Multi-agent navigation in restricted environments presents significant challenges, since agents need to move to their goals in an efficient manner while avoiding collisions with both static and dynamic obstacles. Previously, C-Nav was proposed as a method that can effectively coordinate groups of agents in very restricted environments. In this work, we propose and evaluate three alternative communications models for C-Nav, that intend to provide more flexibility to the original method. The results of our experiments show that each of our proposed methods can lead to significant improvements over C-Nav in specific types of restricted environments.

中文翻译：

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复杂环境下协同导航的多智能体通信模型

受限环境中的多智能体导航提出了重大挑战，因为智能体需要以有效的方式移动到他们的目标，同时避免与静态和动态障碍物发生碰撞。以前，C-Nav 被提议作为一种可以在非常受限的环境中有效协调代理组的方法。在这项工作中，我们为 C-Nav 提出并评估了三种替代通信模型，旨在为原始方法提供更大的灵活性。我们的实验结果表明，在特定类型的受限环境中，我们提出的每种方法都可以显着改善 C-Nav。