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Roboticists at the University of California in San Diego developed the simple system to track the location of flexible surgical robots inside the human body.
“Continuum medical robots work really well in highly constrained environments inside the body,” said co-creator Tania Morimoto, professor of mechanical engineering. “They're inherently safer and more compliant than rigid tools. But it becomes a lot harder to track their location and their shape inside the body. And so if we are able to track them more easily, that would be a great benefit both to patients and surgeons."
The researchers embedded a magnet in the tip of a flexible robot that can be used in delicate places inside the body, such as arterial passages in the brain.
“We worked with a growing robot, which is a robot made of a very thin nylon that we invert, almost like a sock, and pressurise with a fluid which causes the robot to grow,” said co-creator and PhD student Connor Watson. Because the robot is soft and moves by growing, it has very little impact on its surroundings, making it ideal for use in medical settings.
The researchers then used existing magnet localisation methods to develop a computer model that predicts the robot's location, similar to GPS.
Global positioning satellites ‘ping’ smartphones, and determine where they are based on how long it takes for the signal to arrive. Similarly, researchers know how strong the magnetic field should be around the magnet embedded in the robot. They rely on four sensors that are carefully spaced around the area where the robot operates to measure the magnetic field strength. Based on how strong the field is, they are able to determine where the tip of the robot is.
The whole system, including the robot, magnets and magnet localisation setup, reportedly costs about $100 (£82).
Morimoto and Watson then went a step further by training a neural network to learn the difference between what the sensors were reading and what the model said the sensors should be reading. As a result, they improved localisation accuracy to track the tip of the robot.
“Ideally we are hoping that our localisation tools can help improve these kinds of growing robot technologies. We want to push this research forward so that we can test our system in a clinical setting and eventually translate it into clinical use,” said Morimoto.
The research was published in the April 2020 issue of IEEE Robotics and Automation Letters.
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