Multi-robot fire searching in unknown environment

被引:0
|
作者
Marjovi A.
Nunes J.G.
Marques L.
de Almeida A.
机构
来源
Springer Tracts in Advanced Robotics | 2010年 / 62卷
关键词
Avoiding obstacle - Behavioral decision makings - Frontier-based exploration - Future improvements - Mobile robotic - Multi-robot exploration - Potential field methods - Unknown environments;
D O I
10.1007/978-3-642-13408-1_31
中图分类号
学科分类号
摘要
Exploration of an unknown environment is a fundamental concern in mobile robotics. This paper presents an approach for cooperative multi-robot exploration, fire searching and mapping in an unknown environment. The proposed approach aims to minimize the overall exploration time, making it possible to locate fire sources in an efficient way. In order to achieve this goal, the robots cooperate in order to individually and simultaneously, explore different areas of the environment while they identify fire sources. The proposed approach employs a decentralized frontier based exploration method which evaluates the cost/gain ratio to navigate to target way-points. The target way-points are obtained by an A* search variant algorithm. The potential field method is used to control the robots’ motion while avoiding obstacles. When a robot detects a fire, it estimates the flame’s position by triangulation. The communication between the robots is done in a decentralized control manner where they share the necessary data to generate a map of the environment and to perform cooperative actions in a behavioral decision making way. This paper presents simulated and experimental results of the proposed exploration and fire search method and concludes with a discussion of the obtained results and future improvements. © Springer-Verlag Berlin Heidelberg 2010.
引用
收藏
页码:341 / 351
页数:10
相关论文
共 50 条
  • [11] A Strategy of Multi-robot Formation and Obstacle Avoidance in Unknown Environment
    Chi, Ting
    Zhang, Chengjin
    Song, Yong
    Feng, Jinglun
    2016 IEEE INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION (ICIA), 2016, : 1455 - 1460
  • [12] Formation and Obstacle Avoidance in the Unknown Environment of Multi-Robot System
    Zhang, Tao
    Li, Xiaqin
    Zhu, Yi
    Chen, Song
    Cheng, Yu
    Song, Jingyan
    2009 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS (ROBIO 2009), VOLS 1-4, 2009, : 729 - +
  • [13] Multi-robot Exploration System in Unknown Environment Based on Submap
    Zhang, Yinggang
    Zhang, Yanduo
    Li, Xun
    2021 4TH INTERNATIONAL CONFERENCE ON ROBOTICS, CONTROL AND AUTOMATION ENGINEERING (RCAE 2021), 2021, : 256 - 260
  • [14] Cooperative Multi-robot Searching Algorithm
    Jeon, Seohyun
    Jang, Minsu
    Lee, Daeha
    Cho, Young-Jo
    INTELLIGENT AUTONOMOUS SYSTEMS 12 , VOL 2, 2013, 194 : 749 - 756
  • [15] On Randomized Searching for Multi-robot Coordination
    Hvezda, Jakub
    Kulich, Miroslav
    Preucil, Libor
    INFORMATICS IN CONTROL, AUTOMATION AND ROBOTICS (ICINCO 2018), 2020, 613 : 364 - 383
  • [16] Cooperative Multi-robot Map-building under Unknown Environment
    Liu, Chunyang
    Ma, Yingwei
    Liu, Chang'an
    2009 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND COMPUTATIONAL INTELLIGENCE, VOL III, PROCEEDINGS, 2009, : 392 - 396
  • [17] Multi-robot exploration of an unknown environment, efficiently reducing the odometry error
    Rekleitis, IM
    Dudek, G
    Milios, EE
    IJCAI-97 - PROCEEDINGS OF THE FIFTEENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOLS 1 AND 2, 1997, : 1340 - 1345
  • [18] An Energy-Efficient Method for Multi-Robot Reconnaissance in an Unknown Environment
    Quann, Michael
    Ojeda, Lauro
    Smith, William
    Rizzo, Denise
    Castanier, Matthew
    Barton, Kira
    2017 AMERICAN CONTROL CONFERENCE (ACC), 2017, : 2279 - 2284
  • [19] Multi-robot formation control in unknown environment based on improved DWA
    Chang L.
    Shan L.
    Dai Y.-W.
    Qi Z.-D.
    Kongzhi yu Juece/Control and Decision, 2022, 37 (10): : 2524 - 2534
  • [20] A PSO-based multi-robot cooperation method for target searching in unknown environments
    Dadgar, Masoud
    Jafari, Shahram
    Hamzeh, Ali
    NEUROCOMPUTING, 2016, 177 : 62 - 74