文献类型：会议论文

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]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Sch Informat Sci & Engn,Dept Automat, Shanghai 200237, Peoples R China;[2]Lingnan Univ, Div Ind Data Sci, Hong Kong, Peoples R China

会议论文集：44th Chinese Control Conference-CCC-Annual

会议日期：JUL 28-30, 2025

会议地点：Chongqing, PEOPLES R CHINA

语种：英文

外文关键词：SLAM; extended Kalman filter; sensor scheduling; reinforcement learning

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.