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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2025  > In Press, Accepted Manuscript
Y. Zhang, G. Tian, C.-H. Zhang, C. Hua, W. Ding, and C. Ahn, “Environment modeling for service robots from a task execution perspective,” IEEE/CAA J. Autom. Sinica, 2025. doi: 10.1109/JAS.2025.125168
Citation: Y. Zhang, G. Tian, C.-H. Zhang, C. Hua, W. Ding, and C. Ahn, “Environment modeling for service robots from a task execution perspective,” IEEE/CAA J. Autom. Sinica, 2025. doi: 10.1109/JAS.2025.125168
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Environment Modeling for Service Robots From a Task Execution Perspective

doi: 10.1109/JAS.2025.125168
Funds:  This work was supported in part by the National Natural Science Foundation of China (62203378, 62203377, 62073279), in part by the Hebei Natural Science Foundation (F2024203036, F2024203115, F2022203097), in part by the S&T Program of Hebei (236Z2002G, 236Z1603G), in part by the Science Research Project of Hebei Education Department (BJK2024195), and in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT) (NRF-2020R1A2C1005449)
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    Abstract
  • Service robots are increasingly entering the home to provide domestic tasks for residents. However, when working in an open, dynamic, and unstructured home environment, service robots still face challenges such as low intelligence for task execution and poor long-term autonomy (LTA), which has limited their deployment. As the basis of robotic task execution, environment modeling has attracted significant attention. This integrates core technologies such as environment perception, understanding, and representation to accurately recognize environmental information. This paper presents a comprehensive survey of environmental modeling from a new task-execution-oriented perspective. In particular, guided by the requirements of robots in performing domestic service tasks in the home environment, we systematically review the progress that has been made in task-execution-oriented environmental modeling in four respects: 1) localization, 2) navigation, 3) manipulation, and 4) LTA. Current challenges are discussed, and potential research opportunities are also highlighted.

     

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