This paper 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 (SQP); 3) simultaneously optimize the end-effector motion, stiffness, and contact force for both robots using multi-mode 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.

翻译：本文研究了动态操作中最具挑战性的任务之一——大动量运动物体的抓取。超越准静态操作的范畴，处理高动态物体可以显著提升机器人与周围环境交互的能力。然而，快速运动物体与接近机器人之间不可避免的运动失配会导致巨大的冲击力，进而引发不稳定接触，并对物体和机器人造成不可逆的损伤。为解决上述问题，我们提出了一种在线优化框架，以：1）估计和预测物体的线性和角运动；2）通过序列二次规划（SQP）在物体每个表面上搜索并选择最优接触位置以减轻冲击；3）利用多模态轨迹优化（MMTO）同时优化双臂的末端执行器运动、刚度和接触力；4）基于间接力控制器在柔顺机器人系统中实现感知冲击的抓取运动。我们在仿真中验证了冲击分布、接触选择和感知冲击的MMTO算法，并通过真实世界实验（包括抓取具有明确运动、约束运动和自由飞行的多种大动量运动物体）展示了所提框架的优势。