Comparative Analysis of Jacobian-Based Motion Planning Methods for Redundant Manipulators

Abstract

Self-driving laboratories are automated research environments that utilize advanced technologies to conduct experiments and analyze data with minimal human intervention. The presence of robotic manipulators within an autonomous driving research facility plays a crucial role in carrying out tasks effectively. Motion planning schemes are used for planning motions of a manipulator equipped inside self-driving laboratories to facilitate the transition from an initial pose to a final pose during a task execution. In this paper, three motion planning schemes developed based on Jacobian methods are implemented to traverse a redundant manipulator with a coupled finger gripper through given trajectories. RRT* algorithm is used for planning trajectories and inverse solutions of the manipulator are computed separately using three Jacobian based methods such as Jacobian Transpose (JT), Pseudo Inverse (PI), and Damped Least Square (DLS) methods. Smoothness and RMSE pose errors of end-effector motions along with velocity continuity, acceleration profile, jerk, and snap values of joint motions are analysed for determining an efficient motion planning method. Advantages and disadvantages of the proposed motion planning schemes mentioned above are evaluated using simulation studies to determine a suitable inverse solution technique for the tasks. This paper demonstrates motion planning algorithms that achieve precise positioning while ensuring the efficient operation of robotic manipulators operating in service sectors, characterized by smoother trajectories, reduced computational, and energy demands.