Predictive Modeling of Time-Varying Environmental Information for Path Planning

Efficient environmental monitoring is an important application area of wireless sensor networks or unmanned ground/flying robots. In this paper, predictive modeling of environmental information and its usage for informative path planning is considered. In order to represent information gathered by observations, we use radial basis function (RBF) based approximation with Kalman filter. Also, we employ recurrent neural networks (RNNs) to include dynamic inference of the information process, and train it with echo state network (ESN) approach. Moreover, we introduce a technical approach to informative path planning using the predictive information model. A hybrid decision policy is employed as an illustrative method for reducing overall variance of the model prediction and both a simulation and an experiment are performed to validate the proposed framework.