Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Bayesian Landmark Learning for Mobile Robot Localization
Machine Learning
Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
A Decision-Theoretic Approach to Planning, Perception, and Control
IEEE Expert: Intelligent Systems and Their Applications
Sensor Planning with Bayesian Decision Theory
RUR '95 Proceedings of the International Workshop on Reasoning with Uncertainty in Robotics
PEXIS: Probabilistic experience representation based adaptive interaction system for personal robots
Systems and Computers in Japan
Vision-motion planning of a mobile robot considering vision uncertainty and planning cost
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Learning Bayesian network structures by searching for the best ordering with genetic algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
| Hi-index | 0.00 |
In this paper we propose a novel method of sensor planning for a mobile robot localization problem. We represent the conditional dependence relation between local sensing results, actions, and belief of the global localization using a Bayesian network. Initially, the structure of the Bayesian network is learned from the complete data of the environment using the K2 algorithm combined with a genetic algorithm (GA). In the execution phase, when the robot is kidnapped to some place, it plans an optimal sensing action by taking into account the trade-off between the sensing cost and the global localization belief, which is obtained by inference in the Bayesian network. We have validated the learning and planning algorithm by simulation experiments in an office environment.