US 12,326,732 B2
Graph construction and execution ML techniques
Darrell L. Young, Falls Church, VA (US); Christopher A. Eccles, Ashburn, VA (US); and Franklin Tanner, Ashburn, VA (US)
Assigned to Raytheon Company, Arlington, VA (US)
Filed by Raytheon Company, Arlington, VA (US)
Filed on Apr. 16, 2021, as Appl. No. 17/232,818.
Claims priority of provisional application 63/010,994, filed on Apr. 16, 2020.
Prior Publication US 2021/0325891 A1, Oct. 21, 2021
Int. Cl. G05D 1/00 (2024.01); G06F 18/20 (2023.01); G06N 20/00 (2019.01)
CPC G05D 1/0221 (2013.01) [G05D 1/0214 (2013.01); G05D 1/0251 (2013.01); G06F 18/295 (2023.01); G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A device comprising:
at least one memory storing data of a path generation machine learning (ML) technique, an executive multi-agent reinforcement learning (MARL) ML technique, a motion ML technique, and a goal associated with a target; and
processing circuitry configured to:
implement the path generation ML technique to determine multiple, distinct paths between the device and a target resulting in pre-determined paths that jointly form a graph, the path generation ML technique is constrained to generate a path of the pre-determined paths that is achievable based on a physics-based model of a vehicle and minimizes usage of a resource and then blocking that path to find an alternative path of the pre-determined paths that minimizes the usage of the resource;
determine a node of the pre-determined paths as an intersection of at least two paths of the pre-determined paths;
implement the executive MARL ML technique to determine which of the at least two pre-determined paths to take at a node of the graph to achieve the goal and reach the target resulting in a selected path; and
implement the motion ML technique to cause the device to traverse the selected path.