Online-HMM with Two-Layer Bayesian Method for Operator's Expected Speed Estimation in Teleoperated Gluing Tasks *

Abstract

For direct teleoperation tasks, the follower robot accomplishes tasks by strictly executing the inputs from the operator. However, the operator's physiological tremor seriously reduces the smoothness of the trajectory, especially in tasks relying on operator’s experience such as gluing, while the random tremor is hard to be described and suppressed online. To navigate this challenge, this paper proposes an Online Hidden Markov Model with Two-Layer Bayesian (TLB-OHMM) method to suppress the tremor by estimating the operator's expected speed, which can recognize new intention online and is adaptive to complex trajectories. First, the actual moving speed of the human hand is modeled as an HMM. Then, an Online-HMM method based on online expectation maximum (EM) algorithm is introduced to shorten the training time and realize the online updating of the HMM parameters. Finally, a two-layer Bayesian method is proposed to estimate the expected speed of the human hand. Experimental results in simulation and real teleoperated gluing task show that the proposed method greatly reduces computation time and improves the quality of trajectories, especially for complex curve trajectories, compared with the traditional HMM based method.