发表机构
Purdue University（普渡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出FRUITNINJA，一种GPU上的任意时间采样规划器，通过精确三次曲线和并行搜索满足执行器限制，在实时模拟中显著提升机械臂抛射物拦截成功率。
AI 中文摘要
抛射物拦截是一个具有挑战性的动态操作问题。用机械臂拦截一个被抛出的物体，需要在物体通过轨迹上的某一点时到达该点。切割还固定了刀片在接触时的速度和方向。因此，目标是在物体下落时移动的机器人状态和到达时间的子集，并且机械臂必须在毫秒内在其执行器限制内到达该目标。我们提出了FRUITNINJA，一种基于采样的任意时间规划器，它在GPU上以批次方式向拦截流形生长一棵树。每条边都是一个精确的三次曲线，其行进时间通过对机械臂动力学的并行搜索找到，因此每条边都满足执行器限制。计划通过一个考虑物体位置和机械臂到达时间不确定性的风险感知目标进行排序。我们在Franka Research 3上，在标定的实时模拟器中与六个基线进行了评估，FRUITNINJA在开放环境中切断了96.7%的投掷，在五个障碍物中切断了68.3%，而最佳基线分别为68.3%和35.0%。
英文摘要
Projectile interception is a challenging dynamic manipulation problem. Intercepting a thrown object with a robot arm requires reaching a point on the object's path as the object passes through it. Slicing also fixes the blade's velocity and orientation at contact. The goal is therefore a subset of the states of the robot and arrival times that moves as the object falls, and the arm must reach it within its actuator limits in milliseconds. We present FRUITNINJA, an anytime sampling-based planner that grows a tree on the GPU in batches toward the interception manifold. Each edge is an exact cubic whose travel time is found by a parallel search against the arm's dynamics, so every edge satisfies the actuator limits. Plans are ranked by a risk-aware objective over the uncertainty in the object's position and the arm's arrival time. We evaluate on a Franka Research 3 against six baselines in a calibrated real-time simulator, where FRUITNINJA cuts 96.7% of tosses in the open and 68.3% among five obstacles, versus the best baseline's 68.3% and 35.0% respectively.
Comments8 pages, 4 figures, 5 tables. Submitted to the 2027 IEEE International Conference on Robotics and Automation
