Distributed Environmental Modeling and Adaptive Sampling for Multi-Robot Sensor Coverage

Abstract

We consider the problem of online distributed environmental modeling and adaptive sampling for multi-robot sensor coverage, where a team of robots spread out over the workspace in order to optimize the sensing performance over environmental phenomena, whose distribution is often referred to as a density function. Unlike most existing works that either assume certain knowledge of the density function beforehand or centrally learn the density function assuming global knowledge of collected data from all the robots, we propose a fully distributed adaptive sampling approach to allow robots to efficiently learn the unknown density function online. In particular, we developed adaptive coverage controllers based on the learned density functions for minimizing the sensing cost. To capture significantly different components of the environmental phenomenon with only locally collected data for each robot when global knowledge is not available, we propose a distributed mixture of Gaussian Processes algorithm that enables robots to collaboratively learn the global density function by exchanging only model-related parameters. We empirically demonstrate the effectiveness of our algorithm via evaluation on real-world data gathered from agricultural field robot and indoor static sensors.