TY - GEN
T1 - Potential Implementation of Embodied Intelligence Technology in the Power Grid
T2 - 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024
AU - Song, Rui
AU - Majeed, Anwar P.P.Abdul
AU - Wang, Lanruo
AU - Li, Wei
AU - Yang, Wei
AU - Luo, Yang
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - The rapid development of power artificial intelligence (AI) technology have brought significant benefits to various aspects of the grid. However, a critical challenge remains: the lack of automated actuators for AI-generated strategies, which currently rely heavily on human execution. This paper addresses the need for intelligent robots in power grid applications, particularly in inspection and live-line work, to enhance operational safety and efficiency. We explore the potential implementation of embodied intelligence (EI) technology, focusing on two primary approaches: the integration of large and small models for vision, language, and action, and end-to-end visual-language-action (VLA) models. The first approach, exemplified by the PixelNav framework, demonstrates effective navigation and manipulation capabilities, while the second approach, represented by models like RT-2 and 3D-VLA, shows promise in generalization and robustness. Additionally, we discuss the application of these EI technologies in specific power grid scenarios, such as substation and distribution station inspections, and highlight the importance of developing more intelligent and adaptable robotic systems. The aim of this paper is to provide a comprehensive overview of the latest advancements in EI technology and to offer practical insights and directions for its deployment in the power grid, ultimately contributing to the stability, reliability, and safety of the power system.
AB - The rapid development of power artificial intelligence (AI) technology have brought significant benefits to various aspects of the grid. However, a critical challenge remains: the lack of automated actuators for AI-generated strategies, which currently rely heavily on human execution. This paper addresses the need for intelligent robots in power grid applications, particularly in inspection and live-line work, to enhance operational safety and efficiency. We explore the potential implementation of embodied intelligence (EI) technology, focusing on two primary approaches: the integration of large and small models for vision, language, and action, and end-to-end visual-language-action (VLA) models. The first approach, exemplified by the PixelNav framework, demonstrates effective navigation and manipulation capabilities, while the second approach, represented by models like RT-2 and 3D-VLA, shows promise in generalization and robustness. Additionally, we discuss the application of these EI technologies in specific power grid scenarios, such as substation and distribution station inspections, and highlight the importance of developing more intelligent and adaptable robotic systems. The aim of this paper is to provide a comprehensive overview of the latest advancements in EI technology and to offer practical insights and directions for its deployment in the power grid, ultimately contributing to the stability, reliability, and safety of the power system.
KW - Artificial Intelligence
KW - Embodied Intelligence
KW - Live-line Work
KW - Power Grid
KW - Power Inspection
UR - http://www.scopus.com/inward/record.url?scp=105002714263&partnerID=8YFLogxK
U2 - 10.1007/978-981-96-3949-6_67
DO - 10.1007/978-981-96-3949-6_67
M3 - Conference Proceeding
AN - SCOPUS:105002714263
SN - 9789819639489
T3 - Lecture Notes in Networks and Systems
SP - 799
EP - 814
BT - Selected Proceedings from the 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 - Advances in Intelligent Manufacturing and Robotics
A2 - Chen, Wei
A2 - Ping Tan, Andrew Huey
A2 - Luo, Yang
A2 - Huang, Long
A2 - Zhu, Yuyi
A2 - PP Abdul Majeed, Anwar
A2 - Zhang, Fan
A2 - Yan, Yuyao
A2 - Liu, Chenguang
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 22 August 2024 through 23 August 2024
ER -