D2M2N：面向多机器人的去中心化可微分记忆使能建图与导航
D2M2N: Decentralized Differentiable Memory-Enabled Mapping and Navigation for Multiple Robots
AI总结：
针对现有多机器人导航模型缺乏记忆、难以利用历史观测的问题，本文提出去中心化可微分记忆架构D2M2N，通过紧凑环境表示与价值迭代网络实现复杂建图与导航，并在实验中显著超越现有最优模型。
摘要：
近年来，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，仅依赖机器人当前的观测来规划动作，无法利用过去的观测来规划更优路径，尤其是在复杂环境中。在本工作中，我们提出了一种完全可微分且去中心化的记忆使能多机器人导航与建图架构，称为D2M2N。D2M2N维护环境的紧凑表示以记住过去的观测，并使用价值迭代网络进行复杂导航。我们进行了大量实验，表明D2M2N在复杂建图与导航任务中显著优于最先进的模型。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
