Manipulability-Augmented Next-Best-Configuration Exploration Planner for High-DoF Manipulators

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).