Human Detection and Human Pose Classification for Mobile Robots Interaction

Mobile robots are widely used in many departments such as industry, hospitals, restaurants, etc. The human detection and the human pose classification are usually used for human-robot interaction. The current study proposes human pose classification for human-robot interaction to avoid the collision. There are three main steps of the presented method. First, the algorithm detects the entire human within the determined range of 3D camera. Second, the K-Nearest Neighbor (KNN) model with skeleton points features is used for classifying the six postures of detected human such as neutral, left and right raise, both hand raise, cross hand posture and one opening hand forward. According to the posture classification, these can command the robot to move forward, stop, stop for a few seconds, and cancel the command. Finally, the command is used to interacting with the mobile robot to control the robot movement and to avoid the collision. The experiment results show that the designed algorithm can effectively detect and classify human posture with 86.14% for the accuracy of algorithms and interact effectively to avoid the collisions stop automatically within the 1.8 meters between human and robot.