Mobile robot localization using active sensing based on Bayesian network inference

Robotics and Autonomous Systems

Volumn 55, Issue 4, 2007, Pages 292-305

  Zhou, Hongjun ^a   Sakane, Shigeyuki ^a  

^a CHUO UNIVERSITY   (Japan)

Author keywords

Bayesian network; Localization; Mobile robot; Sensor planning; Structure learning

Indexed keywords

BAYESIAN NETWORKS; COMPUTER SIMULATION; DATA REDUCTION; GENETIC ALGORITHMS; MOTION PLANNING; SENSORS;

PLANNING ALGORITHMS; SENSOR PLANNING; STRUCTURE LEARNING;

MOBILE ROBOTS;

EID: 33947137238     PISSN: 09218890     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.robot.2006.11.005     Document Type: Article

References (23)

1. Thrun S. Probabilistic algorithms in robotics. AI Magazine 21 4 (2000) 93-109
2. Thrun S. Bayesian landmark learning for mobile robot localization. Machine Learning 33 (1998) 41-76
3. N. Roy, W. Burgard, D. Fox, S. Thrun, Coastal navigation: Robot navigation under uncertainty in dynamic environments, in: Proc. of IEEE Int. Conf. on Robotics and Automation, 1999
4. Thrun S., Fox D., Burgard W., and Dellaert F. Robust Monte Carlo localization for mobile robots. Artificial Intelligence (AI) (2001)
5. H. Asoh, Y. Motomura, I. Hara, S. Akaho, S. Hayamizu, T. Matsui, Combining probabilistic map and dialog for robust life-long office navigation, in: Proc. of Int. Conf. on Intelligent Robots and Systems, IROS'96, 1996, pp. 880-885
6. Inamura T., Inaba M., and Inoue H. PEXIS : Probabilistic experience representation based adaptive interaction system for personal robots. Systems and Computers in Japan 35 6 (2004) 98-109
7. Basye K., Dean T., Kirman J., and Lejter M. A decision-theoretic approach to planning, perception, and control. IEEE Expert 7 4 (1992) 58-65
8. Fox D., Burgard W., and Thrun S. Active Markov localization for mobile robots. Robotics and Autonomous Systems 25 (1998) 195-207
9. P. Jensfelt, S. Kristensen, Active global localization for a mobile robot using multiple hypothesis tracking, in: Proc. of Workshop on Reasoning with Uncertainty in Robot Navigation (by IJCAI'99), 1999, pp. 13-22
10. S. Kristensen, Sensor planning with Bayesian decision analysis, Ph.D. Thesis, Faculty of Technology and Science, Aalborg University, Aalborg, Denmark, 1996
11. J. Miura, Y. Shirai, Vision-motion planning for a mobile robot considering vision uncertainty and planning cost, in: Proc. of 15th Int. Joint Conf. on Artificial Intelligence, 1997, pp. 1194-1200
12. H. Zhou, S. Sakane, Sensor planning for mobile robot localization using Bayesian network representation and inference, in: Proc. of IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, 2002, pp. 440-446
13. D. Heckerman, A Bayesian approach to learning causal networks, Technical Report MSR-TR-95-04, Microsoft Research, March, 1995
14. Cooper G., and Herskovits E. A Bayesian method for the induction of probabilistic networks from data. Machine Learning 9 (1992) 309-347
15. Larranaga P., Kuijpers C., Murga R., and Yurramendi Y. Learning Bayesian network structures by searching for the best ordering with genetic algorithms. IEEE Transactions on Systems, Man and Cybernetics 26 4 (1996) 487-493
16. D. Corney, Designing food with Bayesian belief networks, in: Fourth Int. Conf. on Adaptive Computing in Design and Manufacture, 2000
17. Jensen F.V. Bayesian Networks and Decision Graphs (2001), Springer
18. Cowell R.G., Dawid A.P., Lauritzen S.L., and Spiegelhalter D.J. Probabilistic Networks and Expert Systems (1999), Springer
19. Pearl J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (1988), Morgan Kaufmann
20. Kwan M. Graphic programming using ODD or EVEN points. Chinese Math. 1 (1996) 273-277
21. Edmonds J., and Johnson E.L. Matching Euler tours and the Chinese postman. Mathematical Programming 5 (1973) 88-124
22. Murphy K. The Bayes Net Toolbox for Matlab. Computing Science and Statistics vol. 33 (2001). http://www.ai.mit.edu/~murphyk/Software/BNT/bnt.html Software is available at
23. C. Houck, J. Joines, M. Kay, A genetic algorithm for function optimization: A Matlab implementation, Technical Report NCSU-IE 95-09, 1995. Software is available at http://www.ie.ncsu.edu/mirage/GAToolBox/gaot/