@inproceedings{7f3b96ef29304404b9f6b81e7dc2a5b1,
title = "Tube-Based Robust Obstacle Avoidance and Following Control for Vehicle Formations",
abstract = "This paper presents a robust planning and control framework for vehicle formations, by proposing a safety-aware obstacle-avoidance planner and a formation-following controller. Specifically, a tube-based model predictive controller is designed based on a robust positively invariant (RPI) set for nonholonomic vehicles. Leveraging the RPI set together with the vehicle size and planning position mismatch, a safe region can be constructed for each follower. Subsequently, this region is actuated in a robust rapidly exploring random tree star planner via safety-aware collision checking. The overall scheme exhibits strong robustness in both planning and control, as validated by simulations.",
keywords = "formation following, robust planning, tube-based model predictive control, Vehicle formation",
author = "Wenxian Wang and Deyuan Meng",
note = "Publisher Copyright: {\textcopyright}2025 IEEE.; 7th International Conference on Control and Robotics, ICCR 2025 ; Conference date: 04-12-2025 Through 06-12-2025",
year = "2025",
doi = "10.1109/ICCR67607.2025.11372110",
language = "英语",
series = "2025 7th International Conference on Control and Robotics, ICCR 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "94--98",
booktitle = "2025 7th International Conference on Control and Robotics, ICCR 2025",
address = "美国",
}