부산대학교 도서관

There has been an increasing demand to automate the non-patient care matters so that the clinical staff can focus on delivering patient care. For example, out-patients undergoing chemotherapy increases their toilet usage frequency due to the treatment. As they are undergoing chemotherapy, their output waste contains a level of chemical. This task is compulsory yet troublesome and time-consuming so it is often desired to be removed from the nursing staff for them to focus on patient care. Hence, in this paper, we propose a manipulator system to automatically dispose the bedpan used by patients undergoing chemotherapy. The main technical challenge lies in the removal of the bedpan from the commode as the interaction of the grasping is highly dynamic, along with the different conditions of the bedpans. To address this manipulation issue, a Residual Reinforcement Learning (RRL) method that leverages vision-based commode pose estimation and the reinforcement learning (RL)-based uncertainty compensation for improvement of the grasping accuracy is proposed to increase the robustness of the disposal. The experiments conducted show that the manipulator can dispose the bedpan without human intervention and the proposed method achieves a 100 % success rate while the traditional method without RL is only 50 %.