为机械臂动态避障提出安全感知鲁棒控制框架
Safety-Aware Robust Model Predictive Control for Robotic Arms in Dynamic Environments
- 结合相位控制与鲁棒安全模式，实现平滑切换
- 实时预测障碍物运动，动态调整约束条件
- 在仿真中提升任务效率与避障安全性
工业机械臂在精准抓取操作中至关重要，但在动态环境中规划无碰撞轨迹仍面临挑战，主要源于传感器噪声和时变延迟等不确定性。传统控制方法在此类条件下常失效，促使研究者发展具有约束收紧的鲁棒模型预测控制（RMPC）策略。本文提出一种新型RMPC框架，融合基于相位的名义控制与鲁棒安全模式，实现安全与正常运行间的平滑过渡。该方法根据对移动障碍物（人、机器人或其他动态物体）的实时预测动态调整约束，确保连续无碰撞运行。仿真结果表明，所提控制器在保持更高安全性的同时，提升了运动自然性，并实现比传统方法更快的任务完成速度。
原文摘要 · Abstract (English)
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.
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