Model Predictive Control for Redundant Robot Arms with Real-Time Obstacle Avoidance

This paper introduces a novel dual-layer Model Predictive Control (MPC) framework for real-time obstacle avoidance in redundant robot manipulators while maintaining precise trajectory tracking. The proposed methodology formulates the control problem as constrained quadratic programming optimization that simultaneously integrates end-effector tracking, kinematic redundancy resolution, and dynamic obstacle constraints within a unified receding horizon framework. The dual-layer hierarchical architecture decomposes the optimization into primary trajectory tracking and secondary obstacle avoidance objectives, exploiting the null space of redundant degrees of freedom to satisfy multiple constraints without compromising primary task performance. Experimental validation on a 7-DOF manipulator demonstrates significant improvements, achieving 98.7 % obstacle avoidance success rate and 73 % reduction in tracking error (2.3 mm vs 8.7 mm) compared to traditional inverse kinematics approaches. The computational efficiency enables real-time implementation at 100 Hz control frequencies with 8.7 ms solution times on standard hardware. Realworld validation using a KUKA LWR IV+ manipulator with RGBD sensors confirms practical applicability, achieving 97.2% success rate in dynamic environments, establishing the framework's viability for industrial robotic applications requiring high-performance obstacle avoidance capabilities.