Probabilistic Robot Navigation in Partially Observable Environments
Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence. Learning. Pages 1080-1086.
Abstract
Autonomous mobile robots need very reliable navigation capabilities in order to operate unattended for long periods of time. This paper reports on first results of a research program that uses partially observable Markov models to robustly track a robot's location in office environments and to direct its goal-oriented actions. The approach explicitly maintains a probability distribution over the possible locations of the robot, taking into account various sources of uncertainty, including approximate knowledge of the environment, and actuator and sensor uncertainty. A novel feature of our approach is its integration of topological map information with approximate metric information. We demonstrate the robustness of this approach in controlling an actual indoor mobile robot navigating corridors.
@inproceedings{ijcai1995p140,
title = {Probabilistic Robot Navigation in Partially Observable Environments},
author = {Simmons, Reid G. and Koenig, Sven},
booktitle = {Proceedings of the Fourteenth International Joint Conference on
Artificial Intelligence, {IJCAI-95}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Chris S. Mellish},
pages = {1080--1086},
year = {1995},
note = {Learning},
url = {https://ijcai.org/proceedings/1995/140},
}
TY - CPAPER TI - Probabilistic Robot Navigation in Partially Observable Environments AU - Simmons, Reid G. AU - Koenig, Sven T2 - Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, IJCAI-95 PB - International Joint Conferences on Artificial Intelligence PY - 1995 SP - 1080 EP - 1086 UR - https://ijcai.org/proceedings/1995/140 ER -Copied
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