Intercepting fast moving objects, by its very nature, is challenging because of its tight time constraints. This problem becomes further complicated in the presence of sensor noise because noisy sensors provide, at best, incomplete information, which results in a distribution over target states to be intercepted. Since time is of the essence, to hit the target, the planner must begin directing the interceptor, in this case a robot arm, while still receiving information. We introduce an tree-like structure, which is grown using kinodynamic motion primitives in state-time space. This tree-like structure encodes reachability to multiple goals from a single origin, while enabling real-time value updates as the target belief evolves and seamless transitions between goals. We evaluate our framework on an interception task on a 6 DOF industrial arm (ABB IRB-1600) with an onboard stereo camera (ZED 2i). A robust Innovation-based Adaptive Estimation Adaptive Kalman Filter (RIAE-AKF) is used to track the target and perform belief updates.

翻译：拦截高速运动物体本质上具有挑战性，因其严格的时间约束。在存在传感器噪声的情况下，该问题进一步复杂化，因为噪声传感器至多只能提供不完整信息，导致目标状态呈现待拦截的分布。由于时间至关重要，为击中目标，规划器必须在持续接收信息的同时开始引导拦截器（此处为机械臂）。我们引入一种树状结构，该结构通过在状态-时间空间中使用运动动力学运动基元生成。此树状结构编码了从单一原点到达多个目标的可达性，同时支持目标信念演化时的实时价值更新以及目标间的无缝切换。我们在搭载立体相机（ZED 2i）的6自由度工业机械臂（ABB IRB-1600）上对拦截任务进行框架评估，采用鲁棒的基于创新的自适应估计自适应卡尔曼滤波器（RIAE-AKF）进行目标跟踪和信念更新。