Selective Method Based on Auctions for Map Inspection by Robotic Teams - Foundations on Natural and Artificial Computation - page 174

Selective Method Based on Auctions for Map
Inspection by Robotic Teams

Manuel Martín-Ortiz 1 , Juan Pereda 1 ,JavierdeLope 2 , and Féliz de la Paz 3

1 ITRB Labs
Research, Technology Development and Innovation, S.L
2 Computational Cognitive Robotics
Universidad Politécnica de Madrid
3 Dept. Artificial Intelligence
UNED
{manuel.martin,juan.pereda}@itrblabs.eu, [e-mail],
[e-mail]

Abstract. In the inspection of a known environment by a team of
robots, communication problems may exists between members of the
team, even, due to the hostile environment these members can be dam-
aged. In this paper, a redundant, robust and fault tolerant method to
cover a known environment using a multi-agent system and where the
communications are not guaranteed is presented. Through a simple auc-
tion system for cooperation and coordination, the aim of this method is
to provide an effective way to solve communication or hardware failures
problems in the inspection task of a known environment. We have con-
ducted several experiments in order to verify and validate the proposed
approach. The results are commented and compared to other methods.

1 Introduction

Nowadays, the autonomous robotics field covers a wide range of projects, from
UAV (Unmanned Aerial Vehicles) [6,10,3] to humanoids with social skills [14,2].
This wide range of works shows the great progress that is been achieved in the
last decade and opens a promising path for future research. The main topic of
the autonomous robotics focuses on techniques that allow robots to collaborate
between themselves in order to achieve a specific task [8] as it can be the explo-
ration of surroundings [4,11]. Multi-robot systems and collaborative robotics [7]
are becoming one of the biggest and most exciting challenges in this field.
One of the best applications that could be find in collaborative robotics, is the
assistance to human beings in hostile environments. Pipes, sewers or abandoned
mines are examples of this type of hostile environments. Projects like Makro
[15] or MOIRA [13] and authors like Thrun [18] have already dealt with these
environments, marking the lines to follow and emphasizing the importance of
inspection tasks in these places. Kawaguchi [9] and Zhang [19] design robotic
systems for pipe inspection.
The inspection task can be dealt with solutions based on A* algorithm, like
Learning Real-Time A* (LRTA*) [16]. This algorithm used in robot teams needs