We are interested in studying sports with robots and starting with the problem of intercepting a projectile moving toward a robot manipulator equipped with a shield. To successfully perform this task, the robot needs to (i) detect the incoming projectile, (ii) predict the projectile's future motion, (iii) plan a minimum-time rapid trajectory that can evade obstacles and intercept the projectile, and (iv) execute the planned trajectory. These four steps must be performed under the manipulator's dynamic limits and extreme time constraints (<350ms in our setting) to successfully intercept the projectile. In addition, we want these trajectories to be smooth to reduce the robot's joint torques and the impulse on the platform on which it is mounted. To this end, we propose a kinodynamic motion planning framework that preprocesses smooth trajectories offline to allow real-time collision-free executions online. We present an end-to-end pipeline along with our planning framework, including perception, prediction, and execution modules. We evaluate our framework experimentally in simulation and show that it has a higher blocking success rate than the baselines. Further, we deploy our pipeline on a robotic system comprising an industrial arm (ABB IRB-1600) and an onboard stereo camera (ZED 2i), which achieves a 78% success rate in projectile interceptions.

翻译：摘要： 我们致力于研究机器人参与体育运动，首先解决抛射物向配备盾牌的机器人操控器移动时的拦截问题。为成功完成此任务，机器人需要 (i) 检测来袭抛射物，(ii) 预测抛射物的未来运动，(iii) 规划一条能避开障碍物并拦截抛射物的最小时间快速轨迹，以及 (iv) 执行规划的轨迹。这四个步骤必须在操控器的动态极限和严格时间限制（我们的场景中为 <350 毫秒）内完成，以成功拦截抛射物。此外，我们要求这些轨迹平滑，以减小机器人的关节扭矩及其所安装平台的冲击。为此，我们提出了一种动动态运动规划框架，该框架离线预处理平滑轨迹，以实现实时的无碰撞在线执行。我们呈现了一个端到端的流程以及我们的规划框架，其中包括感知、预测和执行模块。我们在仿真中通过实验评估了该框架，并表明其拦截成功率高于基线方法。此外，我们将该流程部署在由工业臂（ABB IRB-1600）和车载立体相机（ZED 2i）组成的机器人系统上，实现了 78% 的抛射物拦截成功率。