Fuzzy Logic Based Navigation for Multiple Mobile Robots in Indoor Environments

다국어 초록 (Multilingual Abstract)

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.

목차 (Table of Contents)

• Abstract
• 1. Introduction
• 2. Problem Formulation and Working Assumptions
• 3. Navigation Strategies
• 4. Simulation Experiments
• 5. Conclusions
• References

참고문헌 (Reference)

1 R. Carelli, "Vision-based tracking control for mobile robots" 148-152, 2005

2 Xunyu Zhong, "Velocity-Change-Space-based dynamic motion planning for mobile robots navigation" Elsevier BV 143 : 153-163, 2014

3 Dayal Ramakrushna Parhi, "The stable and precise motion control for multiple mobile robots" Elsevier BV 9 (9): 477-487, 2009

4 R. Simmons, "The curvature-velocity method for local obstacle avoidance" 3375-3382, 1996

5 C. G. Zhang, "Rolling path planning and safety analysis of mobile robot in dynamic uncertain environment" 20 (20): 37-44, 2003

6 J.P. van den Berg, "Roadmap-based motion planning in dynamic environments" Institute of Electrical & Electronics Engineers (IEEE) 21 (21): 885-897, 2005

7 M. G. Park, "Obstacle avoidance for mobile robots using artificial potential field approach with simulated annealing" 1530-1535, 2001

8 진태석, "Obstacle Avoidance of Mobile Robot Based on Behavior Hierarchy by Fuzzy Logic" 한국지능시스템학회 12 (12): 245-249, 2012

9 Danica Janglova, "Neural Networks in Mobile Robot Motion" InTech 1 (1): 15-22, 2004

10 Ran Zhao, "Mobile Robot Navigation using Optimized Fuzzy Controller by Genetic Algorithm" 한국지능시스템학회 15 (15): 12-19, 2015

11 Tiago P. Nascimento, "Intelligent state changing applied to multi-robot systems" Elsevier BV 61 (61): 115-124, 2013

12 C. E. Thomas, "Foundations and Tools for Neural Modeling" Springer-Verlag 671-679, 1999

13 W. J. Yim, "Analysis of mobile robot navigation using vector field histogram according to the number of sectors, the robot speed and the width of the path" 1037-1040, 2014

14 Dongya Zhao, "A finite-time approach to formation control of multiple mobile robots with terminal sliding mode" Informa UK Limited 43 (43): 1998-2014, 2012

15 Hoang-Giap Nguyen, "A Study on an Adaptive Robust Fuzzy Controller with GAs for Path Tracking of a Wheeled Mobile Robot" 한국지능시스템학회 10 (10): 12-18, 2010