Scalable Gas Sensing, Mapping, and Path Planning via Decentralized Hilbert Maps.
This paper develops a decentralized approach to gas distribution mapping (GDM) and information-driven path planning for large-scale distributed sensing systems. Gas mapping is performed using a probabilistic representation known as a Hilbert map, which formulates the mapping problem as a multi-class classification task and uses kernel logistic regression to train a discriminative classifier online. A novel Hilbert map information fusion method is presented for rapidly merging the information from individual robot maps using limited data communication. A communication strategy that implements data fusion among many robots is also presented for the decentralized computation of GDMs. New entropy-based information-driven path-planning methods are developed and compared to existing approaches, such as particle swarm optimization (PSO) and random walks (RW). Numerical experiments conducted in simulated indoor and outdoor environments show that the information-driven approaches proposed in this paper far outperform other approaches, and avoid mutual collisions in real time.
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Related Subject Headings
- Analytical Chemistry
- 4606 Distributed computing and systems software
- 4104 Environmental management
- 4009 Electronics, sensors and digital hardware
- 4008 Electrical engineering
- 3103 Ecology
- 0906 Electrical and Electronic Engineering
- 0805 Distributed Computing
- 0602 Ecology
- 0502 Environmental Science and Management
Citation
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Analytical Chemistry
- 4606 Distributed computing and systems software
- 4104 Environmental management
- 4009 Electronics, sensors and digital hardware
- 4008 Electrical engineering
- 3103 Ecology
- 0906 Electrical and Electronic Engineering
- 0805 Distributed Computing
- 0602 Ecology
- 0502 Environmental Science and Management