Information-Lookahead Planning for AUV Mapping
Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence. Pages 1831-1835.
Abstract
Exploration for robotic mapping is typically handled using greedy entropy reduction. Here we show how to apply information lookahead planning to a challenging instance of this problem in which an Autonomous Underwater Vehicle (AUV) maps hydrothermal vents. Given a simulation of vent behaviour we derive an observation function to turn the planning for mapping problem into a POMDP. We test a variety of information state MDP algorithms against greedy, systematic and reactive search strategies. We show that directly rewarding the AUV for visiting vents induces effective mapping strategies. We evaluate the algorithms in simulation and show that our information lookahead method outperforms the others.Zeyn A. Saigol, Richard W. Dearden, Jeremy L. Wyatt, Bramley J. Murton
@inproceedings{ijcai2009p304,
title = {Information-Lookahead Planning for AUV Mapping},
author = {Saigol, Zeyn A. and Dearden, Richard and Wyatt, Jeremy L. and Murton, Bramley J.},
booktitle = {Proceedings of the Twenty-First International Joint Conference on
Artificial Intelligence, {IJCAI-09}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Hiroaki Kitano},
pages = {1831--1835},
year = {2009},
url = {https://ijcai.org/proceedings/2009/304},
}
TY - CPAPER TI - Information-Lookahead Planning for AUV Mapping AU - Saigol, Zeyn A. AU - Dearden, Richard AU - Wyatt, Jeremy L. AU - Murton, Bramley J. T2 - Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence, IJCAI-09 PB - International Joint Conferences on Artificial Intelligence PY - 2009 SP - 1831 EP - 1835 UR - https://ijcai.org/proceedings/2009/304 ER -Copied
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