AN APPROACH DESIGNING AUTONOMOUS ROBOT NAVIGATION SYSTEM BASED ON BEHAVIOR COORDINATION
PDF

Keywords

robot navigation
fuzzy directional relation
fuzzy inference system
mobile robot
behavior hierarchy

Abstract

Robot navigation using fuzzy behavior is suited in unknown and unstructured environment in which each behavior have an individual task. This paper deals with an approach designing autonomous robot navigation system based on fuzzy behaviors including collision avoidance, wall-following, go-to-target. The proposed hierarchy of fuzzy behaviors is used to fuse the command in which each behavior is a fuzzy inference system and its outputs are fuzzy sets. Its inputs are information fused from sensors using fuzzy directional relationship. The simulation results with some statistics show that the system works correctly.

 

https://doi.org/10.29037/ajstd.212
PDF

References

Brooks, R.A. (1986), A Robust Layer Control System for a Mobile Robot, IEEE Journal of Robotics and Automation, vol. 2, No. 1, pp. 14-23.

Ye, C. and Wang, D. (2001), A Novel Behavior Fusion Method for the Navigation of Mobile Robots, Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, Nashville, TN, pp. 3526-3531.

Thongchai, S., Suksakulchai, S., Wilkes, D.M., and Sarkar N. (2000), Sonar BehaviorBased Fuzzy Control for a Mobile Robot, Proceedings of the IEEE International Conference on Systems, Man and Cybernetics.

Yen, J. and Pfluger, N. (1995), A fuzzy logic based extension to Payton and Rosenblatt’s command fusion method for mobile robot navigation, IEEE Trans. Systems, Man and Cybernetics, 25(6), pp. 971-978.

Matsakis, P. and Wendling, L. (1999), A New Way to Represent the Relative Position of Areal Objects, PAMI (IEEE Trans. on Pattern Analysis and Machine Intelligence), vol. 21, No. 7, pp. 634-643.

Bondugula, R., Matsakis, P., and Keller, J. (2004), Force Histograms and Neural Networks for Human-Based Spatial Relationship Generalization, NCI 2004 (IASTED Int. Conf. on Neural Networks and Computational Intelligence), Grindelwald, Switzerland, February 2004, Proceedings.

Skubic, M., Matsakis, P., Chronis, G., and Keller, J. (2003), Generating Multi-level Linguistic Spatial Descriptions from Range Sensor Readings Using the Histogram of Forces," Autonomous Robots, vol. 14, No. 1, pp. 51-69.

Miyajima, K. and Ralescu, A. (1994), Spatial organization in 2D segmented images: representation and recognition of primitive spatial relations, Fuzzy Sets and Systems, 65(2/3), pp. 225-236.

Ngo, L.T., Pham, L.T., and Nguyen, P.H. (2006), Extending fuzzy directional relationship and applying for mobile robot collision avoidance behavior”, International Journal of Advanced Computational Intelligence & Intelligent Informatics, vol. 10, No.4.

Payton, D., Rosenblatt, J., and Keirsey, D. (1990), Plan Guided Reaction, IEEE Trans. System, Man and Cybernetics, vol. 20, No.6, pp. 1370-1382.

Rusu, P., Petriu, E.M., Whalen, T.E., Cornell, A., and Spoelder, H.J.W. (2003), Behavior Based Neuro-Fuzzy Controller for Mobile Robot Navigation, IEEE Trans. On Instrumentation and Measurement, vol. 52, No. 4, pp. 1135-1140.

Ngo, L.T., Pham, L.T., and Nguyen, P.H. (2006), An Approach in the Design Hierarchy of Fuzzy Behaviors for Mobile Robot Navigation”, Proceedings of International Symposium on Management of Engineering (ISME06), Japan, March 2006.

Downloads

Download data is not yet available.