Neuroadaptive Controller for Physical Interaction With an Omni-Directional Mobile Nurse Assistant Robot

Robot-assisted healthcare could help alleviate the shortage of nursing staff in hospitals and is a potential solution to assist with safe patient handling and mobility. In an attempt to off-load some of the physically-demanding tasks and automate mundane duties of overburdened nurses, we have developed the Adaptive Robotic Nursing Assistant (ARNA), which is a custom-built omnidirectional mobile platform with a 6-DoF robotic manipulator and a force sensitive walking handlebar. In this paper, we present a robot-specific neuroadaptive controller (NAC) for ARNA’s mobile base that employs online learning to estimate the robot’s unknown dynamic model and nonlinearities. This control scheme relies on an inner-loop torque controller and features convergence with Lyapunov stability guarantees. The NAC forces the robot to emulate a mechanical system with prescribed admittance characteristics during patient walking exercises and bed moving tasks. The proposed admittance controller is implemented on a model of the robot in a Gazebo-ROS simulation environment, and its effectiveness is investigated in terms of online learning of robot dynamics as well as sensitivity to payload variations.