@inproceedings{74547fe66086428aaeb83c5822eb400c,
title = "Distributed Behavior Control Method for Indoor Multi-robot Collaborative Odor Source Localization",
abstract = "The paper proposes a distributed behavior control method for indoor multi-robot collaborative odor source localization, aiming to improve the efficiency of odor source search while avoiding collisions. This problem is crucial in scenarios such as fire rescue, drug inspection and so on. Multiple robots search for the plume in a spiral fashion. They share the information of the location with the highest detected odor concentration among them and guide robots that do not detect gas to move towards it, which can accelerate the plume discovery speed of the whole system. Multiple robots use particle filter to estimate the location of the odor source. They identify the odor source by setting particle convergence and distance convergence thresholds. A state machine is used to achieve effective odor source search by flexibly selecting the appropriate method according to the current environment and task requirements. Monte Carlo simulation results demonstrate that the proposed algorithm outperforms multi-robot infotaxis method 50 %.",
keywords = "Finite state machine, Multi-robot collaboration, Odor source localization, Particle filter",
author = "Mengjie Jing and Bin Xin",
note = "Publisher Copyright: {\textcopyright} 2024 Technical Committee on Control Theory, Chinese Association of Automation.; 43rd Chinese Control Conference, CCC 2024 ; Conference date: 28-07-2024 Through 31-07-2024",
year = "2024",
doi = "10.23919/CCC63176.2024.10662785",
language = "English",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "5428--5433",
editor = "Jing Na and Jian Sun",
booktitle = "Proceedings of the 43rd Chinese Control Conference, CCC 2024",
address = "United States",
}