Acting Uncertainty: Discrete Bayesian Models for Mobile-Robot Navigation

Authors:
A. Cassandra;L. Kaelbling;J. Kurien
Affiliations:
-;-;-
Venue:
Acting Uncertainty: Discrete Bayesian Models for Mobile-Robot Navigation
Year:
1996

Citing 0
Cited 6

A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots

Autonomous Robots
Rapid Concept Learning for Mobile Robots

Autonomous Robots
Learning of plan execution policies for indoor navigation

AI Communications - Special issue on KI-2001
Mobile Robotics Planning Using Abstract Markov Decision Processes

ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
Region-based approximations for planning in stochastic domains

UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Proximity-based non-uniform abstractions for approximate planning

Journal of Artificial Intelligence Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

Discrete Bayesian models have been used to model uncertainty for mobile-robot navigation, but the question of how actions should be chosen remains largely unexplored. This paper presents the optimal solution to the problem, formulated as a partially observable Markov decision process. Since solving for the optimal control policy is intractable, in general, it goes on to explore a variety of heuristic control strategies. The control strategies are compared experimentally, both in simulation and in runs on a robot.