Fuzzy Logic Control of Robot Manipulator in the Presence of Fixed Obstacle

This paper presents a solution for the problem of learning and controlling a 2R-plan robot manipulator in the presence of fixed obstacle. The objective is to move the arm from an initial position (source) to a final position (target) without collision. Potential field methods are rapidly gaining popularity in obstacle avoidance applications for mobile robots and manipulators. The idea of imaginary forces acting on a robot has been suggested by Andrews and Hogen [1983] and Khatib [1985]. Thus, we propose an approach based on potential fields principle, we define the target as an attractive pole (given as a vector directly calculated from the target position) and the obstacle as a repulsive pole (a vector derived by using fuzzy logic techniques). The linguistic rules, the linguistic variables and the membership functions are the parameters to be determined for the fuzzy controller conception. A learning method based on gradient descent for the self tuning of these parameters is introduced. Thus, it is necessary to have an expert person for moving the arm manually. During this operation of teaching, the arm moves and memorizes the data (inputs and outputs). This operation is used to find the controller parameters in order to reach the desired outputs for given inputs.