Control of a mobile robot using generalized dynamic fuzzy neural networks M.J. Er * , Tien Peng Tan, Sin Yee Loh School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, Singapore, Singapore 639798 Received 13 April 2003; revised 20 April 2004; accepted 28 April 2004 Available online 28 May 2004 Abstract This paper presents the design and implementation of a neural fuzzy controller suitable for real-time control of an autonomous mobile robot. The neural fuzzy controller is developed based on the Generalized Dynamic Fuzzy Neural Networks (GDFNN) learning algorithm of Wu et al. (IEEE Transactions on Fuzzy System 9 (4), 2001, 578–594). Not only the parameters of the controller can be optimized, but also the structure of the controller can be self-adaptive. Experimental results show that in comparison with a conventional fuzzy-logic-based controller, the proposed controller is superior in performance. q 2004 Elsevier B.V. All rights reserved. Keywords: Generalized dynamic fuzzy neural networks; Structure learning; Mobile robot control 1. Introduction One of the challenging tasks for mobile robot navigation is to ensure that the robot follows a certain trajectory and avoid any obstacles placed along the trajectory. Mobile robots can be deployed for military applications, intelligent transportation, seaport automation, airport automation, hospital services or outdoor storage and retrieval systems. The robot’s actions directly depend on the perception of the world by means of its sensors. A fuzzy-logic-based approach was selected initially since fuzzy logic is able to provide human reasoning capabilities to deal with uncertainties. Fuzzy logic systems represent knowledge in linguistic form, which permits the designer to define a highly abstract behavior in an intuitive fashion. Unlike the conventional fuzzy control algorithm that requires predefined and fixed fuzzy rules, the Generalized Dynamic Fuzzy Neural Networks (GDFNN) learning algorithm enables fuzzy rules to be recruited or deleted dynamically and parameters estimated automatically. It has been proven to be a superior learning algorithm. Not only it can learn from the environment at a fast rate, but also it supports dynamic self-organizing and adaptive learning. Furthermore, it allows the mobile robot to build its own controller online by means of supervised learning. Because of its properties, the GDFNN is also employed in a wide range of applications such as static function approximation, nonlinear system identification and multi- link robot control [3]. 2. Problem formulation The objective of this work is to implement the neural fuzzy controller on the Khepera II mobile robot [1,2] so as to control it for wall-following tasks in real time. The Khepera II robot is widely used around the world as a platform for various robotics experiments and applications. It is cylindrical in shape, small and compact, measuring 70 mm in diameter and 30 mm in height. Its weight of 80 g and small size allow the experiment to be performed in a small area. Fig. 1 shows a typical Khepera environment whereby the robot is controlled via a serial link by the workstation. The basic configuration of Khepera is composed of the CPU and the sensor/motor board. The sensory/motor board includes two DC motors coupled with incremental sensors and eight analogue infrared (IR) proximity sensors. Each IR sensor is composed of an emitter and an independent receiver. The sensors measure the absolute ambient light 0141-9331/$ - see front matter q 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.micpro.2004.04.002 Microprocessors and Microsystems 28 (2004) 491–498 www.elsevier.com/locate/micpro * Corresponding author. Tel.: þ 65-67906850; fax: þ 65-63162065. E-mail address: emjer@ntu.edu.sg (M.J. Er).