Development of a Worker-Following Robot System: Worker Position Estimation and Motion Control under Measurement Uncertainty

Abstract

This study proposes a sensor data process and motion control method for a mobile platform essential for transporting finished products or subsidiary materials in a smart factory. We developed a system that recognizes a fiducial marker printed on the work clothes worn by a worker, estimates the worker’s location, and follows the worker using the estimated location. To overcome the limitations of simulation-based research, gait data on a two-dimensional plane were derived through a human gait model and an error model according to the distance between the image sensor and the reference marker. The derived gait data were defined as the localization result for the worker, and a Kalman filter was used to robustly address the uncertainty of the localization result. A virtual spring-damper system was applied to follow the Mecanum wheel-based mobile platform workers. The performance of the proposed algorithm was demonstrated through comparative simulations with existing methods.