文献类型：期刊文献

英文题名：Optimizing the Scheduling of Localization Sensors for an Intelligent Robot by Reinforcement Learning

作者：Qin, Yingying[1]; Liu, Chang[1]; He, Binghan[1]; Zhang, Shijie[1]; Mo, Yanfang[2]; Lu, Jingyi[1]; Yang, Chao[1]

机构：[1] School of Information Science and Engineering, East China University of Science and Technology, Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, Department of Automation, Shanghai, 200237, China; [2] Lingnan University, Division of Industrial Data Science, Hong Kong

年份：2025

起止页码：2383

外文期刊名：Chinese Control Conference, CCC

收录：EI(收录号：20254419433547)

语种：英文

外文关键词：Behavioral research - Computation theory - Intelligent robots - Markov processes - Mobile robots - Navigation - Optimization

摘要：This paper studies the scheduling of on-board localization sensors for an intelligent robot navigating an indoor environment, aiming to reduce power consumption and extend operational lifetime. We propose a novel offline scheduling method that transforms the problem into a Markov decision process model, solved via reinforcement learning. The precomputed scheduling policy dynamically selects sensor combinations based on real-time resource availability and environmental dynamics, adaptively balancing localization precision and computational efficiency. Field tests with a mobile robot in real-world environments demonstrate the practical effectiveness of the proposed approach. ? 2025 Technical Committee on Control Theory, Chinese Association of Automation.