D2M2N: 複数ロボットのための分散型微分可能メモリ活用マッピングとナビゲーション
D2M2N: Decentralized Differentiable Memory-Enabled Mapping and Navigation for Multiple Robots
過去の観測を記憶する微分可能な分散型アーキテクチャを提案し、複数ロボットの複雑なマッピングとナビゲーションで従来手法を大きく上回る性能を示した。
著者: Md Ishat-E-Rabban, Pratap Tokekar
分類: cs.RO
原文アブストラクト
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.
