The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Cooperative forest fire surveillance using a team of small unmanned air vehicles
Abstract
The objective of this paper is to explore the feasibility of using multiple low-altitude, short endurance (LASE) unmanned air vehicles (UAVs) to cooperatively monitor and track the propagation of large forest fires. A real-time algorithm is described for tracking the perimeter of fires with an on-board infrared sensor. Using this algorithm, we develop a decentralized multiple-UAV approach to monitoring the perimeter of a fire. The UAVs are assumed to have limited communication and sensing range. The effectiveness of the approach is demonstrated in simulation using a six degree-of-freedom dynamic model for the UAV and a numerical propagation model for the forest fire. Salient features of the approach include the ability to monitor a changing fire perimeter, the ability to systematically add and remove UAVs from the team, and the ability to supply time-critical information to fire fighters.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Coordination of groups of mobile autonomous agents using nearest neighbor rules
Jadbabaie, A., Lin, J., & Morse, A. S. (2003) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2003.812781 · Cited by 8,339
Cited by
Multi-robot target detection and tracking: taxonomy and survey
Robin, C., & Lacroix, S. (2016) · Autonomous Robots · doi:10.1007/s10514-015-9491-7 · Cited by 290
Evaluating Effects of User Experience and System Transparency on Trust in Automation
Yang, X. J., Unhelkar, V. V., Li, K., & Shah, J. A. (2017) · Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction · doi:10.1145/2909824.3020230 · Cited by 208
Metadata
- Authors
- Casbeer, D. W., Kingston, D. B., Beard, R. W., & McLain, T. W.
- Venue
- International Journal of Systems Science
- Publisher
- Informa UK Limited
- Published
- 2006-05-15
- Volume
- 37
- Issue
- 6
- Pages
- 351-360
- ISSN
- 0020-7721 (ISSN-L)
- 1464-5319
- OpenAlex
- W2103128941
- Cited by
- 532
- References
- 1
- FWCI
- 28.20
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Cooperative forest fire surveillance using a team of small unmanned air vehicles. https://app.cochat.ai/papers/10.1080/00207720500438480
- The paper
- Casbeer, D. W., Kingston, D. B., Beard, R. W., & McLain, T. W. (2006). Cooperative forest fire surveillance using a team of small unmanned air vehicles. International Journal of Systems Science. https://doi.org/10.1080/00207720500438480
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.