The application of autonomous robotics to close-contact healthcare tasks has a clear role for the future due to its potential to reduce infection risks to staff and improve clinical efficiency. Nasopharyngeal (NP) swab sample collection for diagnosing upper-respiratory illnesses is one type of close contact task that is interesting for robotics due to the dexterity requirements and the unobservability of the nasal cavity. We propose a control system that performs the test using a collaborative manipulator arm with an instrumented end-effector to take visual and force measurements, under the scenario that the patient is unrestrained and the tools are general enough to be applied to other close contact tasks. The system employs a visual servo controller to align the swab with the nostrils. A compliant joint velocity controller inserts the swab along a trajectory optimized through a simulation environment, that also reacts to measured forces applied to the swab. Additional subsystems include a fuzzy logic system for detecting when the swab reaches the nasopharynx and a method for detaching the swab and aborting the procedure if safety criteria is violated. The system is evaluated using a second robotic arm that holds a nasal cavity phantom and simulates the natural head motions that could occur during the procedure. Through extensive experiments, we identify controller configurations capable of effectively performing the NP swab test even with significant head motion, which demonstrates the safety and reliability of the system.

The nasopharyngeal (NP) swab sample test, commonly used to detect COVID-19 and other respiratory illnesses, involves moving a swab through the nasal cavity to collect samples from the nasopharynx. While typically this is done by human healthcare workers, there is a significant societal interest to enable robots to do this test to reduce exposure to patients and to free up human resources. The task is challenging from the robotics perspective because of the dexterity and safety requirements. While other works have implemented specific hardware solutions, our research differentiates itself by using a ubiquitous rigid robotic arm. This work presents a case study where we investigate the strengths and challenges using compliant control system to accomplish NP swab tests with such a robotic configuration. To accomplish this, we designed a force sensing end-effector that integrates with the proposed torque controlled compliant control loop. We then conducted experiments where the robot inserted NP swabs into a 3D printed nasal cavity phantom. Ultimately, we found that the compliant control system outperformed a basic position controller and shows promise for human use. However, further efforts are needed to ensure the initial alignment with the nostril and to address head motion.

The typical phases of Bayesian network (BN) structured development include specification of purpose and scope, structure development, parameterisation and validation. Structure development is typically focused on qualitative issues and parameterisation quantitative issues, however there are qualitative and quantitative issues that arise in both phases. A common step that occurs after the initial structure has been developed is to perform a rough parameterisation that only captures and illustrates the intended qualitative behaviour of the model. This is done prior to a more rigorous parameterisation, ensuring that the structure is fit for purpose, as well as supporting later development and validation. In our collective experience and in discussions with other modellers, this step is an important part of the development process, but is under-reported in the literature. Since the practice focuses on qualitative issues, despite being quantitative in nature, we call this step qualitative parameterisation and provide an outline of its role in the BN development process.