Robotics

Low-cost tech uses magnets to track surgical robots

The tube-shaped robotic device that was used in the study, within a human body model
University of California, San Diego
The tube-shaped robotic device that was used in the study, within a human body model
University of California, San Diego

Flexible robotic surgical devices show a lot of promise, as they're able to move through delicate parts of the body without causing damage. And thanks to a new system, it could soon be cheaper, safer and easier to track where they are within the patient.

Because the devices are typically made of soft, squishy materials, they don't always show up well when using traditional imaging techniques. As a result, it may be difficult for doctors to tell where the tools' cameras, cutting instruments or drug-delivery attachments are in relation to the target area.

And while there are currently some methods that help in this regard, they typically require expensive equipment, and may involve exposing the patient to potentially harmful levels of radiation.

Seeking a better alternative, scientists at the University of California, San Diego have created a system in which the front end of a flexible robotic device is equipped with a magnet. As that robot moves through an enclosed environment (which could ultimately be a human body), four strategically spaced external sensors each measure the strength of the magnetic field being produced by the magnet.

Via an artificial neural network, the system compares the readings of the four sensors, using that data to accurately determine the location of the front of the robot. It's not unlike the manner in which GPS figures out where a user is, based on their position relative to multiple satellites.

So far, the system has been successfully tested in a lab-based model, using a nylon-tube-type robotic device that grows longer as fluid is pumped into it. The whole setup, including the robot, magnet, sensors and other electronics, reportedly only cost about US$100.

A paper on the research, which is being led by Prof. Tania Morimoto and PhD student Connor Watson, was recently published in the journal IEEE Robotics and Automation Letters.

Source: University of California, San Diego

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