Latency mitigation using applied HMMs for mixed reality-enhanced intuitive teleoperation in intelligent robotic welding

Abstract Latency mitigation is crucial to increasing operational success, ease of use, and product quality in telemanipulation tasks when remotely guiding complex robotic systems. Hardware limitations have created a gap performance optimization due large teleoperation delays, which machine learning techniques could fill with lower time, improved performance, reduced operating costs. Hidden Markov models (HMMs), particular, been explored alleviate the issue their relative use. A mixed reality-enhanced intuitive framework for immersive telerobotic welding presented. The proposed system implements an HMM generative algorithm learn predict human-welder motion enable low-cost solution, combining smoothing forecasting minimize time delay. predicted simple implement, can be used as general solution solve accurate. More specifically, it provides 66% RMSE reduction compared application without HMM, may further optimized by up 38%. Experiments show lets humans conduct tele-robot-assisted better performance.