A knowledge-driven framework for Robotic Odor Source Localization using large language models
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Neural-adaptive control utilizing a 128-node recurrent neural network delivers rapid responses in surgery and UAV swarms and achieves high accuracy in construction human-robot collaboration, despite significant computational demands.
“Neural-adaptive control, utilizing a 128-node RNN, delivers rapid responses in surgery [11] and UAV swarms [27], achieving high accuracy in construction HRC [40], despite significant computational demands [10].”
From Hybrid Fault-Tolerant Control in Cooperative Robotics: Advances in Resilience and Scalability (2025) · cites this paper as [40]
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- Authors
- Mahmud, K. R., Wang, L., Hassan, S., & Zhang, Z.
- Affiliations
- Louisiana Tech University (US)
- Embry-Riddle Aeronautical University Daytona Beach Florida Campus (US)
- Corresponding author
- Wang, Lingxiao
- Venue
- Robotics and Autonomous Systems
- Publisher
- Elsevier BV
- Published
- 2025-04
- Volume
- 186
- Pages
- 104915
- ISSN
- 0921-8890 (ISSN-L)
- OpenAlex
- W4406277701
- Cited by
- 10
- References
- 92
- Citations by year
- 2026: 4 · 2025: 6
- FWCI
- 6.39
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Robotics and Autonomous Systems (publishedVersion)
- Funders
- State of Louisiana Board of Regents (LEQSF(2024-27)-RD-A-22)
- Topics
- Life Sciences › Agricultural and Biological Sciences › Insect Science › Insect Pheromone Research and Control
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Keywords
- odor source localization, mobile robots, large language models, LLM agents, memory modules, contextual understanding, generalization across environments
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 ›
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