WE PROPOSE TO CREATE A DEEP REINFORCEMENT MACHINE LEARNING (ML) SYSTEM AND DEVELOPMENT APPROACH THAT SUPPOR…

Public award description

WE PROPOSE TO CREATE A DEEP REINFORCEMENT MACHINE LEARNING (ML) SYSTEM AND DEVELOPMENT APPROACH THAT SUPPORTS CERTIFICATION FOR MISSION-CRITICAL APPLICATIONS THROUGH OBSERVABLE, VERIFIABLE ARCHITECTURES AND FUNCTIONAL SAFETY METHODOLOGIES, TO HANDLE THE FULL SCOPE OF ONBOARD, AUTONOMOUS SPACECRAFT GUIDANCE, NAVIGATION, AND CONTROL (GNC). ML SYSTEMS ARE CURRENTLY BEING USED FOR GNC IN MANY AUTONOMOUS SYSTEMS, THE BIGGEST INVESTMENTS BEING FOR SELF-DRIVING CARS AND ROBOTICS. WE WILL INVESTIGATE THE FEASIBILITY OF ADOPTING CERTIFICATION STANDARDS BASED ON THE LATEST DEVELOPMENTS FROM THE AUTOMOTIVE INDUSTRY COMBINED WITH TRADITIONAL AEROSPACE CERTIFICATION PROCESSES. IN PHASE 1 WE WILL IMPLEMENT A LOW FIDELITY ML-BASED GNC SYSTEM IN ORDER TO DEMONSTRATE THE VIABILITY OF THE ML APPROACH, WHEREBY THE TRAJECTORY PLAN IS DETERMINED BY A NEURAL NETWORK AND THE CONTROL LOOP IS EXECUTED WITH AN EXTENDED KALMAN FILTER. THIS DEMONSTRATION WILL INFORM THE DRAFTING OF REQUIREMENTS AND SPECIFICATIONS FOR A FUNCTIONAL SAFETY DEVELOPMENT FRAMEWORK.