Advanced SearchSearch Tips
Fuzzy Logic Based Navigation for Multiple Mobile Robots in Indoor Environments
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Fuzzy Logic Based Navigation for Multiple Mobile Robots in Indoor Environments
Zhao, Ran; Lee, Dong Hwan; Lee, Hong Kyu;
  PDF(new window)
The work presented in this paper deals with a navigation problem for multiple mobile robot system in unknown indoor environments. The environment is completely unknown for all the robots and the surrounding information should be detected by the proximity sensors installed on the robots' bodies. In order to guide all the robots to move along collision-free paths and reach the goal positions, a navigation method based on the combination of a set of primary strategies has been developed. The indoor environments usually contain convex and concave obstacles. In this work, a danger judgment strategy in accordance with the sensors' data is used for avoiding small convex obstacles or moving objects which include both dynamic obstacles and other robots. For big convex obstacles or concave ones, a wall following strategy is designed for dealing with these special situations. In this paper, a state memorizing strategy is also proposed for the "infinite repetition" or "dead cycle" situations. Finally, when there is no collision risk, the robots will be guided towards the targets according to a target positioning strategy. Most of these strategies are achieved by the means of fuzzy logic controllers and uniformly applied for every robot. The simulation experiments verified that the proposed method has a positive effectiveness for the navigation problem.
Robot navigation;Multiple robots;Fuzzy logic;Indoor environment;Dynamic obstacle;
 Cited by
Frequentist and Bayesian Learning Approaches to Artificial Intelligence,;

International Journal of Fuzzy Logic and Intelligent Systems, 2016. vol.16. 2, pp.111-118 crossref(new window)
Frequentist and Bayesian Learning Approaches to Artificial Intelligence, The International Journal of Fuzzy Logic and Intelligent Systems, 2016, 16, 2, 111  crossref(new windwow)
D. Janglova, "Neural networks in mobile robot motion," International Journal of Advanced Robotic Systems, vol. 1, no. 1, pp. 15-22, 2004. crossref(new window)

C. E. Thomas, M. A. C. Pacheco, and M. M. B. R. Vellasco, "Mobile robot path planning using genetic algorithms," in Foundations and Tools for Neural Modeling, J. Mira and J. V. Snchez-Andres, Eds. Berlin: Springer-Verlag, 1999, pp. 671-679.

R. Carelli, C. M. Soria, and B. Morales, "Vision-based tracking control for mobile robots," in Proceedings of 12th International Conference on Advanced Robotics (ICAR'05), Seattle, WA, 2005, pp. 148-152. crossref(new window)

W. J. Yim and J. B. Park, "Analysis of mobile robot navigation using vector field histogram according to the number of sectors, the robot speed and the width of the path," in Proceedings of 2014 14th International Conference on Control, Automation and Systems (ICCAS), Seoul, Korea, 2014, pp. 1037-1040. crossref(new window)

M. G. Park, J. H. Jeon, and M. C. Lee, "Obstacle avoidance for mobile robots using artificial potential field approach with simulated annealing," in Proceedings of IEEE International Symposium on Industrial Electronics (ISIE), Busan, Korea, 2001, pp. 1530-1535. crossref(new window)

R. Simmons, "The curvature-velocity method for local obstacle avoidance," in Proceedings of IEEE International Conference on Robotics and Automation, Minneapolis, MN, 1996, pp. 3375-3382. crossref(new window)

J. P. van den Berg and M. H. Overmars, "Roadmap-based motion planning in dynamic environments," IEEE Transactions on Robotics, vol. 21, no. 5, pp. 885-897, 2005. crossref(new window)

C. G. Zhang and Y. G. Xi, "Rolling path planning and safety analysis of mobile robot in dynamic uncertain environment," Control Theory & Applications, vol. 20, no. 1, pp. 37-44. 2003.

T. Jin, "Obstacle avoidance of mobile robot based on behavior hierarchy by fuzzy logic," International Journal of Fuzzy Logic and Intelligent Systems, vol. 12, no. 3, pp. 245-249, 2012. crossref(new window)

H. G. Nguyen, W. H. Kim, and J. H. Shin, "A study on an adaptive robust fuzzy controller with GAs for path tracking of a wheeled mobile robot," International Journal of Fuzzy Logic and Intelligent Systems, vol. 10, no. 1, pp. 12-18, 2010. crossref(new window)

D. Zhao and T. Zou, "A finite-time approach to formation control of multiple mobile robots with terminal sliding mode," International Journal of Systems Science, vol. 43, no. 11, pp. 1998-2014, 2012. crossref(new window)

X. Zhong, X. Zhong, and X. Peng, "Velocity-Change-Space-based dynamic motion planning for mobile robots navigation," Neurocomputing, vol. 143, pp. 153-163, 2014. crossref(new window)

T. P. Nascimento, A. P. Moreira, A. G. Scolari Conceicao, and A. Bonarini, "Intelligent state changing applied to multi-robot systems," Robotics and Autonomous Systems, vol. 61, no. 2, pp. 115-124, 2013. crossref(new window)

D. R. Parhi, S. K. Pradhan, A. K. Panda, and R. K. Behera, "The stable and precise motion control for multiple mobile robots," Applied Soft Computing, vol. 9, no. 2, pp. 477-487, 2009. crossref(new window)

R. Zhao, D. H. Lee, and H. K. Lee, "Mobile robot navigation using optimized fuzzy controller by genetic algorithm," International Journal of Fuzzy Logic and Intelligent Systems, vol. 15, no. 1, pp. 12-19, 2015. crossref(new window)