Taming Combinatorial Challenges in Optimal Clutter Removal Tasks
This addresses a specific problem in robotics for efficient clutter clearing, presenting incremental algorithmic improvements.
The paper tackles the combinatorial challenge of computing an optimal object removal sequence for a mobile robot to minimize task completion time in clutter removal, establishing NP-hardness and developing algorithms that scale to 40 objects with dynamic programming and show greedy methods perform comparably in random clutter.
We examine an important combinatorial challenge in clearing clutter using a mobile robot equipped with a manipulator, seeking to compute an optimal object removal sequence for minimizing the task completion time, assuming that each object is grasped once and then subsequently removed. On the structural side, we establish that such an optimal sequence can be NP-hard to compute, even when no two objects to be removed have any overlap. Then, we construct asymptotically optimal and heuristic algorithms for clutter removal. Employing dynamic programming, our optimal algorithm scales to 40 objects. On the other hand, for random clutter, fast greedy algorithms tend to produce solutions comparable to these generated by the optimal algorithm.