This paper proposes a control method to address the physical Human-Robot Interaction (pHRI) challenge in the context of hierarchical tasks. A common approach to managing hierarchical tasks is Hierarchical Quadratic Programming (HQP), which, however, cannot be directly applied to human interaction due to its allowance of arbitrary velocity direction adjustments. To resolve this limitation, we introduce the concept of directional constraints and develop a direction-constrained optimization algorithm to handle the nonlinearities induced by these constraints. The algorithm solves two sub-problems, minimizing the error and minimizing the deviation angle, in parallel, and combines the results of the two sub-problems to produce a final optimal outcome. The mutual influence between these two sub-problems is analyzed to determine the best parameter for combination. Additionally, the velocity objective in our control framework is computed using a variable admittance controller. Traditional admittance control does not account for constraints. To address this issue, we propose a variable admittance control method to adjust control objectives dynamically. The method helps reduce the deviation between robot velocity and human intention at the constraint boundaries, thereby enhancing interaction efficiency. We evaluate the proposed method in scenarios where a human operator physically interacts with a 7-degree-of-freedom robotic arm. The results highlight the importance of incorporating directional constraints in pHRI for hierarchical tasks. Compared to existing methods, our approach generates smoother robotic trajectories during interaction while avoiding interaction delays at the constraint boundaries.

翻译：本文提出一种控制方法，以解决分层任务背景下的物理人机交互（pHRI）挑战。管理分层任务的常用方法是分层二次规划（HQP），然而，由于其允许任意速度方向调整，该方法无法直接应用于人机交互。为克服这一局限，我们引入方向约束的概念，并开发了一种方向约束优化算法来处理这些约束引起的非线性问题。该算法并行求解两个子问题——最小化误差与最小化偏转角，并将两个子问题的结果结合以产生最终的最优解。通过分析这两个子问题间的相互影响，确定了最佳的组合参数。此外，我们控制框架中的速度目标通过可变导纳控制器计算。传统导纳控制未考虑约束条件，为此，我们提出一种可变导纳控制方法以动态调整控制目标。该方法有助于减小约束边界处机器人速度与人类意图之间的偏差，从而提升交互效率。我们在操作者与七自由度机械臂进行物理交互的场景中对所提方法进行了评估。结果凸显了在分层任务的pHRI中纳入方向约束的重要性。与现有方法相比，我们的方法在交互过程中能生成更平滑的机器人轨迹，同时避免约束边界处的交互延迟。