IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE) e-ISSN: 2278-1676,p-ISSN: 2320-3331, Volume 10, Issue 2 Ver. I (Mar – Apr. 2015), PP 81-88 www.iosrjournals.org DOI: 10.9790/1676-10218188 www.iosrjournals.org 81 | Page Redundant Three-link Robot Manipulator Local Approach Ivan Isho Gorial Mechatronics Engineering Branch, Control and Systems Engineering Department, University of Technology, Baghdad, Iraq Abstract: This paper proposed a fuzzy logic‐based joint space path planning system of robot manipulator with 3‐DOF joints. The proposed planning system was composed of several separate fuzzy units which control individually each manipulator joint. The Main inputs of the first fuzzy block were ∆ 1  + 1, 1 , the main inputs for the second fuzzy block were ∆2+1, 2  and ∆3+1, 3  of the third fuzzy block. The outputs were∆ 1  +1 and ∆ 2  +1and ∆ 3  +1respectively. The objective was to move the arm from an initial configuration (start configuration) to a final configuration (goal configuration). Simulation results shows that the robot reached the goal configuration successfully. Keywords: Joint space, path planning, fuzzy logic, robot manipulator, motion planning. I. Introduction A robot manipulator is a movable chain of links interconnected by joints. One end is fixed to the ground, and a hand or end effector that can move freely in space is attached at the other end [1]. Most robotic manipulators are strong rigid with powerful motors, strong gearing systems, and very accurate models of the dynamic response [2]. Robotics is a relatively new field of modem technology that crosses traditional engineering boundaries. Understanding the complexity of robots and their applications requires knowledge of electrical engineering, mechanical engineering, industrial engineering, computer science, and mathematics. New disciplines of engineering, such as manufacturing engineering, applications engineering, and knowledge engineering, are beginning to emerge in order to solve more complex problem [3]. The accuracy and complexities of positions of robots may be better achieved by supplementing human capabilities with computer power in order to generate these complex trajectories and to control the robot manipulator, respectively [3]. The advantage of fuzzy logic not only fast response, low cost and good real-time ability also it is not necessary to know the exact model of the object or process to be controlled when apply the fuzzy logic control and it meet the real- time requirements for robot motion planning [4]. There are several robot motion planning methods such as artificial potential field method, configuration space method, and method based on the fuzzy logic. The artificial potentialfield method needs the accurate information of the obstacles, so it cannot be applied for moving objects or inaccurate information. The configuration space method maps the obstacles into the C- space. The computation burden is huge. It would fail to meet the real-time requirements for robot motion planning [5, 6, 7, 8]. Wei and Shimin [9] presented a paper on 3-D Path Planning using Neural networks for a Robot Manipulator. They reported that it is complex to find a good path when the robot is in a complex dynamic change condition. Algorithms for the device to perform path planning and trajectory prediction were described. Kermiche et al [10] demonstrated fuzzy logic control of robot manipulator in the presence of fixed obstacle. They presented a solution for the problem of learning and controlling a 2R-plan robot manipulator in the presence of fixed obstacle. The objective was to move the arm from an initial position (source) to a final position (target) without collision. Potential field methods were rapidly gaining popularity in obstacle avoidance applications for mobile robots and manipulators. They propose an approach based on potential fields principle, and defined 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 were the parameters to be determined for the fuzzy controller conception. A learning method based on gradient descent for the self-tuning of these parameters was introduced. Thus, it was 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 was used to find the controller parameters in order to reach the desired outputs for given inputs. Koker et al [11] presented a neural network based inverse kinematics solution of a robotic manipulator. Inverse kinematics problem is generally more complex for robotic manipulators. Many traditional solutions such as geometric, iterative and algebraic are inadequate if the joint structure of the manipulator is more complex. In that study, three-joint robotic manipulator simulation software, developed in their previous