Name
Title | ||
|---|---|---|
Multi-robot informative path planning for active sensing of environmental phenomena: a tale of two algorithms |
Abstract | ||
|---|---|---|
A key problem of robotic environmental sensing and monitoring is that of active sensing: How can a team of robots plan the most informative observation paths to minimize the uncertainty in modeling and predicting an environmental phenomenon? This paper presents two principled approaches to efficient information-theoretic path planning based on entropy and mutual information criteria for in situ active sensing of an important broad class of widely-occurring environmental phenomena called anisotropic fields. Our proposed algorithms are novel in addressing a trade-off between active sensing performance and time efficiency. An important practical consequence is that our algorithms can exploit the spatial correlation structure of Gaussian process-based anisotropic fields to improve time efficiency while preserving near-optimal active sensing performance. We analyze the time complexity of our algorithms and prove analytically that they scale better than state-of-the-art algorithms with increasing planning horizon length. We provide theoretical guarantees on the active sensing performance of our algorithms for a class of exploration tasks called transect sampling, which, in particular, can be improved with longer planning time and/or lower spatial correlation along the transect. Empirical evaluation on real-world anisotropic field data shows that our algorithms can perform better or at least as well as the state-of-the-art algorithms while often incurring a few orders of magnitude less computational time, even when the field conditions are less favorable. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.5555/2484920.2484926 | adaptive agents and multi-agents systems |
Keywords | DocType | Volume |
multi-robot informative path planning,planning horizon length,computational time,time efficiency,real-world anisotropic field data,state-of-the-art algorithm,environmental phenomenon,field condition,longer planning time,anisotropic field,time complexity,gaussian process,active learning | Conference | abs/1302.0723 |
Citations | PageRank | References |
29 | 1.12 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nannan Cao | 1 | 48 | 2.78 |
| Kian Hsiang Low | 2 | 437 | 32.78 |
| John Dolan | 3 | 977 | 92.41 |