An Efficient Motion Adjustment Method for a Dual-Arm Transfer Robot Based on a Two-Level Neural Network and a Greedy Algorithm

As the manipulation object of a patient transfer robot is a human, which can be considered a complex and time-varying system, motion adjustment of a patient transfer robot is inevitable and essential for ensuring patient safety and comfort. This paper proposes a motion adjustment method based on a two-level deep neural network (DNN) and a greedy algorithm. First, a dataset including information about human posture and contact forces is collected by experiment. Then, the DNN, which is used to estimate contact force, is established and trained with the collected datasets. Furthermore, the adjustment is conducted by comparing the estimated contact force of the next state and the real contact force of the current state by a greedy algorithm. To assess the validity, first, we employed the DNN to estimate contact force and obtained the accuracy and speed of 84% and 30 ms, respectively (implemented with an affordable processing unit). Then, we applied the greedy algorithm to a dual-arm transfer robot and found that the motion adjustment could reduce the contact force and improve human comfort efficiently; these validated the effectiveness of our proposal and provided a new approach to adjust the posture of the care receiver for improving their comfort through reducing the contact force between human and robot.