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

Conference item

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.

Authors: Stephens, R; Lacerda, B; Hawes, N

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IOS Press
• Host title: ECAI 2024
• Pages: 1214-1221
• Series: Frontiers in Artificial Intelligence and Applications
• Series number: 392
• Place of publication: Amsterdam
• Publication date: 2024-10-16
• Acceptance date: 2024-07-04
• Event title: 27th European Conference on Artificial Intelligence (ECAI 2024)
• Event location: Santiago de Compostela, Spain
• Event website: https://www.ecai2024.eu/
• Event start date: 2024-10-19
• Event end date: 2024-10-24
• DOI: 10.3233/FAIA240617
• EISSN: 1879-8314
• ISSN: 0922-6389
• EISBN: 9781643685489
• Language: English