Mobile Robot Real-time Path Planning Based on Virtual Targets Method

This paper presents a virtual targets-based method for mobile robot path planning in dynamical unknown environments. Fuzzy control is used to judge the status of the obstacles and to generate proper virtual targets for mobile robot to follow. Strategies for formulating the fuzzy rule sets for virtual targets have been proposed. The control parameters of mobile robot, linear velocity and angular velocity, are calculated based on the attractive force generated by the virtual targets using Virtual Force Field method. The repulsive force generated by obstacles is omitted in the virtual targets-based method. The mobile robot may detour obstacles by moving toward virtual targets. Experimental results show that the algorithm can generate a collision-free path in a dynamical unknown environment with stationary and moving obstacles. Furthermore, it can be implemented in real time.

[1]  Hakil Kim,et al.  Path planning and navigation for autonomous mobile robot , 2002, IEEE 2002 28th Annual Conference of the Industrial Electronics Society. IECON 02.

[2]  Jiawei Li,et al.  Obstacle avoidance using fuzzy neural networks , 1998, Proceedings of 1998 International Symposium on Underwater Technology.

[3]  Oussama Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1985, Autonomous Robot Vehicles.

[4]  Yoram Koren,et al.  The vector field histogram-fast obstacle avoidance for mobile robots , 1991, IEEE Trans. Robotics Autom..

[5]  Bin Lei,et al.  A Fuzzy Behaviours Fusion Algorithm for Mobile Robot Real-time Path Planning in Unknown Environment , 2007, 2007 IEEE International Conference on Integration Technology.

[6]  Koren,et al.  Real-Time Obstacle Avoidance for Fast Mobile Robots , 2022 .

[7]  Pedro U. Lima,et al.  Avoiding obstacles - multisensor navigation for nonholonomic robots in cluttered environments , 2004, IEEE Robotics & Automation Magazine.

[8]  M. Sarcinelli-Filho,et al.  Avoiding Obstacles in Mobile Robot Navigation: Implementing the Tangential Escape Approach , 2006, 2006 IEEE International Symposium on Industrial Electronics.

[9]  Teodiano Freire Bastos Filho,et al.  A new approach to avoid obstacles in mobile robot navigation: tangential escape , 2005, ICINCO.

[10]  Viii Supervisor.  Sonar-Based Real-World Mapping and Navigation , 2001 .

[11]  Panos E. Trahanias,et al.  Predictive control of robot velocity to avoid obstacles in dynamic environments , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).