通过消息效用估计降低分散式多机器人团队的网络负载
Reducing Network Load via Message Utility Estimation for Decentralized Multirobot Teams
AI总结：
本文针对分散式多机器人团队在资源受限通信下的藻类浓度分位数估计问题，提出基于消息效用估计的在线决策方法，仅传输最有用的消息，在降低42%网络负载的同时将估计误差降低1.84%。
摘要：
我们的研究动机是对湖泊中藻类浓度进行分位数估计，以及分散式多机器人团队如何有效解决这一问题。我们发现，与单个机器人相比，多机器人团队在该任务中能提升性能，而具备通信能力的团队又进一步优于缺乏通信的团队；然而，真实机器人资源受限，通信网络无法支持任意消息负载，这使得朴素、持续的信息共享以及复杂的建模和决策都不可行。考虑到这一点，我们提出了在线的、可本地计算的指标，用于确定向其他团队成员发送给定消息的效用，并提出一种决策论方法，仅选择发送最有用的消息，同时采用分散且独立的框架来维护对其他队友的信念。我们在真实世界水生数据集上通过仿真验证了我们的方法，并表明通过一种基于消息对未来队友行为预期影响的效用估计方法来限制通信，可使网络负载降低42%，同时将分位数估计误差降低1.84%。
英文摘要：
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%.
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。
