Recently, a number of learning-based models have been proposed for multi-robot navigation. However, these models lack memory and only rely on the current observations of the robot to plan their actions. They are unable to leverage past observations to plan better paths, especially in complex environments. In this work, we propose a fully differentiable and decentralized memory-enabled architecture for multi-robot navigation and mapping called D2M2N. D2M2N maintains a compact representation of the environment to remember past observations and uses Value Iteration Network for complex navigation. We conduct extensive experiments to show that D2M2N significantly outperforms the state-of-the-art model in complex mapping and navigation task.

翻译：近期，已有多种基于学习的模型被提出用于多机器人导航。然而，这些模型缺乏记忆能力，仅依赖机器人当前观测规划行动，无法利用历史观测优化路径，尤其在复杂环境中表现受限。本文提出一种名为D2M2N的全可微分去中心化记忆赋能多机器人建图与导航架构。该架构通过维护环境的紧凑表征来记忆历史观测，并采用值迭代网络处理复杂导航任务。大量实验表明，D2M2N在复杂建图与导航任务中显著优于当前最优模型。