@inproceedings{b1b39237890041cca8e241b6c8978730,
title = "Adaptive-Hierarchical-Mechanism-based Grinding Planning with Experience Reuse",
abstract = "In order to make industrial robotic arms accomplish the uniform grinding task autonomously with high computational efficiency as well as minimal path cost, an adaptive-hierarchical-mechanism-based grinding path planning framework is designed in this paper to achieve uniform grinding for tee tube of arbitrary size, spatial orientation and surface characteristic. The Adaptive-Hierarchical-Mechanism(AHM)-based path cost estimation in the framework is designed to reduce computational complexity of the whole planning framework significantly by adaptively making all points grouped evenly. To enhance the optimization capability in grinding path planning in terms of convergence rate and optimal solution quality, Experience-Reuse-Ant-Colony-System(ERACS)-based grinding path planning is designed to reuse existing optimal sorting experience. Compared to the state-of-the-art algorithms, the quantitative performance advantages of planning framework proposed are validated by the experimental results. Specifically, performance advantages are at least 43.65\% and 29.21\% in terms of computational complexity and convergence rate respectively.",
keywords = "adaptive hierarchical, cost estimation, experience reuse, grinding path planning, optimal sorting, robotic arm",
author = "Ningyuan Wang and Yuemeng Ma and Qiang Wang",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2025 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2025 ; Conference date: 19-05-2025 Through 22-05-2025",
year = "2025",
doi = "10.1109/I2MTC62753.2025.11079161",
language = "英语",
series = "Conference Record - IEEE Instrumentation and Measurement Technology Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2025 - Proceedings",
address = "美国",
}