ToMPC: Task-Oriented Model Predictive Control via ADMM for Safe Robotic Manipulation

IF 4.6 2区计算机科学 Q2 ROBOTICS

IEEE Robotics and Automation Letters Pub Date : 2025-06-12 DOI:10.1109/LRA.2025.3579205

Xinyu Jia;Wenxin Wang;Jun Yang;Yongping Pan;Haoyong Yu

引用次数: 0

Abstract

This letter proposes a task-oriented model predictive control (ToMPC) framework for safe and efficient robotic manipulation in open workspaces. The framework unifies collision-free motion and robot-environment interaction to address diverse scenarios. Additionally, it introduces task-oriented obstacle avoidance that leverages kinematic redundancy to enhance manipulation efficiency in obstructed environments. This complex optimization problem is solved by the alternating direction method of multipliers (ADMM), which decomposes the problem into two subproblems tackled by differential dynamic programming (DDP) and quadratic programming (QP), respectively. The effectiveness of this approach is validated in simulation and hardware experiments on a Franka Panda robotic manipulator. Results demonstrate that the framework can plan motion and/or force trajectories in real time, maximize the manipulation range while avoiding obstacles, and strictly adhere to safety-related hard constraints.

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基于ADMM的任务导向模型预测控制在机器人安全操作中的应用

这封信提出了一个面向任务的模型预测控制（ToMPC）框架，用于开放工作空间中安全有效的机器人操作。该框架将无碰撞运动和机器人与环境的交互结合起来，以解决不同的场景。此外，它引入了以任务为导向的避障，利用运动学冗余来提高在受阻环境中的操作效率。该方法将该问题分解为两个子问题，分别用微分动态规划（DDP）和二次规划（QP）求解。通过熊猫机器人的仿真和硬件实验验证了该方法的有效性。结果表明，该框架可以实时规划运动和/或力轨迹，在避开障碍物的同时最大化操作范围，并严格遵守与安全相关的硬约束。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Robotics and Automation Letters Computer Science-Computer Science Applications

CiteScore

9.60

自引率

15.40%

发文量

1428

期刊介绍： The scope of this journal is to publish peer-reviewed articles that provide a timely and concise account of innovative research ideas and application results, reporting significant theoretical findings and application case studies in areas of robotics and automation.