Robot Formation Control Based on Internet of Things Technology Platform

• Published in: IEEE access Vol. 8; pp. 96767 - 96776
• Main Authors: Guan, Jiansheng, Zhou, Wende, Kang, Shaobo, Sun, Yuan, Liu, Zibo
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Control methods; Control systems; Cooperative control; Decision making; Deep learning; formation cooperative control; Internet of Things; Machine learning; Mobile robots; Motion control; Multiple robots; Neural networks; Particle swarm optimization; Real time; Robot; Robot control; Robot dynamics; Robot kinematics; Robot sensing systems; Robots; Task analysis; Visual tasks
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2992701

The cooperative control technology of robot formation can sense all kinds of external environment in real time. It is a multi-functional control and management system including visual recognition, task management execution and distribution, behavior decision-making and so on. It can easily adapt to all kinds of harsh environment. In order to meet the efficient response requirements of robot formation control, a real-time transmission system of robot cooperative motion control is built based on the Internet of things platform, which collects and feeds back the trajectory of multiple robots. Through particle swarm optimization deep learning algorithm, more accurate identification, prediction and guidance of the robot's next action. Finally, the simulation of robot formation motion is established by MATLAB software, which verifies the feasibility of particle swarm optimization deep learning neural network algorithm under the Internet of things technology. Compared with the traditional robot formation control method, the optimized control method has faster convergence speed, smaller error and more accurate position, which provides method guidance for the accuracy and efficiency of robot formation control technology.