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Planning for robotic exploration based on forward simulation

Research output: Contribution to journalArticleScientificpeer-review

Details

Original languageEnglish
Pages (from-to)15-31
Number of pages17
JournalROBOTICS AND AUTONOMOUS SYSTEMS
Volume83
DOIs
Publication statusPublished - 1 Sep 2016
Publication typeA1 Journal article-refereed

Abstract

We address the problem of controlling a mobile robot to explore a partially known environment. The robot’s objective is the maximization of the amount of information collected about the environment. We formulate the problem as a partially observable Markov decision process (POMDP) with an information-theoretic objective function, and solve it applying forward simulation algorithms with an open-loop approximation. We present a new sample-based approximation for mutual information useful in mobile robotics. The approximation can be seamlessly integrated with forward simulation planning algorithms. We investigate the usefulness of POMDP based planning for exploration, and to alleviate some of its weaknesses propose a combination with frontier based exploration. Experimental results in simulated and real environments show that, depending on the environment, applying POMDP based planning for exploration can improve performance over frontier exploration.