Combination of Dynamic Movement Primitives and Recurrent Neural Network to Solve Kinematics and Motion Planning Problems of an Exoskeleton-Based Rehabilitation Robot

Abstract

In the field of robotics, especially in exoskeleton-based rehabilitation robots, motion planning is an important problem. An effective motion trajectory must ensure naturalness and similarity to daily human movements. In this study, the researchers proposed using the dynamic movement primitives (DMPs) method to create trajectories similar to the actual trajectories of daily human activities. Subsequently, the study proposed and constructed a recurrent neural network (RNN) model to solve the inverse kinematics (IK) problem. This model was trained using a large dataset generated from the DMP algorithm mentioned earlier. The IK results applied to simulations on the upper limb exoskeleton robot of Vietnam (UExosVN) under development showed that the obtained trajectories have high accuracy and stability, suitable for control and similar in form to the desired trajectories. The study conducted experiments with two activities of daily living (ADL), namely the playing ball task and drinking water task. The errors in joint variables for the IK problem are all under 1°, except for joint five, which does not significantly impact the robot’s end-point accuracy. The shape similarity level in the predicted trajectory compared to the test data is mostly above 0.75. The combined approach of DMP and RNN provides a potential solution for designing motion trajectories for rehabilitation robots.