According to the U.S. Bureau of Labor Statistics, in 2020, the United States saw almost 3.1 million registered nurses (RNs) nationwide. And this number is only set to grow in the future, with a predicted 7% increase in nursing jobs by 2029! This huge growth potential, especially in the wake of COVID-19, comes from a growing but aging American population and increasing demand for greater access to health care. 

At the same time, the workloads and responsibilities of RNs are only growing, including regular tasks such as assessing patients’ conditions, recording patients’ medical histories and symptoms, observing patients and recording data, administering patients’ treatments, consulting and collaborating with doctors and other healthcare professionals, operating and monitoring medical equipment, and more. But this increased pressure on RNs is resulting in elevated stress and fatigue in crucial healthcare workers. A study conducted on 53,846 nurses from six countries (including the U.S.) showed a strong relationship between increased nurse burnout causing decreased ratings of care quality. 

Naturally, researchers are looking to automation and robotics to take some strain off of RNs. Using Ridgeback, a team from the University of Texas at Arlington (UTA) Department of Computer Science and Engineering and the University of Texas at Arlington Research Institute (UTARI) envisions the future of work for nurses and NAs to be enhanced by a Multitasking Intelligent Nurse Aid (MINA) robotic system.

Creating a Healthier and Safer Future

Enhancing the future work of nurses is vital to ensuring the quality of healthcare quality and the nation’s economic and social well-being. Pervasive technologies offer the potential to improve the work performances of tomorrow’s nurses in multiple ways, including improving the productivity, efficacy, occupational safety, and quality of nurse work-life. This is particularly important as nurses and nursing assistants (NAs) play vital roles in healthcare and make up the largest section of health professions. 

The original goal of MINA is to augment nurses by performing non-critical tasks such as fetching and carrying supplies, or taking contactless vital signs, and providing information, communication, and as-needed help. Through UTARI’s initial interviews with nurses, they identified several tasks of high importance which they used to focus their efforts. These tasks were defined as follows:

  1. Fetching supplies, objects, food, and water for patients’ rooms and carrying supplies necessary for nurses’ work.
  2. Helping patients walk by carrying Intravenous therapy (IV) and monitoring various gait/walking parameters.

Navigating Dynamic Hospital Environments

The challenge with completing tasks like those mentioned above is that hospitals can be busy and tumultuous places. The crowded spaces of hospitals are only being exacerbated by the increased healthcare needs of COVID-19. Rooms and hallways can be filled with moving people, carts, beds, etc, but collisions must be avoided at all costs due to the sensitive nature of people and objects. Naturally then, robot navigation in these crowded environments can be very difficult. Additionally, the UTARI team knew that they wanted to avoid the use of artificial markers or other objects for their project which would require retrofitting existing hospitals or relying on visual features already in place.

For their project, Ridgeback functions as the mobile base of the MINA platform, as it is a high payload omnidirectional robot that can perform collision-free navigation in dynamic settings. Ridgeback also provides DC and AC electrical power and computing power for add-ons such as a robotic arm and sensors. As well, MINA is equipped with both front and back LIDARs for 360-degree mapping and real-time obstacle avoidance.  Finally, the team added a 3D camera for navigation and mapping, and searching for objects in the environment, and a Franka Emika Panda robotic arm to interact with objects for retrieval and other tasks.

“Clearpath Robotics is a leader in creating robots using ROS.  It is close to a turnkey solution for safe navigation and transport of heavy payloads. Furthermore, the Clearpath customer success team has been crucial in our development phase.”

Nicholas R. Gans, Principal Research Scientist and the Division Head for UTARI’s Automation & Intelligent Systems Division

Ridgeback Thrives Under Pressure

The team knew that they needed the perfect robot for such busy environments, and they believed Ridgeback was the right choice for the task. Specifically, they believe that omnidirectional movement allows for easier navigation and obstacle avoidance.  As well, the built-in navigation capability through ROS and GMapping SLAM has eased development. Other robotic platforms would have been more challenging to adapt to their needs as some options are aging, unsupported robots that no longer receive updates or platforms that are too small and have a very limited payload capacity and reach.

Finally, the UTARI team had experience with other Clearpath Robotics platforms such as Jackal UGV, Husky UGV, and Ridgeback platforms for other research. To this extent, they knew they wanted to leverage the platform’s ROS functionality as well as have full access to all the raw sensory data and motor controllers that would help to advance robotic research. As Nicholas R. Gans, Principal Research Scientist and the Division Head for UTARI’s Automation & Intelligent Systems Division at UTARI, stated: “Clearpath Robotics is a leader in creating robots using ROS.  It is close to a turnkey solution for safe navigation and transport of heavy payloads. Furthermore, the Clearpath customer success team has been crucial in our development phase.”

While the MINA project is still very much in the early phases, the team has begun integrating visualization and mapping algorithms.  This will enable MINA to navigate to designated areas and look for specific objects. Visual SLAM algorithms can also build a 3D map/point cloud to assist the robot in navigation and can be viewed by a human user.  As well, the team also has other vision algorithms that can search the scene and recognize UPC bar codes. These codes are commonly used in hospital supply rooms for inventory and are on many products that a nurse or other person might want.  The location of the bar codes are added as landmarks to the map and will help localize the objects for control of grasping.

The team hopes to enable further industry partnerships with their work as they already have existing partnerships with Texas Health Specialty Hospital in Fort Worth, TX  and the Texas Health Harris Methodist Hurst-Euless-Bedford, Bedford, TX. Finally, the team’s work won the best poster paper award at the 14th PErvasive Technologies Related to Assistive Environments Conference (PETRA 2021). You can read their full paper here.

The MINA team from UTARI is comprised of Dr. Nick Gans (Principal Research Scientist and the Division Head for UTARI’s Automation & Intelligent Systems Division at UTARI), Cody Lundberg (Research Engineer II at UTARI), Stephanie Arevalo Arboleda (Visiting Researcher at UTARI and Ph.D. student at the University of Duisburg Essen), Dr. Fillia Makedon (Jenkins-Garrett Professor at UTA), Dr. Maria Kyrarini (Assistant Professor at Santa Clara University), and Harish Ram Nambiappan (Ph.D. student at UTA and UTARI).

To learn more about UTARI, visit their website here.

To learn more about Ridgeback, visit our website here.

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