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：我们致力于研究机器人参与体育运动，并从拦截朝向配备盾牌的机器人操作器运动的抛射物这一问题入手。为成功完成此任务，机器人需：（i）检测来袭抛射物，（ii）预测抛射物的未来运动轨迹，（iii）规划一条能避开障碍物并拦截抛射物的最短时间快速轨迹，以及（iv）执行所规划的轨迹。这四个步骤必须在操作器的动力学极限和极端时间约束下（本设置中<350毫秒）完成，方能成功拦截抛射物。此外，我们希望这些轨迹足够平滑，以降低机器人关节力矩及其安装平台的冲击力。为此，我们提出一种动力学运动规划框架，该框架通过离线预处理平滑轨迹，实现实时的在线无碰撞执行。我们给出了从感知、预测到执行模块的端到端流水线及其规划框架。通过仿真实验评估，该框架在拦截成功率上优于基线方法。进一步地，我们将该流水线部署于由工业机械臂（ABB IRB-1600）和机载立体相机（ZED 2i）组成的机器人系统，实现了78%的抛射物拦截成功率。