Assistant professor Alan Kuntz and recent PhD graduate Michael Bentley from the University of Utah Kahlert School of Computing and Robotics Center, have won the 2022 IEEE Access Best Video Award (Part 1). This award is given to the best video of papers submitted within a 6-month period to the multidisciplinary open access journal, IEEE Access. The award winning video and accompanying research paper is based on the research group’s meaningful steps toward answering the primary question that drives Prof. Alan Kuntz’s research group: “Can flexible, tentacle-like robots make medical interventions and surgery less invasive?” 

By providing a less invasive alternative to traditional surgical methods, this research has the potential to impact the lives of surgery patients all over the world. Dr. Richard N. Yu, a physician and researcher with The Boston Children’s Hospital, says that when surgeries are less invasive, “people recover tremendously faster with better cosmetic appearances.” Dr. Kuntz and Dr. Bentley’s research could help create quicker surgeries with fewer complications, smaller scars, and faster recovery times. 

But just how does their technology work?  “Flexible, tentacle-like robots, which we call ‘continuum robots,’ are capable of moving along naturally-occurring anatomical passages such as sinuses or bronchial trees, as well as curving around anatomical structures in other areas of the body,” said Kuntz, elaborating that the effort “combines the design of these continuum robots with the algorithms that make them easy-to-use for physicians.”

Kuntz’s continuum robots can be fabricated largely on everyday 3D printers, but can be very difficult to control manually due to the complex ways in which they move. Enabling users to control these robots with a haptic device, a kind of three-dimensional computer mouse, is the focus of the award-winning work.

The paper, a collaboration with associate professor Caleb Rucker at the University of Tennessee, presents a new method in a class of robotics algorithms called “motion planning,” and provides a way for the robot to move smartly around obstacles in its environment. This method enables a user to control the tip of the robot while the algorithm plans motions that prevent the robot from colliding with the patient’s body in ways the user doesn’t intend. The algorithm can be more than 17,000 times faster than prior approaches.

Prof. Kuntz and his co-authors propose deploying the robot in a patient’s pleural space (i.e., the space between a person’s chest wall and a collapsed lung) to help diagnose the cause behind the lung’s collapse. However, the researchers believe that the method has the potential to impact many other types of medical procedures throughout the body.  

“Our method now lets a person control this robot in ways not previously possible, which may pave the way for its use in minimally invasive medical procedures” Kuntz says.