D2CoPlan: マルチロボット被覆計画のための微分可能な分散プランナ
D2CoPlan: A Differentiable Decentralized Planner for Multi-Robot Coverage
マルチロボットの被覆計画を、学習ベースの微分可能な分散プランナで解き、エージェント数に対してスケーラブルに動作し、他の学習手法と組み合わせてエンドツーエンド学習も可能にした研究。
著者: Vishnu Dutt Sharma, Lifeng Zhou, Pratap Tokekar
分類: cs.RO
原文アブストラクト
Centralized approaches for multi-robot coverage planning problems suffer from the lack of scalability. Learning-based distributed algorithms provide a scalable avenue in addition to bringing data-oriented feature generation capabilities to the table, allowing integration with other learning-based approaches. To this end, we present a learning-based, differentiable distributed coverage planner (D2COPL A N) which scales efficiently in runtime and number of agents compared to the expert algorithm, and performs on par with the classical distributed algorithm. In addition, we show that D2COPlan can be seamlessly combined with other learning methods to learn end-to-end, resulting in a better solution than the individually trained modules, opening doors to further research for tasks that remain elusive with classical methods.
