Open AccessDECENTRALIZED MULTI-ROBOT PLANNING TO EXPLORE AND PERCEIVE
Author(s) -
Laëtitia Matig,
Laurent Jeanpierre,
AbdelIllah Mouaddib
Publication year - 2015
Publication title -
acta polytechnica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.207
H-Index - 15
eISSN - 1805-2363
pISSN - 1210-2709
DOI - 10.14311/ap.2015.55.0169
Subject(s) - robot , artificial intelligence , computer science , markov decision process , object (grammar) , adaptation (eye) , process (computing) , contest , computer vision , perception , human–computer interaction , space (punctuation) , mobile robot , markov process , mathematics , statistics , physics , neuroscience , law , political science , optics , biology , operating system
In a recent French robotic contest, the objective was to develop a multi-robot system able to autonomously map and explore an unknown area while also detecting and localizing objects. As a participant in this challenge, we proposed a new decentralized Markov decision process (Dec-MDP) resolution based on distributed value functions (DVF) to compute multi-robot exploration strategies. The idea is to take advantage of sparse interactions by allowing each robot to calculate locally a strategy that maximizes the explored space while minimizing robots interactions. In this paper, we propose an adaptation of this method to improve also object recognition by integrating into the DVF the interest in covering explored areas with photos. The robots will then act to maximize the explored space and the photo coverage, ensuring better perception and object recognition.