EKF-based Adaptive Sampling with Mobile Robotic Sensor Nodes

Abstract

The use of robotics in environmental monitoring applications requires distributed sensor systems optimized for effective estimation of relevant models subject to energy and environmental constraints. The mobile robot nodes are agents facilitating the repositioning of sensors in order to estimate a field distribution. This field distribution could be, for instance, water salinity in a lake, or air pollution over an industrial area. Each mobile robot node is characterized by sensor measurement noise in addition to localization uncertainty. This paper addresses an important problem for the robotic deployment of sensor networks, namely adaptive sampling (AS) by selection and repositioning of nodes in order to optimally estimate the parameters of distributed variable field models. The AS problem is posed as a sensor fusion problem within the extended Kalman filter (EKF) framework. We present simulation and experimental results of 2D deployment scenarios using low-cost mobile sensor robots developed in our lab