RM4D：通过降维到4D的适用于常见6/7轴机械臂的可达性与逆可达性组合地图
RM4D: A Combined Reachability and Inverse Reachability Map for Common 6-/7-axis Robot Arms by Dimensionality Reduction to 4D
AI总结：
针对传统6D可达性与逆可达性地图构建繁琐的问题，提出适用于常见6/7轴机械臂的4D可达性地图RM4D，仅需单个4D结构即可支持双向查询，构建速度快一个数量级且无精度损失，可用于移动机械臂抓取规划。
摘要：
机械臂工作空间的相关知识是机器人设计、抓取规划、机器人基座布置等多种任务的基础，因此工作空间表示方法在机器人领域已有充分研究。其中两类重要表示方法是可达性地图与逆可达性地图：前者用于预测给定末端执行器位姿从机器人当前位置是否可达，后者则为期望的末端执行器位姿推荐合适的基座位置。\n通常，可达性地图的构建方式是对包含机器人工作空间的6D空间进行离散化，并判断每个单元格是否可达，随后将可达性地图反转以构建逆地图。这一过程十分繁琐，限制了此类地图的应用。\n在本研究中，我们利用现有6轴与7轴机械臂的共性，将离散化维度从6D降至4D，提出了4D可达性地图（Reachability Map 4D, RM4D）——该地图仅需单个4D数据结构即可支持正向与逆向查询。这使得地图结构更为紧凑，构建速度比现有地图快一个数量级，且无反转开销、无精度损失。我们的实验验证了RM4D在移动机械臂抓取规划中的实用性。
英文摘要：
Knowledge of a manipulator's workspace is fundamental for a variety of tasks including robot design, grasp planning and robot base placement. Consequently, workspace representations are well studied in robotics. Two important representations are reachability maps and inverse reachability maps. The former predicts whether a given end-effector pose is reachable from where the robot currently is, and the latter suggests suitable base positions for a desired end-effector pose. Typically, the reachability map is built by discretizing the 6D space containing the robot's workspace and determining, for each cell, whether it is reachable or not. The reachability map is subsequently inverted to build the inverse map. This is a cumbersome process which restricts the applications of such maps. In this work, we exploit commonalities of existing six and seven axis robot arms to reduce the dimension of the discretization from 6D to 4D. We propose Reachability Map 4D (RM4D), a map that only requires a single 4D data structure for both forward and inverse queries. This gives a much more compact map that can be constructed by an order of magnitude faster than existing maps, with no inversion overheads and no loss in accuracy. Our experiments showcase the usefulness of RM4D for grasp planning with a mobile manipulator.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
