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222 results on '"Huang, Guoming"'

1. Learn Once Plan Arbitrarily (LOPA): Attention-Enhanced Deep Reinforcement Learning Method for Global Path Planning

Author

Huang, Guoming, Hou, Mingxin, Yuan, Xiaofang, Huang, Shuqiao, and Wang, Yaonan

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Robotics
Abstract

Deep reinforcement learning (DRL) methods have recently shown promise in path planning tasks. However, when dealing with global planning tasks, these methods face serious challenges such as poor convergence and generalization. To this end, we propose an attention-enhanced DRL method called LOPA (Learn Once Plan Arbitrarily) in this paper. Firstly, we analyze the reasons of these problems from the perspective of DRL's observation, revealing that the traditional design causes DRL to be interfered by irrelevant map information. Secondly, we develop the LOPA which utilizes a novel attention-enhanced mechanism to attain an improved attention capability towards the key information of the observation. Such a mechanism is realized by two steps: (1) an attention model is built to transform the DRL's observation into two dynamic views: local and global, significantly guiding the LOPA to focus on the key information on the given maps; (2) a dual-channel network is constructed to process these two views and integrate them to attain an improved reasoning capability. The LOPA is validated via multi-objective global path planning experiments. The result suggests the LOPA has improved convergence and generalization performance as well as great path planning efficiency.

Published
2024

2. Deep Reinforcement Learning-based Multi-objective Path Planning on the Off-road Terrain Environment for Ground Vehicles

Author

Huang, Shuqiao, Wu, Xiru, and Huang, Guoming

Subjects
Computer Science - Robotics, Computer Science - Artificial Intelligence
Abstract

Due to the vastly different energy consumption between up-slope and down-slope, a path with the shortest length on a complex off-road terrain environment (2.5D map) is not always the path with the least energy consumption. For any energy-sensitive vehicle, realizing a good trade-off between distance and energy consumption in 2.5D path planning is significantly meaningful. In this paper, we propose a deep reinforcement learning-based 2.5D multi-objective path planning method (DMOP). The DMOP can efficiently find the desired path in three steps: (1) Transform the high-resolution 2.5D map into a small-size map. (2) Use a trained deep Q network (DQN) to find the desired path on the small-size map. (3) Build the planned path to the original high-resolution map using a path-enhanced method. In addition, the hybrid exploration strategy and reward shaping theory are applied to train the DQN. The reward function is constructed with the information of terrain, distance, and border. Simulation results show that the proposed method can finish the multi-objective 2.5D path planning task with significantly high efficiency. With similar planned paths, the speed of the proposed method is more than 100 times faster than that of the A* method and 30 times faster than that of H3DM method. Also, simulation proves that the method has powerful reasoning capability that enables it to perform arbitrary untrained planning tasks., Comment: 9 pages,8 figures

Published
2023

5. Deep Reinforcement Learning-Based Multi-objective 3D Path Planning for Vehicles

Author

Huang, Shuqiao, Wu, Xiru, Huang, Guoming, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Jia, Yingmin, editor, Zhang, Weicun, editor, Fu, Yongling, editor, and Wang, Jiqiang, editor

Published
2023
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18. Dual‐Responsive Turn‐On T1 Imaging‐Guided Mild Photothermia for Precise Apoptotic Cancer Therapy

Author

Song, Sijie, primary, Wang, Qi, additional, Xie, Jiangao, additional, Dai, Junduan, additional, Ouyang, Dilan, additional, Huang, Guoming, additional, Guo, Yuheng, additional, Chen, Chen, additional, Wu, Mengnan, additional, Huang, Tingjing, additional, Ruan, Jingwen, additional, Cheng, Xiaofeng, additional, Lin, Xucong, additional, He, Yu, additional, Rozhkova, Elena A., additional, Chen, Zhaowei, additional, and Yang, Huanghao, additional

Published
2023
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23. Contributors

Author

Cai, Zhixiong, primary, Cao, Junji, additional, Chen, Xi, additional, Chen, Xiaofeng, additional, Chen, Xiaomei, additional, Ding, Longjiang, additional, Duan, Wei, additional, Dunn, Andrew, additional, Fang, Yaning, additional, Hou, Li, additional, Huang, Yipeng, additional, Huang, Guoming, additional, Huang, Mengjiao, additional, Huang, Yu, additional, Huang, Rujin, additional, Li, Feiming, additional, Li, Baichuan, additional, Li, Mengmeng, additional, Lian, Ying, additional, Lin, Liping, additional, Lin, Chunshui, additional, Lin, Xucong, additional, Lin, Jou, additional, Lu, Chun-Hua, additional, Pan, Yi, additional, Rong, Mingcong, additional, Shi, Donglu, additional, Wang, Xiaoru, additional, Wang, Weijie, additional, Wang, Xu-dong, additional, Wang, Wei, additional, Wei, Liming, additional, Wu, Xiaoping, additional, Xie, Zenghong, additional, Xu, Wei, additional, Xue, Yitong, additional, Yang, Pengyuan, additional, Yang, Huang-Hao, additional, Yang, Chaoyong, additional, Yao, Qiuhong, additional, Yi, Changqing, additional, Zeng, Jingbin, additional, Zhang, Wei, additional, Zhang, Xiaoai, additional, Zhang, Yinglu, additional, Zhang, Yufei, additional, Zhao, Yuan, additional, and Zhu, Lin, additional

Published
2019
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25. A Telomerase-Activated Magnetic Resonance Imaging Probe for Consecutively Monitoring Tumor Growth Kinetics and In Situ Screening Inhibitors

Author

Dai, Junduan, primary, Liu, Zheng, additional, Wang, Lili, additional, Huang, Guoming, additional, Song, Sijie, additional, Chen, Chen, additional, Wu, Ting, additional, Xu, Xiao, additional, Hao, Chaojie, additional, Bian, Yijie, additional, Rozhkova, Elena A., additional, Chen, Zhaowei, additional, and Yang, Huanghao, additional

Published
2023
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27. Deep Reinforcement Learning-Based 2.5D Multi-Objective Path Planning for Ground Vehicles: Considering Distance and Energy Consumption.

Author

Wu, Xiru, Huang, Shuqiao, and Huang, Guoming

Subjects
ENERGY consumption, REINFORCEMENT learning, DEEP reinforcement learning
Abstract

Due to the vastly different energy consumption between up-slope and down-slope, a path with the shortest length in a complex off-road terrain environment (2.5D map) is not always the path with the least energy consumption. For any energy-sensitive vehicle, realizing a good trade-off between distance and energy consumption in 2.5D path planning is significantly meaningful. In this paper, we propose a deep reinforcement learning-based 2.5D multi-objective path planning method (DMOP). The DMOP can efficiently find the desired path in three steps: (1) transform the high-resolution 2.5D map into a small-size map, (2) use a trained deep Q network (DQN) to find the desired path on the small-size map, and (3) build the planned path to the original high-resolution map using a path-enhanced method. In addition, the hybrid exploration strategy and reward-shaping theory are applied to train the DQN. The reward function is constructed with the information of terrain, distance, and border. The simulation results show that the proposed method can finish the multi-objective 2.5D path planning task with significantly high efficiency and quality. Also, simulations prove that the method has powerful reasoning capability that enables it to perform arbitrary untrained planning tasks. [ABSTRACT FROM AUTHOR]

Published
2023
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34. Systematic review/Meta Analysis of Moxibustion in the Treatment of Functional Constipation based on Radar Plot Multivariate Evaluation of Literature Quality

Author

Mao, Qiang, primary, Liu, Huilin, additional, Luo, Guofeng, additional, Li, Peiling, additional, Wu, Desheng, additional, Zhang, Xiaoming, additional, Zhou, Xingchen, additional, Yang, Yanan, additional, Huang, Guoming, additional, Li, Ziru, additional, Zhou, Shuisheng, additional, Yang, Rui, additional, Xiao, Qi, additional, Jiao, Lin, additional, and Chi, Zhenhai, additional

Published
2022
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41. Design and Fabrication of a Novel Dual-Frequency Confocal Ultrasound Transducer for Microvessels Super-Harmonic Imaging

Author

Tingnian Xu, Qinghao Li, Liyang Luo, Huang Guoming, Xinqi Deng, Jun Ou-Yang, Mingxing Xie, Yue Zhao, Zhengjie Wu, Benpeng Zhu, and Xiaofei Yang

Subjects
Materials science, Fabrication, Microbubbles, Acoustics and Ultrasonics, business.industry, Phantoms, Imaging, Confocal, Image (category theory), Transducers, Second-harmonic imaging microscopy, Piezoelectricity, Imaging phantom, Optics, Transducer, Microvessels, Ultrasonic sensor, Electrical and Electronic Engineering, business, Instrumentation, Ultrasonography
Abstract

Recently, super-harmonic ultrasound imaging technology has caused much attention due to its capability of distinguishing microvessels from the tissues surrounding them. However, the fabrication of a dual-frequency confocal transducer is still a challenge. In this work, 270- $\mu \text{m}$ PMN-PT single crystal 1–3 composite and 28- $\mu \text{m}$ PVDF thick film, acting as transmission layer and receiving layer, respectively, are integrated in a novel co-focusing structure. To realize delicate wave propagation control, microwave transmission line theory is introduced to design such structure. Two acoustic filter layers, 13- $\mu \text{m}$ copper layer and 39- $\mu \text{m}$ Epoxy 301 layer, are indispensable and should be added between two piezoelectric layers. Therefore, an acoustic issue can be overcome via an electrical method and the successful achievement of a dual-frequency (5 MHz/30 MHz) ultrasound transducer with a confocal distance of 8 mm can be realized. The super-harmonic ultrasound imaging experiment is conducted using this kind of device. The 3-D image of 110- $\mu \text{m}$ -diameter phantom tube injected with microbubbles can be obtained. These promising results demonstrate that this novel dual-frequency (5 MHz/30 MHz) confocal ultrasound transducer is potentially usable for microvascular medical imaging application in the future.

Published
2020

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