Frontier-based exploration is the most common approach to exploration, a fundamental problem in robotics. In frontier-based exploration, robots explore by repeatedly computing (and moving towards) frontiers, the segments which separate the known regions from those unknown. However, most frontier detection algorithms process the entire map data. This can be a time consuming process which slows down the exploration. In this paper, we present two novel frontier detection algorithms: WFD, a graph search based algorithm and FFD, which is based on processing only the new laser readings data. In contrast to state-of-the-art methods, both algorithms do not process the entire map data. We implemented both algorithms and showed that both are faster than a state-of-the-art frontier detector implementation (by several orders of magnitude). Copyright © 2012, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.
|Number of pages
|11th International Conference on Autonomous Agents and Multiagent Systems 2012, AAMAS 2012: Innovative Applications Track
|Published - 4 Jun 2012