论文标题
为多个机器人计算高质量的混乱解决方案
Computing High-Quality Clutter Removal Solutions for Multiple Robots
论文作者
论文摘要
我们研究了从工作区中清除混乱的任务和运动计划问题，其中多个机器人的入口/出口访问有限。我们称问题为多机器人混乱（MRCR）。针对运动计划非平凡但不是瓶颈的实用应用，我们专注于为可行的MRCR实例找到高质量的解决方案，这取决于有效计算高质量对象去除序列的能力。尽管有具有挑战性的多机器人设置，但我们提出的基于*，动态编程和最佳启发式方法的搜索算法都为数十个对象生成了明显优于单个机器人解决方案的对象的解决方案。使用多个Kuka Youbots的现实模拟进一步证实了我们算法解决方案的有效性。相比之下，我们还表明，确定MRCR的最佳对象去除序列在计算上是棘手的。
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.
