الفهرس | Only 14 pages are availabe for public view |
Abstract A new wave of technology powered by mobile manipulators enables us to be versatile in many filed, such as construction, space, production lines, and warehouses. The main objective of the present work is to study the kinematics of a 6 DOF arm manipulator mounted on a high maneuverability mecanum wheeled mobile base and solve the inverse kinematics of the arm using the Neural Networks technique. The current thesis investigates a mobile manipulator with six degrees of freedom (DOFs) that can track a desired trajectory. A motion planning approach is proposed based on examining its kinematics models. The forward and inverse kinematics are analyzed using the Denavit-Hartenberg technique. The end-location effector and orientation are divided into two sections. In the first part, the manipulator gives sub-vectors like position and orientation projected on the Z-axis in the world frame. The mobile base and manipulator follow the necessary path in the second half to get to the sub-vectors on the X and Y axes of the world frame, respectively. The efficacy of the suggested technique is demonstrated using simulated outcomes. The curves and results of the extracted experiment of the mobile base support the accuracy, viability, and excellent maneuverability. The arm manipulator’s kinematic redundancy is considered while solving the inverse issue. The Neural network model is used to address the inverse kinematics problem, made non-linear solving systems easier than traditional techniques for solving such complicated systems. An intermediary step before gathering the training data of 117,000 randomly selected points from the Mobile Manipulator’s entire workspace employed at the neural network was to generate 6 million points using an implemented method. |