发表机构
School of Mechanical Engineering, Tel-Aviv University（特拉维夫大学机械工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对视觉传感器等可视性设备，提出VisPRM和VisRRT两种新型采样TAMP算法，经仿真与物理实验验证，其成功率和运行效率优于RRT、PRM等适配算法。
AI 中文摘要
机器人任务与运动规划（TAMP）算法通过将末端执行器工具的特定功能和约束（如夹具、烙铁）直接融入规划过程，实现自主运行。本文针对一类关键设备研究基于采样的TAMP算法，这类设备的独特属性使传统规划方法失效，包括视觉传感器、相机、手电筒、定向天线等基于可视性的仪器，其视场特性导致常用启发式算法和距离指标效果不佳。我们提出两种针对可视性任务的新型采样算法：VisPRM与VisRRT。VisPRM采用环境分层分解，利用可视性完整性概念高效采样与目标有清晰视线的构型；VisRRT配备专用逆运动学求解器，可在适当时机向目标方向“扫视”，加速关键构型的发现。通过仿真与物理实验，我们证明VisPRM和VisRRT相比RRT、PRM、VIR的适配版本，具有更高的成功率和更快的运行时间。
英文摘要
Robot Task and Motion Planning (TAMP) algorithms enable autonomous operation by incorporating the specific functions and constraints of end-effector tools, such as grippers or soldering irons, directly into the planning process. In this paper, we explore sampling-based TAMP algorithms specifically designed for a critical subset of devices whose unique properties make traditional planning methods ineffective. Visibility-based instruments, such as exteroceptive sensors, cameras, flashlights and directional antennas, are essential across a vast array of human activities. The unique properties of these devices, and particularly, their field-of-view, render many widely used heuristics and distance metrics less effective. We introduce two new sampling-based algorithms, FOV-PRM and FOV-RRT, designed to tackle visibility-based tasks. FOV-PRM employs a hierarchical decomposition of the environment, leveraging the concept of visibility integrity, to efficiently sample configurations with a clear line-of-sight to the target. A specialized Inverse Kinematics solver enables FOV-RRT to "glance" in the direction of the target at opportune moments, facilitating the rapid discovery of key configurations. We show that FOV-PRM and FOV-RRT achieve a higher success rate and faster runtimes compared to adaptations of RRT, PRM and VIR, through both simulated and physical experiments.
CommentsAccepted to IEEE RA-L
