Title :
Elman Fuzzy Adaptive Control for Obstacle Avoidance of Mobile Robots Using Hybrid Force/Position Incorporation
Author :
Shuhuan Wen ; Wei Zheng ; Jinghai Zhu ; Xiaoli Li ; Shengyong Chen
Author_Institution :
Key Lab. of Ind. Comput. Control Eng. of Hebei Province, Yanshan Univ., Qinhuangdao, China
fDate :
7/1/2012 12:00:00 AM
Abstract :
This paper addresses a virtual force field between mobile robots and obstacles to keep them away with a desired distance. An online learning method of hybrid force/position control is proposed for obstacle avoidance in a robot environment. An Elman neural network is proposed to compensate the effect of uncertainties between the dynamic robot model and the obstacles. Moreover, this paper uses an Elman fuzzy adaptive controller to adjust the exact distance between the robot and the obstacles. The effectiveness of the proposed method is demonstrated by simulation examples.
Keywords :
adaptive control; collision avoidance; force control; fuzzy control; learning (artificial intelligence); mobile robots; neurocontrollers; recurrent neural nets; uncertain systems; Elman fuzzy adaptive control; Elman neural network; dynamic robot model; hybrid force control; mobile robot; obstacle avoidance; online learning method; position control; uncertainties effect; virtual force field; Collision avoidance; Dynamics; Force; Mobile robots; PD control; Wheels; Elman neural network (ENN); fuzzy PD control; hybrid force/position control; mobile robot; obstacle avoidance; path planning;
Journal_Title :
Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on
DOI :
10.1109/TSMCC.2011.2157682
