Parasuraman Ramviyas, Fabry Thomas, Molinari Luca, Kershaw Keith, Di Castro Mario, Masi Alessandro, Ferre Manuel
European Organization for Nuclear Research (CERN), Geneva 1211, Switzerland.
CAR UPM-CSIC, Universidad Politécnica de Madrid, Madrid 28006, Spain.
Sensors (Basel). 2014 Dec 12;14(12):23970-4003. doi: 10.3390/s141223970.
The reliability of wireless communication in a network of mobile wireless robot nodes depends on the received radio signal strength (RSS). When the robot nodes are deployed in hostile environments with ionizing radiations (such as in some scientific facilities), there is a possibility that some electronic components may fail randomly (due to radiation effects), which causes problems in wireless connectivity. The objective of this paper is to maximize robot mission capabilities by maximizing the wireless network capacity and to reduce the risk of communication failure. Thus, in this paper, we consider a multi-node wireless tethering structure called the "server-relay-client" framework that uses (multiple) relay nodes in between a server and a client node. We propose a robust stochastic optimization (RSO) algorithm using a multi-sensor-based RSS sampling method at the relay nodes to efficiently improve and balance the RSS between the source and client nodes to improve the network capacity and to provide redundant networking abilities. We use pre-processing techniques, such as exponential moving averaging and spatial averaging filters on the RSS data for smoothing. We apply a receiver spatial diversity concept and employ a position controller on the relay node using a stochastic gradient ascent method for self-positioning the relay node to achieve the RSS balancing task. The effectiveness of the proposed solution is validated by extensive simulations and field experiments in CERN facilities. For the field trials, we used a youBot mobile robot platform as the relay node, and two stand-alone Raspberry Pi computers as the client and server nodes. The algorithm has been proven to be robust to noise in the radio signals and to work effectively even under non-line-of-sight conditions.
移动无线机器人节点网络中无线通信的可靠性取决于接收到的无线电信号强度（RSS）。当机器人节点部署在存在电离辐射的恶劣环境中时（例如在一些科学设施中），某些电子组件可能会随机发生故障（由于辐射效应），这会导致无线连接出现问题。本文的目标是通过最大化无线网络容量来最大化机器人的任务能力，并降低通信失败的风险。因此，在本文中，我们考虑一种称为“服务器 - 中继 - 客户端”框架的多节点无线 tethering 结构，该结构在服务器和客户端节点之间使用（多个）中继节点。我们提出了一种鲁棒随机优化（RSO）算法，该算法在中继节点处使用基于多传感器的 RSS 采样方法，以有效地改善和平衡源节点与客户端节点之间的 RSS，从而提高网络容量并提供冗余网络能力。我们对 RSS 数据使用预处理技术，如指数移动平均和空间平均滤波器进行平滑处理。我们应用接收端空间分集概念，并在中继节点上使用随机梯度上升方法的位置控制器来对中继节点进行自定位，以完成 RSS 平衡任务。通过在欧洲核子研究组织（CERN）设施中进行的广泛模拟和现场实验，验证了所提出解决方案的有效性。对于现场试验，我们使用 youBot 移动机器人平台作为中继节点，两台独立的 Raspberry Pi 计算机作为客户端和服务器节点。该算法已被证明对无线电信号中的噪声具有鲁棒性，并且即使在非视距条件下也能有效工作。
