Safety-Aware Robust Model Predictive Control for Robotic Arms in Dynamic Environments

TL;DR

This paper introduces a novel robust MPC framework for robotic arms that dynamically ensures safety and collision avoidance in unpredictable, dynamic environments, improving both safety and efficiency.

Contribution

The paper presents a new RMPC approach combining phase-based control with a safety mode, enabling smooth transitions and real-time obstacle constraint adjustments.

Findings

Enhanced safety in dynamic environments

Faster task completion compared to traditional methods

Improved motion naturalness and collision avoidance

Abstract

Robotic manipulators are essential for precise industrial pick-and-place operations, yet planning collision-free trajectories in dynamic environments remains challenging due to uncertainties such as sensor noise and time-varying delays. Conventional control methods often fail under these conditions, motivating the development of Robust MPC (RMPC) strategies with constraint tightening. In this paper, we propose a novel RMPC framework that integrates phase-based nominal control with a robust safety mode, allowing smooth transitions between safe and nominal operations. Our approach dynamically adjusts constraints based on real-time predictions of moving obstacles\textemdash whether human, robot, or other dynamic objects\textemdash thus ensuring continuous, collision-free operation. Simulation studies demonstrate that our controller improves both motion naturalness and safety, achieving faster task completion than conventional methods.