Risk-Aware Decision Making for Service Robots to Minimize Risk of Patient Falls in Hospitals
Year: 2021
Type: conference-paper
Abstract: Planning under uncertainty is a crucial capability for autonomous systems to operate reliably in uncertain and dynamic environments. The concern of safety becomes even more critical in healthcare settings where robots interact with human patients. In this paper, we propose a novel risk-aware planning framework to minimize the risk of falls by providing a patient with an assistive device. Our approach combines learning-based prediction with model-based control to plan for the fall prevention task. This provides advantages compared to end-to-end learning methods in which the robot's performance is limited to specific scenarios, or purely model-based approaches that use relatively simple function approximators and are prone to high modeling errors. We compare various risk metrics and the results from simulated scenarios show that using the proposed cost function, the robot can plan interventions to avoid high fall score events.
Authors: Roya Sabbagh Novin, Amir Yazdani, Andrew Merryweather, Tucker Hermans
Institution: University of Utah
Language: English
Field: Health Professions
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W3092731427