Object-Reconstruction-Aware Whole-body Control of Mobile Manipulators

Object reconstruction and inspection tasks play a crucial role in various robotics applications. Identifying paths that reveal the most unknown areas of the object becomes paramount in this context, as it directly affects efficiency, and this problem is known as the view path planning problem. Current methods often use sampling-based path planning techniques, evaluating potential views along the path to enhance reconstruction performance. However, these methods are computationally expensive as they require evaluating several candidate views on the path. To this end, we propose a computationally efficient solution that relies on calculating a focus point in the most informative (unknown) region and having the robot maintain this point in the camera field of view along the path. We incorporated this strategy into the whole-body control of a mobile manipulator employing a visibility constraint without the need for an additional path planner. We conducted comprehensive and realistic simulations using a large dataset of 114 diverse objects of varying sizes from 57 categories to compare our method with a sampling-based planning strategy using Bayesian data analysis. Furthermore, we performed real-world experiments with an 8-DoF mobile manipulator to demonstrate the proposed method's performance in practice. Our results suggest that there is no significant difference in object coverage and entropy. In contrast, our method is approximately nine times faster than the baseline sampling-based method in terms of the average time the robot spends between views.