Model Predictive Control Design of a 3-DOF Robot Arm Based on Recognition of Spatial Coordinates

TL;DR

This paper presents a model predictive control approach for a 3-DOF robotic arm that uses visual recognition to accurately reach and grasp objects by calculating optimal servo torques.

Contribution

It integrates visual recognition with MPC for precise robotic arm control, including coordinate transformation and dynamic modeling.

Findings

Successful implementation of MPC for 3-DOF arm control

Accurate object recognition and coordinate calculation

Optimized servo input torques for precise grasping

Abstract

This paper uses Model Predictive Control (MPC) to optimise the input torques of a Three-Degrees-of-Freedom (DOF) robotic arm, enabling it to operate to the target position and grasp the object accurately. A monocular camera is firstly used to recognise the colour and depth of the object. Then, the inverse kinematics calculation and the spatial coordinates of the object through coordinate transformation are combined to get the required rotating angle of each servo. Finally, the dynamic model of the robotic arm structure is derived and the model predictive control is applied to simulate the optimal input torques of servos to minimize the cost function.