RSS-based Robot Localization in Critical Environments using Reservoir Computing

Abstract

Supporting both accurate and reliable localization in critical environments is key to increasing the potential of logistic mobile robots. This paper presents a system for indoor robot localization based on Reservoir Computing from noisy radio signal strength index (RSSI) data generated by a network of sensors. The proposed approach is assessed under different conditions in a real-world hospital environment. Experimental results show that the resulting system represents a good trade-off between localization performance and deployment complexity, with the ability to recover from cases in which permanent changes in the environment affect its generalization performance.