Nguyen Thi Thanh Van , Hoang Le Hung , Nguyen Cong Luong , Huy T. Nguyen , Nguyen Tien Hoa , Ngo Manh Duy , Ngo Manh Tien
{"title":"Energy consumption minimization for robotic systems in intelligent factories with the assistance of STAR-RIS: A reinforcement learning approach","authors":"Nguyen Thi Thanh Van , Hoang Le Hung , Nguyen Cong Luong , Huy T. Nguyen , Nguyen Tien Hoa , Ngo Manh Duy , Ngo Manh Tien","doi":"10.1016/j.comnet.2025.111441","DOIUrl":null,"url":null,"abstract":"<div><div>The integration of wireless communication to factory robots to form connected robots has become an encouraging technology for intelligent factories due to their cost-effectiveness and high flexibility. However, due to obstacles, guaranteeing stable communication links between an access point (AP) and a robot is challenging. To address this, we propose to deploy a Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surface (STAR-RIS) which allows incident signals to be reflected and transmitted on both sides of the RIS surface, enabling comprehensive <span><math><mrow><mn>36</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>0</mn></mrow></msup></mrow></math></span> coverage to improve data rate between the AP and the robot. Then, we define an optimization problem that seeks to minimize the system’s overall energy consumption including the AP communication energy and the robot energy consumption while satisfying the requirements of the robot’s safety distance, maximum movement duration, and data rate threshold. The problem involves optimizing the robot’s trajectory, the transmitted power of the AP, the phase shifts, and the transmitting/reflecting coefficient of the STAR-RIS. The optimization problem is nonconvex due to the nonconvex objective function, the nonconvex obstacle-robot distance constraint, the phase shifts and transmitting/reflecting coefficient of STAR-RIS, and the data rate requirement constraint. In addition, there are many dynamic factors in the working environment, such as the robot’s location, the channel between the AP and robot. Therefore, we first approximate the original optimization problem by a Markov Decision Process (MDP) model, then propose to use a DRL algorithm based on Proximal Policy Optimization (PPO) which uses an actor and critic network policy reinforcement to solve the optimization problem. We conducted extensive simulations under various scenarios, and the results show that the case with the use of the STAR-RIS significantly reduces the travel distance of the robot and the system energy consumption compared with the cases with A2C based algorithm, the conventional RIS or without RIS.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"270 ","pages":"Article 111441"},"PeriodicalIF":4.6000,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Networks","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1389128625004086","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
引用次数: 0
Abstract
The integration of wireless communication to factory robots to form connected robots has become an encouraging technology for intelligent factories due to their cost-effectiveness and high flexibility. However, due to obstacles, guaranteeing stable communication links between an access point (AP) and a robot is challenging. To address this, we propose to deploy a Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surface (STAR-RIS) which allows incident signals to be reflected and transmitted on both sides of the RIS surface, enabling comprehensive coverage to improve data rate between the AP and the robot. Then, we define an optimization problem that seeks to minimize the system’s overall energy consumption including the AP communication energy and the robot energy consumption while satisfying the requirements of the robot’s safety distance, maximum movement duration, and data rate threshold. The problem involves optimizing the robot’s trajectory, the transmitted power of the AP, the phase shifts, and the transmitting/reflecting coefficient of the STAR-RIS. The optimization problem is nonconvex due to the nonconvex objective function, the nonconvex obstacle-robot distance constraint, the phase shifts and transmitting/reflecting coefficient of STAR-RIS, and the data rate requirement constraint. In addition, there are many dynamic factors in the working environment, such as the robot’s location, the channel between the AP and robot. Therefore, we first approximate the original optimization problem by a Markov Decision Process (MDP) model, then propose to use a DRL algorithm based on Proximal Policy Optimization (PPO) which uses an actor and critic network policy reinforcement to solve the optimization problem. We conducted extensive simulations under various scenarios, and the results show that the case with the use of the STAR-RIS significantly reduces the travel distance of the robot and the system energy consumption compared with the cases with A2C based algorithm, the conventional RIS or without RIS.
期刊介绍:
Computer Networks is an international, archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in the computer communications networking area. The audience includes researchers, managers and operators of networks as well as designers and implementors. The Editorial Board will consider any material for publication that is of interest to those groups.