This paper studies the cooperative source seeking problem via a networked multi-vehicle system. In contrast to existing literature, the multi-vehicle system is controlled to the source position that maximizes aggregated multiple unknown scalar fields and each sensor-enabled vehicle only samples measurements of one scalar field. Thus, a single vehicle is unable to localize the source and has to cooperate with its neighboring vehicles. By jointly exploiting the ideas of the consensus algorithm and the stochastic extremum seeking (ES), this paper proposes novel distributed stochastic ES controllers, which are gradient-free and do not need any absolute information, such that the multi-vehicle system simultaneously approaches the source position. The effectiveness of the proposed controllers is proved for quadratic scalar fields. Finally, illustrative examples are included to validate the theoretical results.

本文通过网络化的多车系统研究协同寻源问题。与现有文献相反，将多车辆系统控制在源位置，该源位置最大化了聚合的多个未知标量场，并且每个启用传感器的车辆仅对一个标量场。因此，单个车辆无法定位源并且必须与其相邻车辆合作。通过共同利用共识算法和随机极值搜索（ES）的思想，本文提出了新颖的分布式随机ES控制器，该控制器无梯度且不需要任何绝对信息，因此多车系统同时接近源位置。证明了所提控制器对于二次标量场的有效性。最后，包括说明性实例以验证理论结果。