Abstract
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.
Highlights
Networks of mobile wireless robots are increasingly considered in applications in hazardous environments where humans cannot perform some tasks because of safety issues and challenges in the environments [1,2]
The proposed method is novel in the following way compared to the state of the art: no prior knowledge or model of the radio environment is required; a combination of spatial and temporal pre-processing techniques is used to deal with the measurement noise and stochastic nature of the radio signal strength (RSS) and to mitigate fading effects; multiple receivers spatially distributed on-board the relay node are used instead of using multiple antennas connected to the same receiver, avoiding the need to switch between different antennas connected to the same receiver and making the algorithm applicable directly at the application layer
The RSS can be measured directly in most of the commercial wireless devices using a metric called the radio signal strength indicator (RSSI) (even though the RSSI metric may report different measurements, such as the absolute signal power, a relative signal level, the signal-to-noise ratio (SNR) or the bit error rate (BER) (%), due to its vendor-specific nature, the wireless devices used in this paper report absolute signal power in RSSI) [31]; this is not the case for SNR
Summary
Networks of mobile wireless robots are increasingly considered in applications in hazardous environments where humans cannot perform some tasks because of safety issues and challenges in the environments [1,2]. (3) the robot shall in no way interrupt the operation or damage any objects in the facilities where it is deployed To meet these requirements and, to improve wireless communication performance in non-line-of-sight (NLOS) conditions and underground tunnels, the concept of wireless tethering using intermediate relay nodes (forming a network of mobile robotic nodes) has been suggested by previous researchers [9,10,11,12,13]. We extend our previous work and propose an adaptive robust stochastic optimization (RSO) algorithm for autonomous wireless relay positioning using a spatially spread RSS sensing technique to dynamically optimize the RSS assuming an unknown, noisy radio environment. We conclude the paper and summarize the key values and advantages of the proposed method
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