Computer scientists at the University of California San Diego have developed a more accurate navigation system that will allow robots to better negotiate busy clinical environments in general and emergency departments more specifically. The researchers have also developed a dataset of open source videos to help train robotic navigation systems in the future. The project stemmed from conversations with clinicians over several years. The consensus was that robots would best help physicians, nurses and staff in the emergency department by delivering supplies and materials. But this means robots have to know how to avoid situations where clinicians are busy tending to a patient in critical or serious condition.

Computer scientists at the University of California San Diego have developed a navigation system that will allow robots to better negotiate busy clinical environments. A navigation system developed by University of California, San Diego (UC San Diego) computer scientists aims to improve the ability of robots to navigate busy clinical environments, especially hospital emergency departments. The Safety Critical Deep Q-Network (SafeDQN) navigation system is built around an algorithm that factors in the number of people clustered in a space and the speed and abruptness with which they are moving and directs robots to move around them. The researchers trained the algorithm using a dataset of more than 700 YouTube videos, mainly from documentaries and reality shows. When tested in a simulated environment and compared to other state-of-the-art robotic navigation systems, the researchers determined that SafeDQN found the most efficient and safest paths in all cases.