Abstract-Fuzzy logic enables designers to control complex systems more effectively than traditional approaches as it provides a simple way to arrive at a definite conclusion upon ambiguous, imprecise or noisy information. In this paper, we describe the development of two miniature LEGO robots, which are the line following and the light searching mobile robots to provide a better understanding of fuzzy logic control theory and real life application for an undergraduate training system. This study is divided into two parts. In the first part, an object sorter robot is built to perform pick and place task to load different colour objects on a fuzzy logic controlled line following robot which then carries the preloaded objects to a goal by following a white line. In the second part, an intelligent fuzzy logic controlled light searching robot with the capability to navigate in a maze is developed. All of the robots are constructed by using the LEGO Mindstorms kit. Interactive C programming language is used to program fuzzy logic robots. Experimental results show that the robots has successfully track the predefined path and navigate towards light source under the influence of the fuzzy logic controller; and therefore can be used as a training system in undergraduate fuzzy logic class.
Tracking a desired trajectory in joint space has been favored in several robot manipulators and end-effector control scheme due to the simplicity and high sampling rate offered by the joint space scheme. This usually require the trajectory conversion process, of the desired position, velocity, and acceleration, from Cartesian space to joint space using conventional inverse kinematics solutions which have been known to have several limitations and which often pose a big challenge, computationally, and even prohibitive, to achieve, for some robot designs.
In this study, an intelligent approach to the inverse kinematics problem using adaptive neuro-fuzzy inference system (ANFIS) is proposed for control of a 3-DOF end-effector based neurorehabilitation platform. The joint positions, velocities, andaccelerations are achieved/predicted by means of the ANFIS networks which is trained with data obtained from the forward kinematics, velocity Jacobian and the differential of the velocity kinematics equations. Simulation studies have shown that the proposed intelligent techniques has simplified both the trajectory conversion process and the control framework while tracking is achieve to a high degree of accuracy.
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