Abstract
This paper propose simulation results of navigation for a mobile robot with an active camera, which is intelligently searching the goal location in unknown dynamic environments using sensor fusion, data that is usually required in real-time mobile robotics or simulation. Instead of using "physical sensor fusion" method which generates the trajectory of a robot based upon the environment model and sensory data. In this paper, "command fusion" method is used to govern the robot motions. The navigation strategy is based on the combination of fuzzy rules tuned for both goal-approach and obstacle-avoidance. To identify the environments, a distributed control command technique is introduced, where the sensory data of ultrasonic sensors and a vision sensor are fused into the identification process.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Er, M., Tan, T.P., Loh, S.Y.: Control of a mobile robot using generalized dynamic fuzzy neural networks. Microprocessors and Microsystems 28, 491–498 (2004)
Zadeh, L.A.: Outline of a New Approach to the Analysis of Complex Systems and Decision Processes. IEEE Transactions on Systems, Man, and Cybernetics 3(1), 28–44 (1973)
Nair, D., Aggarwal, J.K.: Moving Obstacle Detection from a Navigation Robot. IEEE Trans. on robotics and automation 14(3), 404–416 (1998)
Bentalba, S., El Hajjaji, A., Tachid, A.: Fuzzy Control of a Mobile Robot: a New Approach. In: IEEE International Conference on Control Applications, pp. 69–72 (1997)
Furuhashi, T., Nakaoka, K., Morikawa, K., Maeda, H., Uchikawa, Y.: A study on knowledge finding using fuzzy classifier system. Journal of Japan Society for Fuzzy Theory and Systems, vol 7(4), 839–848 (1995)
Itani, H., Furuhashi, T.: A study on teaching information understanding by autonomous mobile robot. Trans. of SICE 38(11), 966–973 (2002)
Beom, H.R., Cho, H.S.: A sensor-Based Navigation for a Mobile Robot Using Fuzzy Logic and Reinforcement Learning. IEEE Trans. on system, man, and cybernetics 25(3), 464–477 (1995)
Ohya, A., Kosaka, A., Kak, A.: Vision-Based Navigation by a Mobile Robot with Obstacle Avoidance Using Single-Camera Vision and Ultrasonic Sensing. IEEE Transactions on Robotics and Automation 14(6), 969–978 (1998)
Tunstel, E.: Fuzzy-behavior synthesis, coordination, and evolution in an adaptive behavior hierarchy. In: Saffiotti, A., Driankov, D. (eds.) Fuzzy Logic Techniques for Autonomous 470 TUNSTEL, de OLIVEIRA, AND BERMAN Vehicle Navigation. STUDFUZZ, vol. 61, pp. 205–234. Springer, Heidelberg (2000)
Tunstel, E.: Fuzzy behavior modulation with threshold activation for autonomous vehicle navigation. In: 18th International Conference of the North American Fuzzy Information Processing Society, pp. 776–780 (1999)
Er, M., Tan, T.P., Loh, S.Y.: Control of a mobile robot using generalized dynamic fuzzy neural networks. Microprocessors and Microsystems 28, 491–498 (2004)
Jouffe, L.: Fuzzy inference system learning by reinforcement method. IEEE Trans. Syst., Man, Cybern., Part C 28(3), 338–355 (1998)
Leng, G., McGinnity, T.M., Prasad, G.: An approach for on-line extraction of fuzzy rules using a self-organising fuzzy neural network. Fuzzy Sets and Systems 150, 211–243 (2005)
Nishina, T., Hagiwara, M.: Fuzzy inference neural network. Neurocomputing 14, 223–239 (1997)
Takahama, T., Sakai, S., Ogura, H., Nakamura, M.: Learning fuzzy rules for bang-bang control by reinforcement learning method. Journal of Japan Society for Fuzzy Theory and Systems 8(1), 115–122 (1996)
Mehrjerdi, H., Saad, M., Ghommam, J.: Hierarchical Fuzzy Cooperative Control and Path Following for a Team of Mobile Robots. IEEE/ASME Transactions on Mechatronics 16(5), 907–917 (2011)
Wang, D.S., Zhang, Y.S., Si, W.J.: Behavior-based hierarchical fuzzy control for mobile robot navigation in dynamic environment. In: 2011 Chinese Control and Decision Conference(CCDC), pp. 2419–2424 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Kim, M., Jin, T. (2014). Simulation of Mobile Robot Navigation Using the Distributed Control Command Based Fuzzy Inference. In: Zhang, X., Liu, H., Chen, Z., Wang, N. (eds) Intelligent Robotics and Applications. ICIRA 2014. Lecture Notes in Computer Science(), vol 8917. Springer, Cham. https://doi.org/10.1007/978-3-319-13966-1_45
Download citation
DOI: https://doi.org/10.1007/978-3-319-13966-1_45
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13965-4
Online ISBN: 978-3-319-13966-1
eBook Packages: Computer ScienceComputer Science (R0)