Building millimeter sized robots w/Professor Julien Bourgeois
03/13/20 • 45 min
Julien Bourgeois talks about self-reconfiguring modular robotics and how he is developing millimeter sized robots called Claytronics.
Julien started out as a computer scientist. He was always interested in robotics privately but then had the opportunity to get into micro robots when his lab was merged into the FEMTO-ST Institute. He later worked with Seth Copen Goldstein at Carnegie Mellon on the Claytronics project.
He tells us how he works on creating a world built with programmable material that would allow objects to change their form and function automatically by running a program. This will create smart objects that can adapt to the world around them and user preference in a totally new way.
One large benefit of programmable matter is that development can happen both in the computer and in the real world with changes transferred between them. A change done in the code would appear in the part made up of programmable matter, but the part can also be changed in the real world and the change would be transferred to the program controlling it. This would create a very flexible, dynamic and highly intuitive design process.
The structures based on programmable matter also exhibit many very special characteristics. They can be self-healing if they get damaged, they can dynamically respond to load and be as strong as needed, they can degrade gracefully and predictably and can even indicate that they are overloaded and might fail so that the user can take the appropriate actions.
We also learn about a system for sorting very small components he built and how cameras could not be applied.
Per and Julien discuss how developing programmable material is hard and that many difficult problems have to be overcome. At the same time, many problems with the current way of doing things will be solved in a fundamentally better way by systems built with
programmable matter.
Julien shows an enlarged mock-up of the small robots that make up programmable matter, catoms, and speaks about how they are designed. Currently he is working on a unit that is one centimeter in diameter and he shows us the very small CPU that goes into that model.
There is also an art project in progress, using another version of programmable material building blocks.
More about the small CPUs mentioned at https://www.cubeworks.io This episode was recorded at ICML, IJCAI-ECAI, AAMAS in Stockholm, Sweden 2018.
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
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If you are interested in sponsoring the podcast, you can contact us at [email protected]
Julien Bourgeois talks about self-reconfiguring modular robotics and how he is developing millimeter sized robots called Claytronics.
Julien started out as a computer scientist. He was always interested in robotics privately but then had the opportunity to get into micro robots when his lab was merged into the FEMTO-ST Institute. He later worked with Seth Copen Goldstein at Carnegie Mellon on the Claytronics project.
He tells us how he works on creating a world built with programmable material that would allow objects to change their form and function automatically by running a program. This will create smart objects that can adapt to the world around them and user preference in a totally new way.
One large benefit of programmable matter is that development can happen both in the computer and in the real world with changes transferred between them. A change done in the code would appear in the part made up of programmable matter, but the part can also be changed in the real world and the change would be transferred to the program controlling it. This would create a very flexible, dynamic and highly intuitive design process.
The structures based on programmable matter also exhibit many very special characteristics. They can be self-healing if they get damaged, they can dynamically respond to load and be as strong as needed, they can degrade gracefully and predictably and can even indicate that they are overloaded and might fail so that the user can take the appropriate actions.
We also learn about a system for sorting very small components he built and how cameras could not be applied.
Per and Julien discuss how developing programmable material is hard and that many difficult problems have to be overcome. At the same time, many problems with the current way of doing things will be solved in a fundamentally better way by systems built with
programmable matter.
Julien shows an enlarged mock-up of the small robots that make up programmable matter, catoms, and speaks about how they are designed. Currently he is working on a unit that is one centimeter in diameter and he shows us the very small CPU that goes into that model.
There is also an art project in progress, using another version of programmable material building blocks.
More about the small CPUs mentioned at https://www.cubeworks.io This episode was recorded at ICML, IJCAI-ECAI, AAMAS in Stockholm, Sweden 2018.
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 [email protected]
Previous Episode
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 [email protected]
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