@article{4b892c32b31d46c786fa46da80228152,
title = "Scalable distributed algorithms for multi-robot near-optimal motion planning",
abstract = "This paper investigates a class of motion planning problems where multiple unicycle robots desire to safely reach their respective goal regions with minimal traveling times. We present a distributed algorithm which integrates decoupled optimal feedback planning and distributed conflict resolution. Collision avoidance and finite-time arrival at the goal regions are formally guaranteed. Further, the computational complexity of the proposed algorithm is independent of the robot number. A set of simulations are conducted to verify the scalability and near-optimality of the proposed algorithm.",
author = "Guoxiang Zhao and Minghui Zhu",
note = "Funding Information: This work was partially supported by the grants National Science Foundation, USA ECCS-1710859 and National Science Foundation, USA CNS 1830390 . The material in this paper was presented at the 58th IEEE Conference on Decision and Control, December 11–13, 2019, Nice, France. This paper was recommended for publication in revised form by Associate Editor Dimos V. Dimarogonas under the direction of Editor Christos G. Cassandras. Funding Information: This work was partially supported by the grants National Science Foundation, USAECCS-1710859 and National Science Foundation, USACNS 1830390. The material in this paper was presented at the 58th IEEE Conference on Decision and Control, December 11–13, 2019, Nice, France. This paper was recommended for publication in revised form by Associate Editor Dimos V. Dimarogonas under the direction of Editor Christos G. Cassandras. Publisher Copyright: {\textcopyright} 2022 Elsevier Ltd",
year = "2022",
month = jun,
doi = "10.1016/j.automatica.2022.110241",
language = "English (US)",
volume = "140",
journal = "Automatica",
issn = "0005-1098",
publisher = "Elsevier Limited",
}