论文标题
D2COPLAN：多机覆盖的可区分分散计划者
D2CoPlan: A Differentiable Decentralized Planner for Multi-Robot Coverage
论文作者
论文摘要
多机器人覆盖计划问题的集中式方法缺乏可扩展性。基于学习的分布式算法除了将面向数据的特征生成功能带入表格外，还提供了可扩展的途径，从而可以与其他基于学习的方法集成。为此，我们提出了一个基于学习的，可区分的分布式覆盖范围计划（D2COPL A N），该计划者与专家算法相比在运行时和代理数量上有效地扩展，并与经典的分布式算法相同。此外，我们表明D2Coplan可以与其他学习方法无缝地结合到端到端的学习方法，从而提供了比单独训练的模块更好的解决方案，从而为对具有经典方法难以捉摸的任务进行了进一步研究。
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.
