An autonomous human-following robot for office automation

Abstract

As offices become increasingly automated, the demand for service robots that can seamlessly navigate and interact with people is rapidly growing. Yet, developing such robots with reliable tracking and smooth mobility remains challenging when constrained to low-cost hardware. To fulfil this requirement, this paper presents the design and evaluation of a low-cost autonomous human-following robot for office automation. The system combines AprilTag fiducial markers for robust human tracking with a hybrid control architecture integrating a Raspberry Pi 4 for real-time computer vision and an Arduino Uno for motor control. Motion smoothing is achieved through PID algorithms, while ultrasonic sensors enable dynamic obstacle avoidance in cluttered indoor environments. Two central research questions guided the study: (1) How can a human-following robot maintain reliable, real-time tracking in dynamic indoor environments using low-cost hardware? and (2) Which vision-based approach offers the best balance of accuracy, computational efficiency, and robustness? Comparative evaluation of vision-based tracking, QR codes, and AprilTags showed that AprilTags achieved superior performance, with over 98% tracking success, high pose estimation accuracy, and effective recovery after tracking loss. Real-world testing demonstrated consistent following behaviour, maintaining an average distance of 84±7cm with minimal latency (<4s). The results confirm that affordable hardware, combined with fiducial marker tracking and PID-based control, can deliver reliable and natural human-following performance suitable for office logistics. The research provides a scalable foundation for service robotics in office, healthcare, and industrial environments.