@inproceedings{edbde3c8e01448a996e43cec9ca5b5c1,
title = "Artificial Potential Field Method with Predicted State and Input Threshold for Multi-agent System",
abstract = "In this article, we consider the multi-agent control based on the artificial potential field (APF) method with predicted state and input threshold. APF is a very practical and efficient method for multi-agent control. However, the accuracy of APF is susceptible to communication delay. Hence, we introduce the predictive state model to reduce the impact of this delay when the agent is avoiding collisions and maintaining formation. Meanwhile, the input threshold is applied to ensure the safety of the system. The introduction of the predicted state and the input threshold leads to the failure of traditional APF. Therefore, we propose a new controller based on the improved APF. Then, the Lyapunov stability of the designed controller is analyzed. Simulation results show the effectiveness of the proposed controller and its superiority over the original method.",
keywords = "Artificial potential field, Input threshold, Multi-agent system, Predicted state model",
author = "Dengxiu Yu and Zhize Du and Zhen Wang",
note = "Publisher Copyright: {\textcopyright} 2022, Springer Nature Switzerland AG.; 13th International Conference on Swarm Intelligence, ICSI 2022 ; Conference date: 15-07-2022 Through 19-07-2022",
year = "2022",
doi = "10.1007/978-3-031-09726-3_4",
language = "英语",
isbn = "9783031097256",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "35--44",
editor = "Ying Tan and Yuhui Shi and Ben Niu",
booktitle = "Advances in Swarm Intelligence - 13th International Conference, ICSI 2022, Proceedings, Part II",
}