Self-Feeding Assistive Robotic Arm for People with Severe Disabilities

To assist people with severe disabilities to eat and drink at their own pace, we present a self-feeding robotic arm system with the functions of object detections using YOLOv5 model and user's mouth detection using MediaPipe. The redundant robotic arm with 7-DOF (degree-of-freedom) was made to perform natural eating motion and avoid obstacles by considering the home care and nursing care environments in Japan. An open-source software, ROS (Robot Operating System), and a motion planning framework, MoveIt! simulator including the motion planning solver using inverse kinematics, were used to simulate the 7-DOF robotic arm. Moreover, a single-finger operated interface was applied as a controller for the robotic arm system. We demonstrated that using the proposed robotic arm system, the tasks to grasp and move the plastic bottle to the user's mouth were conducted. From the simulation and experimental results, we found that the detections of the target object and the user's mouth were conducted effectively. In addition, it was shown that the success rates of the tasks were more than 80% or equal when having no object in the calculated trajectory of the robotic arm. Future works include conducting some experiments of the tasks with people with disabilities.