メッセージ効用推定による分散型マルチロボットチームのネットワーク負荷低減
Reducing Network Load via Message Utility Estimation for Decentralized Multirobot Teams
分散型マルチロボットチームで、送信メッセージの効用をオンラインで推定し、有用なものだけを送ることで、湖の藻類濃度の分位点推定精度を保ちつつ通信量を42%削減した。
著者: Isabel M. Rayas Fernández, Christopher E. Denniston, Gaurav S. Sukhatme
分類: cs.RO, cs.MA
原文アブストラクト
We are motivated by quantile estimation of algae concentration in lakes and how decentralized multirobot teams can effectively tackle this problem. We find that multirobot teams improve performance in this task over single robots, and communication-enabled teams further over communication-deprived teams; however, real robots are resource-constrained, and communication networks cannot support arbitrary message loads, making naive, constant information-sharing but also complex modeling and decision-making infeasible. With this in mind, we propose online, locally computable metrics for determining the utility of transmitting a given message to the other team members and a decision-theoretic approach that chooses to transmit only the most useful messages, using a decentralized and independent framework for maintaining beliefs of other teammates. We validate our approach in simulation on a real-world aquatic dataset, and we show that restricting communication via a utility estimation method based on the expected impact of a message on future teammate behavior results in a 42% decrease in network load while simultaneously decreasing quantile estimation error by 1.84%.
