This letter presents MA-NBCP, a novel hierarchical framework targeting autonomous exploration and inspection for high-DoF manipulators. MA-NBCP iteratively selects the manipulability-augmented next-best-configuration for exploring unknown regions surrounding a manipulator, while providing collision-avoidance guarantee. Toward developing MA-NBCP, an efficient exploration information system (EIS) is first built that dynamically maintains critical, extensible information to facilitate the exploration planning process. Leveraging EIS, the higher level of MA-NBCP selects the best exploration subregion based on an angle-weighted metric. At the lower level, a Fibonacci grids-based spherical uniform sampling strategy generates many candidate viewpoints. The process yields a diverse set of sensor-robot configurations, which are subsequently ranked based on the information gain of the viewpoint and manipulability index of the corresponding robot configuration to jointly determine the next-best-configuration. To further speed up run-time lookup, a database containing high-manipulability robot configurations is pre-built and integrated into EIS. As a result, MA-NBCP can efficiently carry out autonomous collision-free exploration of unknown environments. Thorough simulation and real hardware (over a 7-DoF manipulator equipped with a depth camera) in highly confined settings demonstrate that MA-NBCP has significant advantages over the current SOTA approaches in terms of exploration time and distance travelled in the joint space (specifically, 56% and 63% better on average, respectively), as well as the mean manipulability index of intermediate configurations at exploration iterations (79% higher on average).

中文翻译：

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高自由度机械臂的可操纵性增强次佳配置探索规划器

这封信介绍了 MA-NBCP，这是一种针对高自由度机械臂自主探索和检查的新型分层框架。 MA-NBCP 迭代选择可操作性增强用于探索操纵器周围未知区域的次佳配置，同时提供避免碰撞的保证。为了开发 MA-NBCP，首先构建了一个高效的勘探信息系统 (EIS)，该系统动态维护关键的、可扩展的信息，以促进勘探规划过程。利用 EIS，更高级别的 MA-NBCP 根据角度加权指标选择最佳勘探子区域。在较低级别，基于斐波那契网格的球形均匀采样策略生成许多候选观点。该过程产生了一组不同的传感器机器人配置，随后根据相应机器人配置的视点信息增益和可操作性指数对其进行排序，以共同确定下一个最佳配置。为了进一步加快运行时查找速度，预先构建了一个包含高可操作性机器人配置的数据库并将其集成到 EIS 中。因此，MA-NBCP可以高效地对未知环境进行自主无碰撞探索。在高度受限的环境中进行的全面仿真和真实硬件（通过配备深度相机的 7 自由度机械臂）表明，MA-NBCP 在探索时间和在关节空间中移动的距离方面比当前的 SOTA 方法具有显着优势（具体来说，平均分别提高了 56% 和 63%），以及探索迭代时中间配置的平均可操作性指数（平均提高了 79%）。