一种分布式主动感知源搜索和电平曲线跟踪策略
A Distributed Active Perception Strategy for Source Seeking and Level Curve Tracking
摘要：
用于多代理系统定位源或遵循空间分布标量场的期望水平曲线的算法通常需要在代理之间共享场测量以用于梯度估计。然而，在本文中，我们提出了一种分布式主动感知策略，使不同大小和图形结构的群体执行源搜索和水平曲线跟踪，而不需要明确地估计场梯度或明确地共享测量。所提出的方法利用了一个共识式的主成分分析感知算法，不需要显式的通信，以计算一个局部的身体帧。这个身体框架被用来设计一个分布式控制法律，其中每个代理调制其运动的基础上，只有其瞬时场测量。几个稳定性的结果内得到的奇异摄动框架，证明了该战略的收敛性和鲁棒性。此外，效率通过各种计算机模拟和机器人实施$2$ -D标量场进行验证。主动感知策略利用了可用的本地信息，并有可能被用于各种应用，如生物和机器人群体中的信息传播建模。
英文摘要：
Algorithms for multi-agent systems to locate a source or to follow a desired level curve of spatially distributed scalar fields generally require sharing field measurements among the agents for gradient estimation. Yet, in this paper, we propose a distributed active perception strategy that enables swarms of various sizes and graph structures to perform source seeking and level curve tracking without the need to explicitly estimate the field gradient or explicitly share measurements. The proposed method utilizes a consensus-like Principal Component Analysis perception algorithm that does not require explicit communication in order to compute a local body frame. This body frame is used to design a distributed control law where each agent modulates its motion based only on its instantaneous field measurement. Several stability results are obtained within a singular perturbation framework which justifies the convergence and robustness of the strategy. Additionally, efficiency is validated through various computer simulations and robots implementation in $2$-D scalar fields. The active perception strategy leverages the available local information and has the potential to be used in various applications such as modeling information propagation in biological and robotic swarms.
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