7 papers
Information-Driven Fault Detection and Identification for Multi-Agent Spacecraft Systems: Collaborative On-Orbit Inspection Mission
Akshita Gupta, Arna Bhardwaj, Yashwanth Kumar Nakka +2
This work presents a global-to-local, task-aware fault detection and identification (FDI) framework for multi-spacecraft systems conducting collaborative inspection missions in low…
Global Task-aware Fault Detection, Identification For On-Orbit Multi-Spacecraft Collaborative Inspection
Akshita Gupta, Yashwanth Kumar Nakka, Changrak Choi +1
In this paper, we present a global-to-local task-aware fault detection and identification algorithm to detect failures in a multi-spacecraft system performing a collaborative inspe…
CaRT: Certified Safety and Robust Tracking in Learning-based Motion Planning for Multi-Agent Systems
Hiroyasu Tsukamoto, Benjamin Rivière, Changrak Choi +2
The key innovation of our analytical method, CaRT, lies in establishing a new hierarchical, distributed architecture to guarantee the safety and robustness of a given learning-base…
Machine Learning Based Relative Orbit Transfer for Swarm Spacecraft Motion Planning
Alex Sabol, Kyongsik Yun, Muhammad Adil +2
In this paper we describe a machine learning based framework for spacecraft swarm trajectory planning. In particular, we focus on coordinating motions of multi-spacecraft in format…
Optimal Multi-Robot Motion Planning via Parabolic Relaxation
Changrak Choi, Muhammad Adil, Amir Rahmani +1
Multi-robot systems offer enhanced capability over their monolithic counterparts, but they come at a cost of increased complexity in coordination. To reduce complexity and to make…
GEOSCAN: Global Earth Observation using Swarm of Coordinated Autonomous Nanosats
Changrak Choi, Anthony B. Davis
The climate crisis we are facing calls for significant improvements in our understanding of natural phenomena, with clouds being identified as a dominant source of uncertainty. To…
Multi-Agent Motion Planning using Deep Learning for Space Applications
Kyongsik Yun, Changrak Choi, Ryan Alimo +5
State-of-the-art motion planners cannot scale to a large number of systems. Motion planning for multiple agents is an NP (non-deterministic polynomial-time) hard problem, so the co…