Human Identification for Human-Robot Interactions

Abstract

In co-robotics applications, the robots must identify human partners and recognize their status in dynamic interactions for enhanced acceptance and effectiveness as socially interactive agents. Using the data from depth cameras, people can be identified from a person’s skeletal information. This paper presents the implementation of a human identification algorithm using a depth camera (Carmine from PrimeSense), an open-source middleware (NITE from OpenNI) with the Java-based Processing language and an Arduino microcontroller. This implementation and communication sets a framework for future applications of human-robot interactions. Based on the movements of the individual in the depth sensor’s field of view, the program can be set to track a human skeleton or the closest pixel in the image. Joint locations in the tracked human can be isolated for specific usage by the program. Joints include the head, torso, shoulders, elbows, hands, knees and feet. Logic and calibration techniques were used to create systems such as a facial tracking pan and tilt servomotor mechanism. The control system presented here sets groundwork for future implementation into student built animatronic figures and mobile robot platforms such as Turtlebot.