Multimodal Control of Manipulators: Coupling Kinematics and Vision for Self-Driving Laboratory Operations

TL;DR

This paper develops and compares three Jacobian-based motion planning schemes for a redundant manipulator with a coupled gripper, integrating trajectory planning and inverse kinematics to optimize performance in laboratory operations.

Contribution

It introduces three novel Jacobian-based motion planning schemes using RRT* and screw theory, analyzing their efficiency and suitability for manipulator control.

Findings

Damped Least Square method provides smoother trajectories.

Pseudo Inverse method yields higher manipulability.

Jacobian Transpose method is computationally faster.

Abstract

Motion planning schemes are used for planning motions of a manipulator from an initial pose to a final pose during a task execution. A motion planning scheme generally comprises of a trajectory planning method and an inverse kinematic solver to determine trajectories and joints solutions respectively. In this paper, 3 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 screw theory based forward kinematic equations are solved for determining joint solutions of the manipulator and gripper. Inverse solutions are computed separately using 3 Jacobian based methods such as Jacobian Transpose (JT), Pseudo Inverse (PI), and Damped Least Square (DLS) methods. Space Jacobian and manipulability measurements of the manipulator and gripper are obtained using screw theory formulations. Smoothness and RMSE error of generated trajectories and velocity continuity, acceleration profile, jerk, and snap values of joint motions are analysed for determining an efficient motion planning method for a given task. Advantages and disadvantages of the proposed motion planning schemes mentioned above are analysed using simulation studies to determine a suitable inverse solution technique for the tasks.