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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.

 

Pressure sensitive robotic skins have long been investigated for applications to physical human-robot interaction (pHRI). Numerous challenges related to fabrication, sensitivity, density, and reliability remain to be addressed under various environmental and use conditions. In our previous studies, we designed novel strain gauge sensor structures for robotic skin arrays. We coated these star-shaped designs with an organic polymer piezoresistive material, Poly (3, 4-ethylenedioxythiophene)-ploy(styrenesulfonate) or PEDOT: PSS and integrated sensor arrays into elastomer robotic skins. In this paper, we describe a dry etching photolithographic method to create a stable uniform sensor layer of PEDOT:PSS onto star-shaped sensors and a lamination process for creating double-sided robotic skins that can be used with temperature compensation. An integrated circuit and load testing apparatus was designed for testing the resulting robotic skin pressure performance. Experiments were conducted to measure the loading performance of the resulting sensor prototypes and results indicate that over 80% sensor yields are possible with this fabrication process.