Aurora Flight Sciences Corporation

The Boeing Company

Aurora Flight Sciences, a Subsidiary of the Boeing Company

Jeffery Saunders

William Robert Bosworth

Jason Christopher Ryan

Craig John Bonnoit

Douglas Famularo

James Donald Paduano

John Brooke Wissler

Jae-Woo Choi

Riley C Griffin

Margaret Reagan

Joseph Chung

Alfredo Giuliano

John F Langford

Jason T Jewell

Nathan Otenti

Benjamin C Stringer

Carter Durno

William R Bosworth

Methods and apparatus for pointing logic in aircraft are disclosed. A disclosed example apparatus to aim an aiming device carried by an aircraft includes at least one memory, machine readable instructions, and processor circuitry. The processor is to at least one of instantiate or execute the machine readable instructions to determine a position of a target, determine an orientation of the aircraft, determine aiming points based on the orientation and a movement range of the aiming device, and determine a movement of at least one of the aircraft or the aiming device based on the aiming points and the position to orient the aiming device toward the target.

Methods and apparatus for pointing logic in aircraft

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.

Causing a robot to execute a mission using a behavior tree and a leaf node library

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.

Fast path planning for dynamic avoidance in partially known environments

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.

Conflict detection and avoidance along a current route of a robot

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.

Selection of an alternate destination in response to a contingency event

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.

Evaluation of a ground region for landing a robot

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.

Systems and methods for operating a vehicle in a degraded visual environment

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. The task execution module may be configured to monitor the vehicle or the vehicle controller during execution of the task assignment list to identify any errors.

Adaptive autonomy system architecture

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.

Global positioning denied navigation

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.

Causing a robot to execute a mission using a task graph and a task library

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.

Trajectory planner for a vehicle

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.

Growing community of inventors

Manassas, VA, United States of America

Jeffery Saunders