Trajectory Generation in Joint Space Using Modified Hidden Markov Model

Human guide robots need to generate a trajectory from human training. The popular work space methods have to calculate the inverse kinematics. While the joint space methods need the dynamic time warping. These destroy the accuracy of the trajectory model. In this paper, we use Lloyd's algorithm to hidden Markov model (HMM). The advantages of the method over the other HMM are the time difference does not affects the HMM training, and the training data can be generated in joint space. We also modify the traditional HMM such that the model in the joint space works similar as the task space. Simulation and experimental results show that the modified HMM with Lloyd's algorithm in joint space is effective to generate the desired trajectory.