实时规划拦截高速飞行物，考虑传感器噪声与动态目标状态不确定性。
Think Fast: Real-Time Kinodynamic Belief-Space Planning for Projectile Interception
- 用时空中的动力学运动基元构建树状结构，支持快速更新与目标切换。
- 在6自由度机械臂上实现毫秒级响应，成功拦截高速移动目标。
- 适合需要高实时性与抗噪声能力的机器人拦截任务场景。
拦截高速运动物体本身极具挑战性，因时间约束极为紧张。当存在传感器噪声时，信息不完整导致目标状态分布不确定，需在接收新信息的同时持续规划。本文提出一种基于时空状态空间中动力学运动基元的树状结构，可从单一起点编码对多个目标的可达性，并支持目标信念演化过程中的实时价值更新及目标间无缝切换。在搭载ZED 2i立体摄像头的6自由度工业机械臂（ABB IRB-1600）上评估该框架。采用创新自适应估计卡尔曼滤波器（RIAE-AKF）进行目标跟踪与信念更新，实现鲁棒、实时的拦截决策。
原文摘要 · Abstract (English)
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.
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