Scalable Gas Sensing, Mapping, and Path Planning via Decentralized Hilbert Maps
Name
sensors-19-01524.pdf
Size
3.4 MB
Format
Adobe PDF
Checksum (MD5)
3b7c949bd871f0685175a2d454673dc7
Author(s)
Zhu, Pingping
Ferrari, Silvia
Morelli, Julian
Linares, Richard
Doerr, Bryce
Date Issued
March 28, 2019
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Sensors 19 (7): 1524 (2019)
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3390/s19071524
