We consider the problem of ferrying data between nodes of a sparsely distributed sensing network of Unattended Ground Sensors (UGS) with endurance-constrained Unmanned Aerial Systems (UAS). The sensing domain wherein the sparsely distributed UGS network is deployed is assumed to be highly nonstationary (time-varying) and noisy. This makes the data-ferrying problem very complicated as the expected value-of-information at a sensing location can rapidly change. To address this issue, we present a new class of data ferrying and persistent exploration algorithms termed Exploitation by Informed Exploration between Isolated Operatives (EIEIO), and show that with several reasonable assumptions and a model on the predicted accumulation of value-of-information, the problem can be simpli ed to a mathematical linear program. To solve the linear program, the UAS learns to anticipate regions in the sensing domain that have the highest degree of change. The degree of change, is learned using a novel implementation of a Cox Process called the Cox-Gaussian Process (CGP). Our approach does not require a priori knowledge of the sensing domain model to arrive at an optimal UAS allocation strategy.
Video Descriptions: The videos depict that available Kullback-Leibler (KL) divergence in the Intel Berkeley Data Set in green, the modeled KL divergence in blue, and the green circles turn red when they are selected in a particular episode.
Left Video: This video shows the performance of sequentially sampling the sensing locations in batches of 6.
Right Video: This video shows the performance of the Real-time Adaptive Prediction of Time-varying and Obscure Rewards (RAPTOR) sampling algorithm, where locations are selected in batches of 6.
