Spatial wireless connectivity prediction for mobile robots

Abstract: Mobile robots, either autonomous or tele-operated have the potential of assisting humans in various situations such as during natural disasters, Urban Search and Rescue (USAR) efforts, and in Explosive Ordinance Disposal (EOD). These robots need steady wireless connectivity with their base station for control and communication. On one hand, the wireless link has to be managed to maintain a stable high quality network connection. On other hand, wireless connection should be continuously monitored to foresee network failure or inadequate link quality situations caused by entering access with low signal strength. This thesis focus on the later where we aim to address the prediction of wireless network connectivity for mobile robots. To indicate wireless connection quality, we use the Radio Signal Strength (RSS) parameter which is readily available by most wireless devices, and it has been frequently used in the literature to indicate wireless connection quality as the RSS have direct relation to the network throughput. Thus the focus of this thesis is to predict the RSS in future robot positions with reference to the current position of the robot. The solution is not straight forward because of the challenging nature of the radio signal propagation which involves complex phenomena such as path loss, shadowing and multipath fading. The RSS prediction method designed in this thesis has two stages. In the first stage, we estimate the location of radio signal source using an RSS gradient-based approach that can work in both single and multiple receivers arrangements. This information will be applied in the next prediction stage. For RSS prediction, we make use of Gaussian Process Regression (GPR) due to non-parametric nature, robustness to noise in the RSS data and changes in the environment. We validate our design with extensive experiments conducted using different types of mobile robots and wireless devices in indoor and outdoor environments, and under line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. We are able to achieve results with source localization error of up to 2 meters for indoor and 5 meters for outdoor environment. In terms of RSS prediction, we obtain the mean absolute prediction error of less than 5 dBm on average, for prediction within 5 meters in indoor environment and 20 meters in outdoor environment. The work is not only promising in terms of prediction time and accuracy but also outperform the state-of-the-art (SOTA) methods including the GPR algorithm, the Kriging interpolation method and the linear regression approaches.