Applying Bayesian Learning to Multi-Robot Patrol

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

In Proc. of 11th IEEE Int. Symp. on Safety, Security, and Rescue Robotics (SSRR 2013), Linköping, Sweden, Oct. 21-26, 2013.

Abstract

Performing a patrolling mission with multiple mobile robots is a challenging task that requires effective coordination between agents. While predefined patrol circuits may lead to suitable routing performance, their deterministic nature eases the task of potential intruders. Therefore, the need to propose probabilistic strategies becomes evident. In this paper, a new multi-robot patrolling strategy is proposed, in which concurrent learning agents adapt their moves to the state of the system at the time, using Bayesian decision. When patrolling a given site, each agent evaluates the context and adopts a reward-based learning technique that influences future moves. Experiments show the potential of the approach, which outperforms several other state-of-the-art strategies.

Index Terms — Multi-robot patrolling; Bayesian learner; coordination.

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 = "Applying Bayesian Learning to Multi-Robot Patrol",

BOOKTITLE = "Proc. of 11th IEEE Int. Symp. on Safety, Security, and Rescue Robotics (SSRR 2013)",

ADDRESS = "Linköping, Sweden",

YEAR = "2013",

MONTH = "Oct."

)

(top of the page)

Last update: 25/10/2013