The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
Robots for Environmental Monitoring: Significant Advancements and Applications
Abstract
Robotic systems are increasingly being utilized as fundamental data-gathering tools by scientists, allowing new perspectives and a greater understanding of the planet and its environmental processes. Today's robots are already exploring our deep oceans, tracking harmful algal blooms and pollution spread, monitoring climate variables, and even studying remote volcanoes. This article collates and discusses the significant advancements and applications of marine, terrestrial, and airborne robotic systems developed for environmental monitoring during the last two decades. Emerging research trends for achieving large-scale environmental monitoring are also reviewed, including cooperative robotic teams, robot and wireless sensor network (WSN) interaction, adaptive sampling and model-aided path planning. These trends offer efficient and precise measurement of environmental processes at unprecedented scales that will push the frontiers of robotic and natural sciences.
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Citations
Cites
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Data-aggregation techniques in sensor networks: A survey
Rajagopalan, R., & Varshney, P. K. (2006) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2006.283821 · Cited by 900
Environmental Sensor Networks: A revolution in the earth system science?
Hart, J. K., & Martinez, K. (2006) · Earth-Science Reviews · doi:10.1016/j.earscirev.2006.05.001 · Cited by 778
Design and Control of Autonomous Underwater Robots: A Survey
Yuh, J. (2000) · Autonomous Robots · doi:10.1023/a:1008984701078 · Cited by 668
Data collection, storage, and retrieval with an underwater sensor network
Vasilescu, I., Kotay, K., Rus, D., Dunbabin, M., & Corke, P. (2005) · Proceedings of the 3rd international conference on Embedded networked sensor systems · doi:10.1145/1098918.1098936 · Cited by 625
Self-citationThe Coverage Problem in a Wireless Sensor Network
Huang, C.-F., & Tseng, Y.-C. (2005) · Mobile Networks and Applications · doi:10.1007/s11036-005-1564-y · Cited by 588
CO2 Fluxes over Plant Canopies and Solar Radiation: A Review
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Environmental Wireless Sensor Networks
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Cited by
Chen, K., Chen, H., Zhou, C., Huang, Y., Qi, X., Shen, R., Liu, F., Zuo, M., Zou, X., Wang, J., et al. (2020) · Water Research · doi:10.1016/j.watres.2019.115454 · Cited by 580
Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336
A knowledge-driven framework for Robotic Odor Source Localization using large language models
Mahmud, K. R., Wang, L., Hassan, S., & Zhang, Z. (2025) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2025.104915 · Cited by 10
Metadata
- Authors
- Dunbabin, M., & Marques, L.
- Venue
- IEEE Robotics & Automation Magazine
- Published
- 2012-03
- Volume
- 19
- Issue
- 1
- Pages
- 24-39
- ISSN
- 1070-9932 (ISSN-L)
- OpenAlex
- W1590192302
- Cited by
- 728
- References
- 49
- FWCI
- 44.97
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
