Application of social entertainment robots based on machine learning algorithms and the Internet of Things in collaborative art performances

Abstract

In terms of control strategies for social entertainment robots, advanced control system design was adopted in the study, aiming to enable robots to achieve efficient collaborative art performances. The control system is based on machine learning algorithms and Internet of Things technology, combined with the application of sensing technology, providing accurate environmental perception and real-time feedback mechanisms for robots. Considering the collaboration and interaction between robots and human actors, control strategies adapted to different scenes were designed by analyzing and understanding the needs of artistic performances. These strategies not only consider the robot’s own actions and performance, but also the interaction with human actors and the coordination with the entire performance scene. The control method in this article combines machine learning algorithms and sensing technology to enable robots to make intelligent decisions and action planning by learning and perceiving real-time environmental information. By modeling and simulating the structure and characteristics of the robot, precise planning and control of the robot’s motion trajectory can be achieved. Through dynamic modeling, it is possible to better understand the motion characteristics and energy consumption of robots, and to adjust and optimize their actions during the performance process.