Hierarchical planning for resource-constrained long-term monitoring missions in time-varying environments

We consider autonomous robots deployed on long-term monitoring missions in unknown environments. The planning objective is to maximise the value of observations obtained over the course of a mission, subject to resource constraints which demand periodic visits to depots where resources can be replen...

Full description

Bibliographic Details
Main Authors: Stephens, R, Lacerda, B, Hawes, N
Format: Conference item
Language:English
Published: IOS Press 2024
Description
Summary:We consider autonomous robots deployed on long-term monitoring missions in unknown environments. The planning objective is to maximise the value of observations obtained over the course of a mission, subject to resource constraints which demand periodic visits to depots where resources can be replenished. Effective planning in this setting requires reasoning over long horizons based on sparse observational data, and flexible management of the constrained resources. We present a hierarchical planning approach to this problem, using a spatiotemporal Gaussian process environment model at different levels of abstraction for short- and long-horizon planning. We empirically evaluate our approach on a series of synthetic domains, and a wildfire monitoring scenario based on real data.