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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/19/7/1524 |
| _version_ | 1828115567343566848 |
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| author | Pingping Zhu Silvia Ferrari Julian Morelli Richard Linares Bryce Doerr |
| author_facet | Pingping Zhu Silvia Ferrari Julian Morelli Richard Linares Bryce Doerr |
| author_sort | Pingping Zhu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T12:42:28Z |
| format | Article |
| id | doaj.art-c8ece7b8571949cc80beec239a264369 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T12:42:28Z |
| publishDate | 2019-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c8ece7b8571949cc80beec239a2643692022-12-22T04:23:28ZengMDPI AGSensors1424-82202019-03-01197152410.3390/s19071524s19071524Scalable Gas Sensing, Mapping, and Path Planning via Decentralized Hilbert MapsPingping Zhu0Silvia Ferrari1Julian Morelli2Richard Linares3Bryce Doerr4Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853, USASibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853, USASibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853, USADepartment of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA 02139, USADepartment of Aerospace Engineering and Mechanics, University of Minnesota, Minneapolis, MN 55455, USAThis 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.https://www.mdpi.com/1424-8220/19/7/1524gas sensingmulti-agent systemsvery large-scale robotic systemsinformation theory |
| spellingShingle | Pingping Zhu Silvia Ferrari Julian Morelli Richard Linares Bryce Doerr Scalable Gas Sensing, Mapping, and Path Planning via Decentralized Hilbert Maps Sensors gas sensing multi-agent systems very large-scale robotic systems information theory |
| title | Scalable Gas Sensing, Mapping, and Path Planning via Decentralized Hilbert Maps |
| title_full | Scalable Gas Sensing, Mapping, and Path Planning via Decentralized Hilbert Maps |
| title_fullStr | Scalable Gas Sensing, Mapping, and Path Planning via Decentralized Hilbert Maps |
| title_full_unstemmed | Scalable Gas Sensing, Mapping, and Path Planning via Decentralized Hilbert Maps |
| title_short | Scalable Gas Sensing, Mapping, and Path Planning via Decentralized Hilbert Maps |
| title_sort | scalable gas sensing mapping and path planning via decentralized hilbert maps |
| topic | gas sensing multi-agent systems very large-scale robotic systems information theory |
| url | https://www.mdpi.com/1424-8220/19/7/1524 |
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