发表机构
EPFL（洛桑联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出基于释放状态的模型框架，通过分阶段动力学识别和参数筛选，为6自由度机械臂设计投掷运动与回旋镖，首次实现机器人产生返回式回旋镖飞行，释放速度51 rad/s，返回距离0.31米。
AI 中文摘要
投掷产生气动升力的物体可以极大地扩展机器人投掷超越弹道飞行的能力。回旋镖是一个具有挑战性的例子，因为其飞行强烈依赖于释放速度、姿态和旋转，而机器人操纵器无法轻易复现人类投掷中使用的快速运动。我们提出了一种以释放状态为中心的基于模型的机器人回旋镖投掷框架。我们分阶段识别回旋镖的飞行动力学，以预测飞行如何随设计变化而变化。为了系统地设计机器人投掷运动，我们根据候选参数在不确定接触条件下控制释放旋转的强度和一致性来筛选它们。然后使用这些模型为具有有限关节速度的6自由度操纵器设计投掷运动和回旋镖。据我们所知，这是第一个产生返回式回旋镖飞行的机器人操纵器。在演示的返回试验中，回旋镖以51弧度/秒（8.1转/秒）的速度释放，到达距机器人底座2.03米处，并返回降落在距底座0.31米处。成功的释放与测量的人类投掷显著不同，表明机器人不需要模仿人类投掷运动即可实现返回飞行。项目页面可在该https URL获取。
英文摘要
Throwing objects that generate aerodynamic lift can greatly extend robot throwing beyond ballistic flight. A returning boomerang is a challenging example because its flight depends strongly on the release velocity, attitude, and spin, while robotic manipulators cannot readily reproduce the rapid motions used in human throwing. We present a model-based framework for robotic boomerang throwing centered on the release state. We identify the boomerang flight dynamics in stages to predict how flight changes across design variations. To systematically design the robot throwing motion, we screen candidate parameters according to how strongly and consistently they control release spin under uncertain contact conditions. These models are then used to design the throwing motion and boomerang for a 6-DoF manipulator with limited joint speeds. To our knowledge, this is the first robotic manipulator to generate a returning boomerang flight. In the demonstrated returning trial, the boomerang is released at 51 rad/s (8.1 rev/s), reaches 2.03 m from the robot base, and returns to touch down 0.31 m from the base. The successful release differs significantly from the measured human throws, showing that a robot need not imitate human throwing motion to achieve a returning flight. The project page is available at https://robot-boomerang.github.io
CommentsProject page: https://robot-boomerang.github.io
