Tiny Origami Robot That Can Unfold Itself From a Swallowed Capsule
It is possible to have a small robot that can unfold itself and move around the stomach to collect mistakenly swallowed items and also work on wounds. Researchers at the University of Sheffield, MIT and the Tokyo Institute of Technology will present their findings at the International Conference on Robotics and Automation.
It is possible to have a small robot that can unfold itself and move around the stomach to collect mistakenly swallowed items and also work on wounds. Researchers at the University of Sheffield, MIT and the Tokyo Institute of Technology will present their findings at the International Conference on Robotics and Automation.
Many people were involved in the paper including Shuhei Miyashita an electronics lecturer in York, England, mechanical engineering graduate Steven Guitron, CSAIL postdoc Shuguang Li and Sheffield Universities, Dana Damian.
The robot is a redesign of the model that was reported last year although it still uses “stick-slip” to move. The movement of the body and changes in weight distribution lead to motion. It also has a layer that becomes smaller when heated, and this is set between structural material. The new robot is made from a malleable biocompatible material and needs fewer slots to ensure it is stiff enough.
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Water motion also helps movement around the stomach and as a result, there is a fin attached, so it is no longer a flat item. The robot had to be swallowed in the form of a capsule but had to be strong enough to unfold to begin work. Accordion folds were incorporated, and the corners were pinched.
Dozens of materials were tested, and dried pig intestine was selected.
As thousands of small batteries are swallowed every year, there is a need to get them out of the stomach before they produce hydroxide and burn it. Bradley Nelson of the Swiss Federal Institute of Technology finds the robot “highly creative and highly practical.”
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Daniel Okafor
Daniel Okafor is the Senior AI Reporter at TrendinTech, where he covers large language models, machine learning research and the practical use of artificial intelligence across business and government. He previously reported on artificial intelligence for MIT Technology Review, covering the labs behind the current generation of frontier models and the policy debates in Washington and Brussels. Daniel holds a Master of Science in Machine Learning from Carnegie Mellon University and follows the research community closely, attending NeurIPS and ICML each year to speak with the people behind the papers. He has a particular interest in evaluation: how models are benchmarked, where those benchmarks fail and what that means for the companies betting on them.
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