碰撞感知的任务空间二次规划控制
Impact-Aware Task-Space Quadratic-Programming Control
AI总结：
针对机器人以非零速度接触时的冲击失稳问题，提出碰撞感知的任务空间二次规划控制，通过硬件冲击界限和碰撞后状态多面体约束实现一步前瞻鲁棒接触，并在 Panda 与 HRP-4 上验证。
摘要：
机器人通常在刚性表面上以接近零的相对速度建立接触。否则，碰撞诱发的能量会在机器人连杆中传播，并可能对硬件造成不可逆损伤。此外，任务空间接触速度的突变和碰撞力峰值还会导致机器人关节速度和力矩突变，从而损害控制器的稳定性，尤其是基于光滑模型的控制器。现实中，若干任务需要以中等偏高的速度建立接触。我们建议增强以二次规划形式构建的任务空间多目标控制器，使其能够承受三维摩擦碰撞。我们设计了新的约束，并重构了常用约束，使其对前述关节状态突变具有鲁棒性。一旦最优控制搜索空间能够感知以下两点，碰撞事件就成为受控过程：（1）硬件可承受的碰撞界限；（2）以解析方式计算、用于约束碰撞后关键状态的可行集（多面体）。在目标接触点之前及其附近，我们在每个控制周期都假定碰撞将在下一迭代发生。这种近似一步前瞻使控制器对碰撞时间和位置具有鲁棒性。为评估该方法，我们在 Panda 机械臂上实验了其对中等碰撞的承受能力，并在 HRP-4 人形机器人上实现了快速抓取任务。
英文摘要：
Robots usually establish contacts at rigid surfaces with near-zero relative velocities. Otherwise, impact-induced energy propagates in the robot's linkage and may cause irreversible damage to the hardware. Moreover, abrupt changes in task-space contact velocity and peak impact forces also result in abrupt changes in robot joint velocities and torques; which can compromise controllers' stability, especially for those based on smooth models. In reality, several tasks would require establishing contact with moderately high velocity. We propose to enhance task-space multi-objective controllers formulated as a quadratic program to be resilient to frictional impacts in three dimensions. We devise new constraints and reformulate the usual ones to be robust to the abrupt joint state changes mentioned earlier. The impact event becomes a controlled process once the optimal control search space is aware of: (1) the hardware-affordable impact bounds and (2) analytically-computed feasible set (polyhedra) that constrain post-impact critical states. Prior to and nearby the targeted contact spot, we assume, at each control cycle, that the impact will occur at the next iteration. This somewhat one-step preview makes our controller robust to impact time and location. To assess our approach, we experimented its resilience to moderate impacts with the Panda manipulator and achieved swift grabbing tasks with the HRP-4 humanoid robot.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
