Concurrent Bayesian Learners for Multi-Robot Patrolling Missions

D. Portugal, M. S. Couceiro and R. P. Rocha

In Proc. of Workshop on Towards Fully Decentralized Multi-Robot Systems: Hardware, Software and Integration, 2013 IEEE Int. Conf. on Robotics and Automation (ICRA 2013), Karlsruhe, Germany, May 6, 2013.

Abstract

Distributed robot systems have been adopted lately for security purposes, such as in automatic multi-robot patrolling of infra-structures. Research has shown that deterministic patrol routes can lead to effective performance. However, they can potentially be predicted by intelligent intruders. This work presents a probabilistic multi-robot patrolling strategy, where each autonomous agent uses Bayesian reasoning to decide its moves in the environment. Each member of the team is a learning agent that collects information and assesses the state of the system, using concurrent reinforcement learning to influence future moves. This way, effective coordination in collective patrol is achieved, as shown by preliminary simulation experiments.

Index Terms — Multi-robot patrolling, distributed patrol, Bayesian learners.

Full text

You may ask Rui Rocha for an electronic copy of this publication’s full text by e-mail:
.

Please select for your message’s subject ‘Requesting Rui Rocha’s electronic copy’ and include on the message’s body your full name, title and affiliation, why do you need to access the publication and the BibTeX information below.

BibTeX

@INPROCEEDINGS(Portugal_et_al_13c,

AUTHOR = "Portugal, D. and Couceiro, M. S. and Rocha, R. P.",

TITLE = "Concurrent Bayesian Learners for Multi-Robot Patrolling Missions",

BOOKTITLE = "Proc. of Workshop on Towards Fully Decentralized Multi-Robot Systems: Hardware, Software and Integration, 2013 IEEE Int. Conf. on Robotics and Automation (ICRA 2013)",

ADDRESS = "Karlsruhe, Germany",

YEAR = "2013",

MONTH = "May"

)

(top of the page)

Last update: 29/07/2013