In this paper, we present a method of multi-robot motion planning by biasing centralized, sampling-based tree search with decentralized, data-driven steer and distance heuristics. Over a range of robot and obstacle densities, we evaluate the plain Rapidly-expanding Random Trees (RRT), and variants of our method for double integrator dynamics. We show that whereas plain RRT fails in every instance to plan for $4$ robots, our method can plan for up to 16 robots, corresponding to searching through a very large 65-dimensional space, which validates the effectiveness of data-driven heuristics at combating exponential search space growth. We also find that the heuristic information is complementary; using both heuristics produces search trees with lower failure rates, nodes, and path costs when compared to using each in isolation. These results illustrate the effective decomposition of high-dimensional joint-space motion planning problems into local problems.

翻译：本文提出了一种通过将基于集中式采样的树搜索与分散式数据驱动的导向和距离启发式算法相结合的多机器人运动规划方法。我们针对不同的机器人和障碍物密度，评估了普通快速扩展随机树（RRT）以及我们方法在双积分器动力学下的变体。结果表明，尽管普通RRT在所有情况下都无法为4个机器人规划路径，但我们的方法能够为多达16个机器人进行规划——这相当于在高达65维的巨大空间中进行搜索——从而验证了数据驱动启发式算法在抑制指数级搜索空间增长方面的有效性。我们还发现这些启发式信息具有互补性：与单独使用每种启发式算法相比，同时使用两者可降低搜索树的失败率、节点数量和路径成本。这些结果展示了将高维联合空间运动规划问题有效分解为局部问题的能力。