This article investigates one of the most challenging tasks in dynamic manipulation—catching large-momentum moving objects. Beyond the realm of quasi-static manipulation, dealing with highly dynamic objects can significantly improve the robot's capability of interacting with its surrounding environment. Yet, the inevitable motion mismatch between the fast moving object and the approaching robot will result in large impulsive forces, which lead to the unstable contacts and irreversible damage to both the object and the robot. To address the above problems, we propose an online optimization framework to: 1) estimate and predict the linear and angular motion of the object, 2) search and select the optimal contact locations across every surface of the object to mitigate impact through sequential quadratic programming, 3) simultaneously optimize the end-effector motion, stiffness, and contact force for both robots using multimode trajectory optimization (MMTO), and 4) realise the impact-aware catching motion on the compliant robotic system based on indirect force controller. We validate the impulse distribution, contact selection, and impact-aware MMTO algorithms in simulation and demonstrate the benefits of the proposed framework in real-world experiments including catching large-momentum moving objects with well-defined motion, constrained motion, and free-flying motion.

中文翻译：

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具有冲击感知能力的双手捕捉大动量物体

本文研究了动态操纵中最具挑战性的任务之一——捕捉大动量移动物体。除了准静态操纵领域之外，处理高度动态的物体可以显着提高机器人与周围环境交互的能力。然而，快速移动的物体和接近的机器人之间不可避免的运动不匹配会产生很大的脉冲力，从而导致物体和机器人的不稳定接触和不可逆的损坏。为了解决上述问题，我们提出了一个在线优化框架：1）估计和预测物体的线性和角运动，2）搜索并选择物体每个表面的最佳接触位置，以通过顺序二次规划减轻影响，3）使用多模式轨迹优化（MMTO）同时优化两个机器人的末端执行器运动、刚度和接触力，4）基于间接力控制器在柔顺机器人系统上实现冲击感知捕捉运动。我们在仿真中验证了脉冲分布、接触选择和冲击感知 MMTO 算法，并在现实实验中展示了所提出的框架的优势，包括捕捉具有明确运动、约束运动和自由飞行的大动量移动物体运动。