Online control programming algorithm for human–robot interaction system with a novel real

This article proposes an online control programming algorithm for human–robot interaction systems, where robot actions are controlled by the recognition results of gestures performed by human operators based on visual images. In contrast to traditional robot control systems that use pre-defined programs to control a robot where the robot cannot change its tasks freely, this system allows the operator to train online and replan human–robot interaction tasks in real time. The proposed system is comprised of three components: an online personal feature pretraining system, a gesture recognition system, and a task replanning system for robot control. First, we collected and analyzed features extracted from images of human gestures and used those features to train the recognition program in real time. Second, a multifeature cascade classifier algorithm was applied to guarantee both the accuracy and real-time processing of our gesture recognition method. Finally, to confirm the effectiveness of our algorithm, we selected a flight robot as our test platform to conduct an online robot control experiment based on the visual gesture recognition algorithm. Through extensive experiments, the effectiveness and efficiency of our method has been confirmed