Information-Lookahead Planning for AUV Mapping
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
Cite
Text
Saigol et al. "Information-Lookahead Planning for AUV Mapping." International Joint Conference on Artificial Intelligence, 2009.Markdown
[Saigol et al. "Information-Lookahead Planning for AUV Mapping." International Joint Conference on Artificial Intelligence, 2009.](https://mlanthology.org/ijcai/2009/saigol2009ijcai-information/)BibTeX
@inproceedings{saigol2009ijcai-information,
title = {{Information-Lookahead Planning for AUV Mapping}},
author = {Saigol, Zeyn A. and Dearden, Richard and Wyatt, Jeremy L. and Murton, Bramley J.},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2009},
pages = {1831-1836},
url = {https://mlanthology.org/ijcai/2009/saigol2009ijcai-information/}
}