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Louhi Kasahara Lab
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The lab uses robots and AI to automate work that only people could do until now.
AboutAbout the lab
Robots and AI built for use on real-world sites.
The Louhi Kasahara Lab researches how to use robotics and artificial intelligence (AI) to automate work that until now only people could do.
Always keeping real-world sites in mind, the lab works on signal processing, machine learning, sensor fusion, and more.
The lab is advancing research on automating hammering test inspection and on unmanned construction.
Key terms
- Hammering test inspection
- An inspection method in which concrete or similar material is struck with a hammer, and internal damage is found from differences in the sound
- Unmanned construction
- Carrying out construction work without people riding in the construction machines, by operating them remotely or controlling them automatically
- Unsupervised learning
- A machine learning method in which a computer finds the features and groupings in data that has no correct answers attached
ResearchResearch themes
The lab focuses on using robotics and artificial intelligence to automate work that until now only people could do. Always keeping real-world sites in mind, it works on signal processing, machine learning, sensor fusion, and more.
*Illustrative imageInspecting infrastructure by sound
This research uses acoustic data to inspect infrastructure. The lab is also developing a robot that inspects tunnels automatically.
*Illustrative imageDetecting anomalies with machine learning
This research detects anomalies using machine learning methods such as unsupervised learning and semi-supervised learning.
*Illustrative imageRecognizing construction machine actions from images
This research uses images to recognize what actions a construction machine is performing. The lab also operates construction machines inside a simulator.
*Illustrative imageJudging whether disaster sites are passable
This research runs small construction machines to determine whether they can get through a site hit by a disaster (traversability).
MembersFaculty
- Project Associate ProfessorJun Younes Louhi KasaharaPublications ↗
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Construction site excavator and truck” Neb / CC BY-SA 4.0 (cropped)
- Inspecting infrastructure by sound: “Kazan Tunnel Kyoto JPN 004” Ignis / CC BY-SA 4.0 (cropped)
- Detecting anomalies with machine learning: “Cracks in concrete - geograph.org.uk - 706423” Evelyn Simak / CC BY-SA 2.0 (cropped)
- Recognizing construction machine actions from images: “20211211 Valkenburg Construction Site 03” Flocci Nivis / CC BY 4.0 (cropped)
- Judging whether disaster sites are passable: “Damaged Road R249 Otani Tunnel (2024 Noto Earthquake)” Ministry of Land, Infrastructure, Transport and Tourism of Japan / CC BY 4.0 (cropped)
- Related lab (Yamashita-An-Hamada Lab): “Image-Omnidirectional image computer lab” Sgeureka / CC BY-SA 3.0 (cropped)
- Related lab (Bio-Inspired Robotics Lab (BIRL)): “Soft Robotics” Oregon State University / CC BY-SA 2.0 (cropped)
- Related lab (i-Construction Systems Lab): “Mangere Bridge Construction East Side” Ingolfson / Public domain (cropped)
- Related lab (An Lab): “Biomechanics lab with force plate” Apfriesen / CC BY-SA 4.0 (cropped)
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