Rescue robotics & using machine learning to detect gasses w/Achim Lilienthal

Achim talks about rescue robotics and how he is working with integrating sensors that can work and be useful in this challenging application like gas sensors. Achim got in to robotics from working in physics when the team hid did his PhD worked on gas sensors and he saw an opportunity to contribute based on his background in physics. He also talks about a strong personal reason for developing gas sensors as a family member was killed in a gas explosion when he was a kid. We also hear more about the challenges in using commercial senors that are intended for lab use and not for field use mounted on a robot. He talks about how he implements machine learning to detect gasses that was not meant to be in that particular situation. We also get to hear about how you can use different sensors to create a fingerprint of the gases in a situation and how you can use this to great a “heat map” describing what gases are there and at what concentration. This can help in determening the risk of an explosion by sensing gas type, consentration and heat. He also tells us about the smokebot project that aims to oreduce risks for emergensy personel and to use resourcess mor eficently in an timecritical amergency situation. We hear about why it is very hard to deploy robots in many emergensy situations and especilay in fires where there are smoke that blocks most sensrors blinding the robot. One of the few sensors that actually still works are radar and that can offer great asistance to firefighters. This podcast is part of the Wevolver network. Wevolver is a platform & community providing engineers informative content to help them innovate. Learn more at Wevolver.com Promote your company in our podcast? If you are interested in sponsoring the podcast, you can contact us at richard@wevolver.com

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Rescue robotics & using machine learning to detect gasses w/Achim Lilienthal is an episode from Wevolver Robots in Depth by Richard Hulskes.

This episode is 00:38:52 long.

This episode was published on Mar 11, 2020.

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