Transputer Neuro-Fuzzy Controlled Behaviour-Based Mobile Robotics System

1.1 A Transputer Mobile Robotics System Mobile robots have received a considerable attention from early research community, from (A. Benmounah, 1991), (Maamri, 1991), (Meystel, 1991) up to this instant (Hegazy, et. al, 2004), and (Pennacchio, et. al., 2005). A fuzzy or neural control Transputer based control mobile robots has received, rather, little attention. A number of, are (Welgarz,1994), (Probert, et. al, 1989), (Iida and Yuta, 1991), and (Brady, et. al., 1993). Recently, neurofuzzy logic controllers are found well suited for controlling mobile robots, (Rusu et. al. 2003). This is because, they are talented of building inferences even under certain uncertainty and unclear conditions, (Kim, and Trivedi, 1998). Having a hierarchical architecture that divides the neurofuzzy into several smaller subsystems will rather condense the negative effect that a large rule-base may have on realtime performance. Problems of insufficient knowledge for designing a rule base can be solved by using a neurofuzzy controller. Learning allows autonomous robots to acquire knowledge by interacting with the environment and subsequently adapting their behaviour. Behaviour learning methods are used to solve complex control problems that autonomous robots encounter in an unfamiliar real-world environment. Neural networks, fuzzy logic, and reinforcementand evolutionary-learning techniques can be utilized to achieve basic behavioural functions necessary to mobile robotics system. There are large number of recent research in mobile robot fuzzy behavior navigation. For instant, (Willgoss and Iqbal, 1999), have reported the use of neurofuzzy learning for teaching mobile robot behaviors, selecting exemplar cases from a potential continuum of behaviors. Proximate active sensing was successfully achieved with infrared in contrast to the usual ultrasonic and viewed the front area of robot movement. (Pennacchio, et. al., 2005), have presented, the FU.LO.RO., a new controller for mobile robot, that moves itself autonomously in an unknown environments. The system has been developed following two different approaches: first one enables a fuzzy controller to determine the robot’s behavior using fuzzy basic rules; the second uses a neurofuzzy controller. (Tsoukalas, et. al., 1997), have presented a neurofuzzy methodology is for motion planning in semi-autonomous mobile robots. The robotic automata considered are devices whose main feature is incremental learning from a human instructor. Fuzzy descriptions are used for the robot to acquire a repertoire of behaviors from an instructor which it may subsequently refine and recall using neural adaptive techniques. The robot is endowed with sensors providing local environmental input and a neurofuzzy internal state processing