Avoiding collisions is the core problem in multi-agent navigation. In decentralized settings, when agents have limited communication and sensory capabilities, collisions are typically avoided in a reactive fashion, relying on local observations/communications. Prominent collision avoidance techniques, e.g. ORCA, are computationally efficient and scale well to a large number of agents. However, in numerous scenarios, involving navigation through the tight passages or confined spaces, deadlocks are likely to occur due to the egoistic behaviour of the agents and as a result, the latter can not achieve their goals. To this end, we suggest an application of the locally confined multi-agent path finding (MAPF) solvers that coordinate sub-groups of the agents that appear to be in a deadlock (to detect the latter we suggest a simple, yet efficient ad-hoc routine). We present a way to build a grid-based MAPF instance, typically required by modern MAPF solvers. We evaluate two of them in our experiments, i.e. Push and Rotate and a bounded-suboptimal version of Conflict Based Search (ECBS), and show that their inclusion into the navigation pipeline significantly increases the success rate, from 15% to 99% in certain cases.

中文翻译：

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基于相互碰撞避免和局部约束多智能体寻路的分布式多智能体导航

避免碰撞是多智能体导航的核心问题。在去中心化环境中，当代理的通信和感知能力有限时，通常会以反应方式避免碰撞，依赖于本地观察/通信。突出的碰撞避免技术，例如ORCA，在计算上是高效的并且可以很好地扩展到大量代理。然而，在许多场景中，涉及通过狭窄通道或密闭空间的导航，由于代理的利己行为，可能会发生僵局，从而导致后者无法实现其目标。为此，我们建议应用局部受限的多智能体路径寻找 (MAPF) 求解器，以协调似乎处于死锁状态的智能体的子组（为了检测后者，我们建议使用简单的、高效的临时例程）。我们提出了一种构建基于网格的 MAPF 实例的方法，现代 MAPF 求解器通常需要这种方法。我们在我们的实验中评估了其中的两个，即推送和旋转以及基于冲突的搜索 (ECBS) 的有界次优版本，并表明将它们包含在导航管道中显着提高了成功率，在某些情况下从 15% 到 99%案件。