用ADMM方法实现安全高效的机器人操作轨迹规划
ToMPC: Task-oriented Model Predictive Control via ADMM for Safe Robotic Manipulation
- 通过ADMM分解复杂优化问题，分别由DDP和QP求解
- 实现实时运动与力轨迹规划，严格满足安全约束
- 利用机械臂冗余度提升受限环境下的操作效率
本文提出一种面向开放工作空间中安全高效机器人操作的任务导向模型预测控制（ToMPC）框架。该框架统一处理无碰撞运动与人机交互，引入任务导向的避障机制，利用运动学冗余提升受阻环境下的操作效率。复杂的优化问题通过交替方向乘子法（ADMM）求解，将问题分解为由微分动态规划（DDP）和二次规划（QP）分别处理的两个子问题。在Franka Panda机械臂上进行的仿真与硬件实验验证了该方法的有效性：系统可实时规划运动与/或力轨迹，在严格遵守安全硬约束的前提下最大化操作范围。
原文摘要 · Abstract (English)
This paper 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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