3 papers
cs.RO2023
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…
cs.RO2021
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…
cs.RO2020
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…