A Comprehensive Research about Multi-Robot Control Models
- DOI
- 10.2991/978-94-6463-300-9_76How to use a DOI?
- Keywords
- Multi-robot control; centralized model; decentralized model
- Abstract
Multi-agent systems (MAS) are composed of multiple agents that have the ability to learn and make decisions autonomously, while interacting with each other and a shared environment. The collaboration of multiple robots within complex spaces inevitably gives rise to potential conflicts, making the development of models to coordinate the entire system a prominent aspect in this field. Despite the growing scholarly attention towards MAS in recent years, the research in this area has remained complex and obscure, lacking a clear and concise summary of the concepts and pertinent details. Therefore, the purpose of this paper is to introduce two distinct models of MAS and review several previous studies. Specifically, the paper describes the centralized model and decentralized model of MAS, presenting both the traditional framework and recent innovative advancements. A thorough analysis of these models will be conducted, evaluating their respective advantages and disadvantages. Furthermore, a conclusive summary will be provided, along with a prospect for future research in the field of MAS.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Shuyan Zhang AU - Ziyang Zheng AU - Shizhe Zu PY - 2023 DA - 2023/11/27 TI - A Comprehensive Research about Multi-Robot Control Models BT - Proceedings of the 2023 International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2023) PB - Atlantis Press SP - 723 EP - 735 SN - 2352-538X UR - https://doi.org/10.2991/978-94-6463-300-9_76 DO - 10.2991/978-94-6463-300-9_76 ID - Zhang2023 ER -