5 papers
Learning to Adapt Control Barrier Functions Under Epistemic and Aleatoric Uncertainty
Taekyung Kim, Robin Inho Kee, Dimitra Panagou
Control barrier functions (CBFs) provide a tractable mechanism for enforcing safety constraints in robotic systems, but their practical performance depends strongly on the choice o…
Time Shift Governor-Guided MPC with Collision Cone CBFs for Safe Adaptive Cruise Control in Dynamic Environments
Robin Inho Kee, Taehyeun Kim, Anouck Girard +1
This paper introduces a Time Shift Governor (TSG)-guided Model Predictive Controller with Control Barrier Functions (CBFs)-based constraints for adaptive cruise control (ACC). This…
Learning to Refine Input Constrained Control Barrier Functions via Uncertainty-Aware Online Parameter Adaptation
Taekyung Kim, Robin Inho Kee, Dimitra Panagou
Control Barrier Functions (CBFs) have become powerful tools for ensuring safety in nonlinear systems. However, finding valid CBFs that guarantee persistent safety and feasibility r…
Vision-Ultrasound Robotic System based on Deep Learning for Gas and Arc Hazard Detection in Manufacturing
Jin-Hee Lee, Dahyun Nam, Robin Inho Kee +2
Gas leaks and arc discharges present significant risks in industrial environments, requiring robust detection systems to ensure safety and operational efficiency. Inspired by human…
Constrained Control for Autonomous Spacecraft Rendezvous: Learning-Based Time Shift Governor
Taehyeun Kim, Robin Inho Kee, Ilya Kolmanovsky +1
This paper develops a Time Shift Governor (TSG)-based control scheme to enforce constraints during rendezvous and docking (RD) missions in the setting of the Two-Body problem. As a…