基于动力学的实时力控框架，让机械臂在复杂环境更安全柔顺地操作
Real-Time Dynamics-Based Torque-Sampling MPPI for Compliant and Force Aware Manipulation
- 将刚体动力学嵌入实时模型预测控制，显式求解非线性系统
- 实测166Hz更新率、0.18秒预测时长，支持高动态响应
- 利用扭矩采样与GPU并行，适合需要力觉反馈的机器人场景
本研究提出一种基于模型预测路径积分（MPPI）的任务空间控制框架。该框架在实时模型预测控制（MPC）中显式求解刚体动力学，并施加安全约束，实现精确的运动与力控，使机械臂在非结构化环境中具备柔顺行为，从而实现安全有效的物理交互。通过利用MPPI，该框架能高效处理传统MPC难以实时求解的非线性动力学问题。此外，我们设计了一种基于扭矩采样的控制架构，充分挖掘基于GPU的并行计算能力，实现高效的柔顺与力感知行为。实验验证表明，该框架在7自由度机械臂上实现了超过166 Hz的求解器更新率和0.18秒的预测时域。
原文摘要 · Abstract (English)
This study proposes a novel Model Predictive Path Integral (MPPI)-based task-space control framework. The proposed framework explicitly solves rigid-body dynamics within a real-time MPC formulation and enforces safety constraints, enabling accurate motion and force control that yields compliant behaviors for safe and effective physical interaction of robotic manipulators in unstructured environments. By leveraging MPPI, the proposed framework efficiently handles nonlinear dynamics that are difficult to solve with conventional MPC approaches in real-time. Furthermore, we develop a torque-sampling-based control architecture that enables efficient exploitation of GPU-based parallelization, resulting in effective compliant and force-aware behaviors. As a result, the proposed framework achieves a solver update rate of over 166 Hz with a 0.18 s prediction horizon, and its performance is validated through real-world experiments on a 7-DoF manipulator.
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