论文标题
分散的分布式专家辅助学习（D2EAL）用于合作目标跟踪的方法
Decentralized Distributed Expert Assisted Learning (D2EAL) approach for cooperative target-tracking
论文作者
论文摘要
本文解决了使用异质的多机器人系统进行合作目标跟踪的问题，该系统在该系统上通过动态通信网络进行通信，而异质性是在机器人中安装的不同类型的传感器和预测算法方面。这个问题被投入到分布式学习框架中，在该框架中，机器人被认为是通过动态通信网络连接的“代理”。他们的预测算法被认为是“专家”，对目标轨迹进行了审查的预测。在本文中，提出了一种新型分散化分布式专家辅助学习（D2EAL）算法，通过使每个机器人能够通过其信息共享来改善目标轨迹的外观预测，从而改善了整体跟踪性能，并通过其加权信息融合与基于预测损失的在线学习相结合的信息融合。对D2EAL进行了理论分析，其中涉及对累积预测损失的最坏情况界限的分析，以及权重分析。仿真研究表明，在涉及专家预测中较大动态偏见或漂移的不利场景中，D2EAL优于众所周知的基于协方差的估计/预测融合方法，无论是在预测性能和可伸缩性方面。
This paper addresses the problem of cooperative target tracking using a heterogeneous multi-robot system, where the robots are communicating over a dynamic communication network, and heterogeneity is in terms of different types of sensors and prediction algorithms installed in the robots. The problem is cast into a distributed learning framework, where robots are considered as 'agents' connected over a dynamic communication network. Their prediction algorithms are considered as 'experts' giving their look-ahead predictions of the target's trajectory. In this paper, a novel Decentralized Distributed Expert-Assisted Learning (D2EAL) algorithm is proposed, which improves the overall tracking performance by enabling each robot to improve its look-ahead prediction of the target's trajectory by its information sharing, and running a weighted information fusion process combined with online learning of weights based on a prediction loss metric. Theoretical analysis of D2EAL is carried out, which involves the analysis of worst-case bounds on cumulative prediction loss, and weights convergence analysis. Simulation studies show that in adverse scenarios involving large dynamic bias or drift in the expert predictions, D2EAL outperforms well-known covariance-based estimate/prediction fusion methods, both in terms of prediction performance and scalability.
