An intelligent system architecture for meal assistant robotic arm

A meal assistant robotic arm is necessary for disable people who cannot move by themselves such as paralysis patients, severely handicapped people, etc. Several research articles have been proposed. However, the system designs for the meal assistant robotic arm have been found for a small number of the improvements. Therefore, this paper presents a detailed study to innovate the meal assistant robotic arm. The proposed system design consists of four parts; 1) feature extraction algorithm using the Microsoft Kinect sensor to create the target position in 3-dimensional Cartesian coordinate, 2) inverse kinematic algorithm to convert the Cartesian coordinate into the joint angles, 3) controller algorithm. The proposed controller uses a new weight updating rule model of the neural network using multi-loop calculation based on the fusion of the gradient algorithm with the cubature Kalman filter (CKF) which can optimize the internal predicted state of the updated weights to improve the proposed controller performances, and 4) the 4-joint robotic arm. To evaluate the performances, the Matlab program is used to implement the overall system. The experimental results show that the meal assistant robotic arm system is able to track the human mouth in the 3-dimensional coordinate system.