include a radar system, an optical sensor, and a processor. The radar system can be coupled to the vehicle and configured to scan a first airspace and generate radar information having a first resolution. The optical sensor can be coupled to the vehicle and configured to image a second airspace and generate optical information at a second resolution that is higher than the first resolution, where the second airspace is within said first airspace and includes a non-cooperative obstacle. The processor can be configured to identify the non-cooperative obstacle within the first airspace based at least in part on the radar information, and direct the optical sensor toward a location of the non-cooperative obstacle using the radar information.

Abstract: An autonomy system for use with a vehicle in an environment. The autonomy system comprising a processor operatively coupled with a memory device, a plurality of sensors operatively coupled with the processor; a vehicle controller, a situational awareness module, a task planning module, and a task execution module. The situational awareness module being configured to determine a state of the environment based at least in part on sensor data from at least one of the plurality of sensors. The task planning module being configured to identify, via the processor, a plurality of tasks to be performed by the vehicle and to generate a task assignment list from the plurality of tasks that is based at least in part on predetermined optimization criteria. The task execution module being configured to instruct the vehicle controller to execute the plurality of tasks in accordance with the task assignment list.

Abstract: A gimbal stabilizing system for an aircraft having an airframe is disclosed. The gimbal stabilizing system may comprise a gimbal apparatus having at least one gimbal actuator to adjust a position of the gimbal apparatus about an axis, wherein the gimbal apparatus is positioned on the airframe, an angular acceleration apparatus positioned on the airframe to generate an angular acceleration signal reflecting an angular acceleration of the airframe, and a gimbal controller operatively coupled to each of said angular acceleration apparatus and said gimbal apparatus. The gimbal controller may be configured to generate a gimbal control signal to compensate for the angular acceleration of the airframe based at least in part on a feedback control loop and a feedforward control loop, the feedforward control loop having the angular acceleration signal as an input thereto.