Abstract: An example method includes identifying a degraded visual environment corresponding to a phase of a route followed by the vehicle. The method includes determining, based on the phase of the route, a first segment of a trajectory of the vehicle along which to search for a location with an improved navigation environment. The method includes causing the vehicle to follow the first segment until: (i) identifying the improved navigation environment, or (ii) reaching an end of the first segment without identifying the improved navigation environment. The method includes determining a second segment of the trajectory based on whether the improved navigation environment has been identified. The method includes causing the vehicle to follow the second segment.

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 method, an apparatus, system, and computer program product for navigating an aircraft. Information indicative of a result of a scan of an environment around the aircraft is received by a computer system for landmarks. Bearings of the landmarks and locations of the landmarks are determined by the computer system. A current position of the aircraft is estimated by the computer system using the bearings of the landmarks and the locations of the landmarks. A set of actions to be performed is determined to guide the aircraft based on the current position of the aircraft is performed by the computer system.

Abstract: A method and corresponding apparatus and computer-readable storage medium are provided for causing one or more robots to execute a mission. The method includes identifying the mission including a nominal sequence of selected tasks that are executable to cause the one or more robots to execute maneuvers to achieve a mission objective. The method includes determining a task graph in which the mission is modeled. The task graph is expressed as a directed graph and includes selected task nodes representing the selected tasks that are connected by edges representing transitions between the selected tasks. The method also includes causing the one or more robots to execute the mission using the task graph and a task library of tasks including a selected task executable to cause the one or more robots to execute a maneuver.

Abstract: Techniques for traversing in an environment that includes at least one obstacle, by a mobile autonomous system, to a destination in the environment, are presented. The techniques can include generating, prior to the mobile autonomous system commencing activity in the environment, a graph including a plurality of vertices representing positions in the environment and a plurality of edges between vertices representing feasible transitions by the mobile autonomous vehicle in the environment; annotating the graph with at least one edge connecting a representation of a present position of the mobile autonomous system to a vertex of the graph; determining, based on the graph, a path from the present position of the mobile autonomous system in the environment to the destination; and traversing the environment to the destination, by the mobile autonomous system, based on the path.

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: Methods and apparatus are provided for allocating tasks to be performed by one or more autonomous vehicles to achieve a mission objective. Generally, a task allocation system identifies a final task associated with a given mission objective, identifies predecessor tasks necessary to complete the final task, generates one or more candidate tasks sequences to accomplish the mission objective, generates a task allocation tree based on the candidate task sequences, and searches the task allocation tree to find a task allocation plan that meets a predetermined selection criteria (e.g., lowest cost). Based on the task allocation plan, the task allocation system determines a task execution plan and generates control data for controlling one or more autonomous vehicles to complete the task execution plan.

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 method is provided for detecting and avoiding conflict along a current route of a robot. The method includes accessing a trajectory of the robot on the current route of the robot, and a predicted trajectory of a nearby moving object, and from the trajectory and predicted trajectory, detecting a conflict between the robot and the nearby moving object. Alternate routes for the robot are determined, each of which includes an alternative route segment offset from the current route, and a transition segment from the current route to the alternative route segment. Routes including the current and alternative routes are evaluated according to a cost metric, and a route from the routes is selected for use in at least one of guidance, navigation or control of the robot to avoid the conflict.

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: In an example, a method for controlling a vehicle in a degraded visual environment is provided. The method includes identifying a degraded visual environment corresponding to a phase of a route followed by the vehicle. The method includes determining, based on the phase of the route, a first segment of a trajectory of the vehicle along which to search for a location with an improved navigation environment. The method includes causing the vehicle to follow the first segment until: (i) identifying the improved navigation environment, or (ii) reaching an end of the first segment without identifying the improved navigation environment. The method includes determining a second segment of the trajectory based on whether the improved navigation environment has been identified. The method includes causing the vehicle to follow the second segment.

Abstract: A method of supporting robot(s) landing within a ground region is provided. The method includes accessing a map in which the ground region is tessellated into cells covering respective areas of the ground region. Each cell is classified as feasible to indicate a respective area is feasible for landing, or infeasible to indicate the respective area is infeasible for landing. The map is searched for clusters of adjoining cells that are classified as feasible, covering clusters of adjoining areas that define sub-regions within the ground region that are feasible for landing. The sub-regions are ranked according to a cost metric, and one of the sub-regions is selected according to the ranking. A geographic position of the selected sub-region is then output for use in at least one of guidance, navigation or control of the robot(s) to land at the selected sub-region within the ground region.

Abstract: A method is provided for causing one or more robots to execute a mission. The method includes determining a behavior tree in which the mission is modeled, and causing the one or more robots to execute the mission using the behavior tree and a leaf node library. The behavior tree is expressed as a directed tree of nodes including a switch node, a trigger node representing a selected task, and action nodes representing others of the tasks. The switch node is connected to the trigger node and the action nodes in a parent-child relationship in which the trigger node and the action nodes are children of the switch node. The trigger node is a first of the children that, when ticked by the switch node, returns an identifier of one of the action nodes to trigger the switch node to next tick the one of the action nodes.

Abstract: A method is provided for supporting a robot in response to a contingency event. The method includes detecting the contingency event during travel of the robot on a route to a destination. In response, the method includes determining a position of the robot, and accessing information about alternate destinations associated with the route. The method includes selecting an alternate destination from the alternate destinations based on a time to travel from the position of the robot to the alternate destination, and the information. And the method includes outputting an indication of the alternate destination for use in at least one of guidance, navigation or control of the robot to the alternate destination.

Abstract: A method and corresponding apparatus and computer-readable storage medium are provided for causing one or more robots to execute a mission. The method includes identifying the mission including a nominal sequence of selected tasks that are executable to cause the one or more robots to execute maneuvers to achieve a mission objective. The method includes determining a task graph in which the mission is modeled. The task graph is expressed as a directed graph and includes selected task nodes representing the selected tasks that are connected by edges representing transitions between the selected tasks. The method also includes causing the one or more robots to execute the mission using the task graph and a task library of tasks including a selected task executable to cause the one or more robots to execute a maneuver.

Abstract: A method, an apparatus, system, and computer program product for navigating an aircraft. Information indicative of a result of a scan of an environment around the aircraft is received by a computer system for landmarks. Bearings of the landmarks and locations of the landmarks are determined by the computer system. A current position of the aircraft is estimated by the computer system using the bearings of the landmarks and the locations of the landmarks. A set of actions to be performed is determined to guide the aircraft based on the current position of the aircraft is performed by the computer system.

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: 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: 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.

Abstract: Methods and apparatus are provided for allocating tasks to be performed by one or more autonomous vehicles to achieve a mission objective. Generally, a task allocation system identifies a final task associated with a given mission objective, identifies predecessor tasks necessary to complete the final task, generates one or more candidate tasks sequences to accomplish the mission objective, generates a task allocation tree based on the candidate task sequences, and searches the task allocation tree to find a task allocation plan that meets a predetermined selection criteria (e.g., lowest cost). Based on the task allocation plan, the task allocation system determines a task execution plan and generates control data for controlling one or more autonomous vehicles to complete the task execution plan.