Unified Adaptive and Cooperative Planning Using Multi-Task Coregionalized Gaussian Processes

Abstract

For robots tasked with surveying the temporal dynamics of a changing environment, a choice must be made to observe novel regions of the environment or to re-survey previously visited regions, which may have changed. We present a novel multi-robot informative path planner (IPP) that combines an environmental and task kernel to direct mobile robots to gather samples from regions that would result in the greatest expected improvement in map accuracy. Our planner utilizes a multi-output Gaussian process to unify priors about the spatiotemporal environment along with priors about observational correlations between sensing vehicles. Additionally, we extend our analysis into an adaptive planning scenario and examine the performance under different planning configurations. We find that planning performance is largely driven by the choice of environmental priors, and that unrepresentative priors can be improved through adaptive planning.