Computing High-Quality Clutter Removal Solutions for Multiple Robots
This addresses the practical problem of efficiently clearing cluttered workspaces with limited access for multiple robots, though it is incremental as it builds on existing task and motion planning methods.
The paper tackles the multi-robot clutter removal (MRCR) problem by developing search algorithms to compute high-quality object removal sequences, resulting in solutions for tens of objects that significantly outperform single-robot approaches in realistic simulations.
We investigate the task and motion planning problem of clearing clutter from a workspace with limited ingress/egress access for multiple robots. We call the problem multi-robot clutter removal (MRCR). Targeting practical applications where motion planning is non-trivial but is not a bottleneck, we focus on finding high-quality solutions for feasible MRCR instances, which depends on the ability to efficiently compute high-quality object removal sequences. Despite the challenging multi-robot setting, our proposed search algorithms based on A*, dynamic programming, and best-first heuristics all produce solutions for tens of objects that significantly outperform single robot solutions. Realistic simulations with multiple Kuka youBots further confirms the effectiveness of our algorithmic solutions. In contrast, we also show that deciding the optimal object removal sequence for MRCR is computationally intractable.