This paper deals with the problem of robust distributed sampling of a field in the presence of unreliable sensors/agents. An algorithm is devised to estimate the maximum of the field over the domain spanned by the agents where some of the sensors can sample wrong measurements over a finite time, higher than the maximum field value. Necessary and sufficient conditions are given to guarantee convergence to the maximum field value and a robust and redundant algorithm design is presented by combining an exhaustive ergodic search with multiagent consensus protocols. In this original setup, the presence of unilateral interactions and exogenous signals is considered, the latter representing the measures sampled by the agents. Representative examples are presented to illustrate the effectiveness of the proposed framework and conditions.

中文翻译：

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场最大值的鲁棒分布式估计

本文讨论了在存在不可靠的传感器/代理的情况下对字段进行鲁棒的分布式采样的问题。设计了一种算法来估计代理所跨越的域中的最大场，其中一些传感器可以在一定时间内对错误的测量值进行采样，高于最大场值。通过将穷举遍历搜索与多主体共识协议相结合，给出了保证收敛到最大字段值的必要和充分条件，并提出了一种健壮和冗余的算法设计。在此原始设置中，考虑了单边交互作用和外源信号的存在，后者表示由代理采样的度量。提出了具有代表性的示例，以说明所提议框架和条件的有效性。