Autonomous Robotic Escort Incorporating Motion Prediction and Human Intention

摘要

This paper presents a technique that allows a robot to escort a human to their destination. Unlike tracking where the robot follows the human from behind, the proposed technique locates the robot in front of the human by incorporating human intention in addition to conventional motion prediction. Human head pose is used as an effective past-proven implicit indicator of intention. A particle filter allows accurate estimation and prediction of the non-Gaussian human trajectory. The predicted pose from both the human motion and intention determines the robot control action which leads to efficient autonomous escorting. Experimental analysis shows that the incorporation of the proposed human intention model reduces human position prediction error by approximately 40% when turning. Experimental validation with an omnidirectional mobile robotic platform shows successful and effective escorting compared to the conventional techniques.