Motion Prediction With Gaussian Processes for Safe Human–Robot Interaction in Virtual Environments

Abstract

Humans use collaborative robots as tools for accomplishing various tasks. The interaction between humans and robots happens in tight shared workspaces. However, these machines must be safe to operate alongside humans to minimize the risk of accidental collisions. Ensuring safety imposes many constraints, such as reduced torque and velocity limits during operation, increasing the time to accomplish many tasks. However, for applications such as using collaborative robots as haptic interfaces with intermittent contacts, speed limitations result in poor user experiences. This research aims to improve the efficiency of a collaborative robot while improving the safety of the human user. We used Gaussian process models to predict human hand motion and developed strategies for human intention detection to improve the time for the robot while improving human security in a virtual environment.We then studied the effect of prediction. Results from comparisons show that the strategies with prediction model improved robot time by 3% and safety by 17%. When used alongside gaze for prediction, the strategy based on the Gaussian process model resulted into an improvement of the robot time by 2% and the safety by 13%.