• Chih-Hung Gilbert Li

    李志鴻

     

    National Taipei University of Technology (Taipei Tech)

    國立臺北科技大學

    Graduate Institute of Manufacturing Technology

    製造科技研究所

    Industry 4.0 Laboratory

    工業4.0實驗室

  • Personal Information

    簡歷

    Education 學歷

    Ph.D. Carnegie Mellon University / Mechanical Engineering

    卡內基梅隆大學機械工程博士

    M.S. Carnegie Mellon University / Mechanical Engineering

    卡內基梅隆大學機械工程碩士

    B.S. National Tsing Hua University / Power Mechanical Engineering

    國立清華大學動力機械工程學士

    Taipei Municipal Jianguo High School

    台北市立建國中學

    Experience 經歷

    Professor / National Taipei University of Technology

    國立臺北科技大學教授

    Associate Dean / College of Mechanical and Electrical Engineering / National Taipei University of Technology

    國立台北科技大學機電學院副院長

    Chief Technology Officer / Shan Hai Recreation Limited Company

    山海休閒科技股份有限公司技術長

    Director / Office of International Affairs / Minghsin University of Science and Technology

    明新科技大學國際交流中心主任

    Director / Automated Vehicles and Equipment Development Center

    明新科技大學自動化載具與設備研發中心主任

    Chief Technology Officer / Lushridge Incorporated

    意璨精工股份有限公司技術長

    Engineering Specialist / Lord Corporation (USA)

    工程專家/美商羅德企業

  • Fields 領域

    務實 創新 合作 堅持

    Research of Industry 4.0

    工業4.0應用研究

    Artificial Intelligence, Cyber Physical System, Internet of Things, intelligent robots and vehicles

    人工智慧、網宇實體系統、物聯網、智動化機器人及交通工具

    Industry 4.0 is a collective noun. Its technologies such as the Internet of Things, big data, cloud computing, artificial intelligence, automation, etc. are revolutionizing many industries including manufacturing. It is expected that not only will much of the production and management efficiency and flexibility be significantly increased, but Industry 4.0 is also more likely to change many existing commercial and industrial operating models. Through systematic research and testing, we are committed to proposing forward-looking and innovative operating models or technologies, such as the development of intelligent service robots, related topics of human-machine collaboration, the automated personal rapid transit system, etc. to promote the advancement of technology for human well-being.

    工業4.0為一集合名詞。其所涵括之物聯網、大數據、雲端運算、人工智慧、自動化等技術,正對包括製造業在內的許多產業產生革命性的影響。預期中,不僅許多生產及管理之效率與彈性可以大幅提升,工業4.0更可能翻轉許多現有的工商業運行模式。透過系統化地研究與測試,我們志在此範疇中,提出前瞻具創見之嶄新運營模式或技術,例如智慧型服務機器人的開發、人機協作之相關課題、以及智慧自動化的個人捷運系統之開發等,以促進科技對人類福祉之提升。

     

    經濟日報報導: 華聯智科攜手北科大 開發AI自主平衡輪式機器人

     

    SELECTIVE PUBLICATIONS:

    JOURNAL

    CONFERENCE

    • Yu-Jen Li, Hsin-Hung Chen, and Chih-Hung G. Li*, "Enhancement of Visual Place Recognition for Robot Localization Subject to Pedestrian Occlusion," in Proc. 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE 2021) Lyon, France.
    • Yu-Cheng Hsu, Ming-Chang Lin, and Chih-Hung G. Li*, "Mobility Improvement on the Two-Wheeled Dynamically Balanced Robot – J4.beta," in Proc. 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE 2021) Lyon, France.
    • Chih-Hung G. Li*, Yu-Hsiang Chang, "Socially Compliant Navigation in Indoor Corridors Based on Reinforcement Learning," in Proc. 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE 2021) Lyon, France.
    • Chih-Hung G. Li*, Long-Ping Zhou, "Training End-to-End Steering of a Self-Balancing Mobile Robot Based on RGB-D Image and Deep ConvNet," in Proc. 2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2020) Boston, MA, USA.
    • Chi-Cheng Lai, Chih-Hung G. Li*, "Video-Based Windshield Rain Detection and Wiper Control Using Holistic-View Deep Learning," in Proc. 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE 2019) Vancouver, BC, Canada.
    • Yi-Feng Hong, Yu-Ming Chang, Chih-Hung G. Li*, "Real-time Visual-Based Localization for Mobile Robot Using Structured-View Deep Learning," in Proc. 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE 2019) Vancouver, BC, Canada.

    Structural Stress Analysis (Finite Element Analysis) and Optimization

    結構應力分析(有限元素分析法)與最佳化

    Structural topology optimization, nonlinear stress and strain analysis, fatigue and fracture analysis

    結構拓樸最佳化、非線性應力與應變分析、疲勞與破裂分析

    We have accumulated more than 20 years of experience in the finite element analysis. Varieties of linear or nonlinear structural stress problems were solved using the finite element software such as ANSYS. Projects include simple models such as trusses or elastic structures and more complex ones such as huge composite structures, large deformation or high strain analysis, contact and friction analysis, plastic deformation analysis, fatigue and fracture analysis, and dynamic collision analysis. In addition, by using the ANSYS APDL, projects that require large amounts of finite element analyses can be efficiently processed and completed. In the advanced design, the topology optimization design of the structure is obtained by using the artificial intelligence algorithm or the Evolutionary Structural Optimization method.

    累積個人與團隊超過20年的有限元素分析技術。舉凡各種線性及非線性之結構應力分析,透過有限元素分析軟體(如ANSYS)的運用,都可以迎刃而解。較簡單的如衍架分析與彈性結構分析等。較複雜的有超大型複合結構分析、彈性體大變形或大應變分析、接觸與摩擦分析、塑性變形分析、疲勞與破裂分析、及動態碰撞分析等。此外,透過程式自動化規劃(APDL),可以高效率處理需要大量有限元素分析的專案。在進階設計方面,運用人工智慧演算法或進化式結構拓樸最佳化法 (Evolutionary Structural Optimization)獲得結構之拓樸最佳化設計。

     

    SELECTIVE PUBLICATIONS:

    JOURNAL

    CONFERENCE

    • Chih-Hung G. Li, "Strength-based Evolutionary Structural Optimization," in Proc. 24th International Congress of Theoretical and Applied Mechanics, Montreal, Canada, 2016.

    Development of Innovative Mechanisms

    創新機構開發設計

    Guitar robot, monorail system, mechanical damper, electromagnetic actuator, bus sliding door, vehicle suspension, retractable carriage, integrated music sounding teaching device, etc.

    吉他機器人、單軌電車系統、橡膠緩衝裝置、電磁致動裝置、巴士滑門、車輛懸吊結構、可伸縮之車廂機構、整合式音樂發聲教學裝置等等

    We are committed to invention and design of patent-protected mechanisms. Previous industry-university cooperation research projects include, but are not limited to, artificial intelligence applications, smart robots, intelligent automated transport systems, novel actuators, robot mechanisms, innovative shock absorbers, various mechanical structures, and equipment with special functions for vehicles. More than 20 domestic or foreign patents have been obtained, and many have been authorized to the industry.

    我們致力於各項專利保護之創新機構發明與設計。各項產學合作研究包含但不限定於人工智慧應用、智慧型機器人、智慧自動化運輸系統、新型致動器、機器人機構、新型避震裝置、各式機械結構、及各種車輛或載具所用的特殊功能設備等等。所獲得的國內外專利達二十件以上,並有多件已授權製造與販售。

     

    SELECTIVE PUBLICATIONS:

    JOURNAL

    CONFERENCE

    • C. G. Li and H. P. Nguyen, “Development of a linearly responsive electromagnetic actuator,” presented at Int. Conf. Computer Science, Data Mining & Mechanical Eng., Bangkok, Thailand, Apr. 20–21, 2015.
  • Projects 實績

    各項產學合作研究、開發、分析計畫之成果

    Junior4: A Self-Balancing Two-Wheeled Robot Featuring Intelligent Navigation and Manipulation

    Junior4: 擁有智慧導航與操縱能力之自平衡雙輪機器人

    Research of Industry 4.0

    工業4.0應用研究

    We debut a self-balancing mobile robot with manipulators – J4.a. A displaceable mass was designed to dynamically shift the overall COG of the system like a human rider controlling the acceleration and speed of a self-balancing scooter. The displaceable mass also plays an effective role in automatically maintaining the dynamic stability, while the manipulators are at work. Installed on the top of a tall body, the manipulators are high enough to interact with people, fetch objects from the table, or operate control panels which were initially designed for human operators. Some modular autonomous capabilities have also been developed for the robot, e.g., object pick-and-place and place recognition; many were realized based on deep learning.

    我們首次發表帶有機械手的自平衡移動機器人J4.a。 在設計上賦予其一具可移動的質量來動態改變系統的整體質心,因此J4.a可以像人類乘者一樣地控制車體移動的加速度和速度。 當機械手工作時,可移動質量塊在自動保持動態穩定性方面也可發揮作用。此外,機械手安裝在高大的身體頂部,高度足以與人互動,從桌子上取放品或操作最初為人類操作員所設計的控制面板。我們也為機器人開發了一些模塊化的自主功能,例如手臂取放與環境辨識,當中許多都是基於深度學習而開發的。

    Chih-Hung G. Li*, Long-Ping Zhou, and Yu-Hua Chao, "Self-Balancing Two-Wheeled Robot Featuring Intelligent End-to-End Deep Visual-Steering," IEEE/ASME Transactions on Mechatronics (SCI), accepted, 2020.

    We built the indoor localization capability of mobile robots with a purely visual architecture. By using a plurality of cameras mounted on the robot and capturing images at multiple predetermined positions along the path, visual feature training sets were established and used to train a location classifier. Using deep learning architecture, we train the robot to recognize the global features of each position. In this test film, one can see that the robot recognizes each location when it navigates along the corridor. The code displayed at the upper left corner of the video changes from 0 to 20 in order. The overall precision rate is 92%; the recall rate is 87%.

    我們以純視覺的架構建立運動機器人的室內定點辨識能力。藉由在機器人身上裝置的多個相機,在預先規畫好的多個路徑位置上,拍攝影像供機器人學習各個場景的辨識。藉由深度學習架構我們訓練機器人認得每個位置的影像。在此測試影片中,可以看到機器人辨識出場景的代號,影片左上角由0至20順序被認出。綜合訓練所得之辨識精確率已達92%,召回率已達87%。

    Chih-Hung G. Li*, Yi-Feng Hong, Po-Kai Hsu, Thavida Maneewarn, "Real-time Topological Localization Using Structured-View ConvNet with Expectation Rules and Training Renewal," Robotics and Autonomous Systems (SCI), 2020.

    Yi-Feng Hong, Yu-Ming Chang, Chih-Hung G. Li*, "Real-time Visual-Based Localization for Mobile Robot Using Structured-View Deep Learning," in Proc. 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE 2019) Vancouver, BC, Canada.

    In this project, the team uses a depth camera to capture continuous images of the front scene while the balance car is moving fast indoors. Learning through the deep convolutional neural networks, robots are taught to react naturally like humans while encountering various conditions in the indoor environment, such as obstacles, left and right walls, moving objects, and so on. In the video, the robot performs automatic cornering; it also moves forward in corridor environments following the right wall without hitting objects. There is no need to remodel the environment to provide any guidance to the robot. It also works in a factory environment.

    在本計畫中,團隊以深度相機在平衡車機器人於室內快速移動時,拍攝前方景物之連續影像。再透過深度捲積神經網路之學習,教導機器人在面對室內環境的各種狀況,如障礙物、左右牆壁、移動物體等等時,擁有類似人類的自然反應。影像中,機器人可以自動轉彎、在走廊上循著右側牆壁直行,而不需要在環境中建置任何標籤或引導裝置。在工廠環境中亦可工作。

    Chih-Hung G. Li*, Long-Ping Zhou, "Training End-to-End Steering of a Self-Balancing Mobile Robot Based on RGB-D Image and Deep ConvNet," in Proc. 2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2020) Boston, MA, USA.

    Illumination-robust Precision Positioning for Real-time Manipulation

    光影強固性物件位置辨識與操作

    工業4.0智慧自動化

    Automated manipulation guided by a precision visual positioning system is realized based on Convolutional Neural Network (ConvNet). An automated training data generation scheme is proposed to enhance the performance of the visual positioning ConvNet under high-contrast shadows cast by other objects. Illumination templates are created by the image-to-image translation GAN (pix2pix GAN). The templates are then applied to the basis photo of the target object to spawn multiple virtual images which vastly enrich the illumination diversity of the training set for the visual positioning ConvNet. Experimental results showed that the positioning accuracy augmented by the illumination module reaches above 85%, while without the illumination-augmented training, the positioning accuracy is below 15%. Experiments on automated visual manipulation with a 5-DOF manipulator also confirmed the feasibility of adopting the proposed framework for real-time operations. With the illumination-augmented training, the manipulation success rate is above 90%; without it, the success rate is less than 40%. We provide the data used for training pix2pix GAN to generate the illumination templates at https://github.com/ntutindustry40/OneShot-II.

    我們基於卷積神經網路(ConvNet)實現了由精密視覺定位系統引導的自動化操縱。為了提高視覺定位ConvNet在高對比度陰影下的性能,我們提出了一種自動訓練數據生成的方案。藉由圖像到圖像轉換GAN(pix2pix GAN)創建照明模板,然後將模板應用於目標對象的基本照片,以生成多個虛擬圖像,從而大大豐富了視覺定位ConvNet訓練集的照明多樣性。實驗結果顯示,藉由照明模塊所提高的定位準確度可達到85%以上,相較之下在沒有進行照明增強訓練的情況下,定位準確度低於15%。我們使用5自由度機械手進行自動視覺操縱的實驗也證實了採用此實時操作框架的可行性。當採用光照增強訓練時,手臂操縱的成功率可提升至90%以上;而沒有採用光照增強訓練時,成功率不到40%。我們在https://github.com/ntutindustry40/OneShot-II提供了用於訓練pix2pix GAN以生成照明模板的數據。

    Chih-Hung G. Li *, Yi-Hao Huang, "Deep-Trained Illumination-Robust Precision Positioning for Real-Time Manipulation of Embedded Objects," The International Journal of Advanced Manufacturing Technology (SCI), 2020.

    Design and Implementation of Multi-parameter Wireless Control of Mobile Robots based on LoRa

    以LoRa設計並實現運動機器人之多參數無線控制系統

    Research of Industry 4.0

    工業4.0應用研究

    The Long Range (LoRa) long-distance and low-power communication technology has the advantages of high Interference immunity, high sensitivity, and long-distance transmission. However, its performance in robot control applications will be affected by its low data rate. In a wireless control system, the performance of control is not only based on its system calculation instructions but also affected by the data rate of wireless communication. The low data rate will result in fewer control actions; if the new action command cannot be updated, the control system will be delayed. This research focuses on the design and implementation of a wireless control system featuring LoRa on a sophisticated mobile robot. We constructed a multi-parameter real-time control system for the mobile robot based on the LoRa module. We designed a secured data transmission method and proved its effectiveness in the real-time control of the mobile robot. We also conducted a systematic LoRa parameter investigation; the results provide recommended settings for both trans-floor and non-trans-floor controls inside a building.

    在無線控制系統中,控制的性能不僅取決於自身的系統運算指令,無線通訊的性能與傳輸率的影響也頗多,兩套系統緊密影響中,低傳輸率會導致提供的控制動作變少,又或者新的動作資訊無法更新導致控制系統延遲甚大。本研究選用LoRa遠距離低功耗通訊技術建置實時多參數控制系統。LoRa擁有優良的抗噪能力、靈敏度與傳輸距離長等優點,但由於其低傳輸速率的性能,需特別設計才能建構一套在上述限制下依然能符合需求的機器人遙控系統。本系統擁有安全運作機制與即時響應控制系統,最後透過調整無線通訊的參數,可調教出在環境中運作最佳的方案。

    Workpiece Visual Placement Using Deep Learning

    深度學習之機器手工件精密定位擺放

    Deep Learning Application

    深度學習應用研究

    An automated visual positioning system is proposed for precision placement of a workpiece on the fixture. The system includes a binocular eye-in-hand system on the end effector of the mobile manipulator and a ConvNet for detecting the relative position of the workpiece based on the holistic views observed by the CMOS cameras. We train the ConvNets with training images that are automatically generated from basis images taken at the target position and annotated with the 2D coordinates of the offset locations. The ConvNet’s superior place recognition capability renders a high success rate of coordinate detection subject to high illumination and viewpoint variations. Experimental evidence of workpiece placement confirms that the low-resolution (640 × 480 pixels) camera can obtain a translational precision of ±0.2 mm; the binocular system can control the rotational error within ± 0.1°. Within the 20 × 20-mm^2 spatial tolerance of the mobile platform, the proposed system achieves a success rate of 100% in 200 workpiece placement tasks. The entire workpiece placement task can be completed in 60 s; the average elapsed time of precision positioning and placement is less than 20 s, with a total of 4 visual positioning steps.

    在此我們提出了一種自動視覺定位系統,可用於將工件精確地放置在夾具上。該系統包括一個位於移動手臂末端執行器上的雙目手眼系統和一個卷積神經網路,使用基於CMOS攝影機觀察到的整體視圖來檢測工件的相對位置。我們用訓練圖像訓練卷積神經網路,而訓練圖像是根據在目標位置拍攝的基礎圖像自動生成的,程式並自動用偏移位置的2D座標加以標註。卷積神經網路出色的位置識別功能可在高光照和視點變化的情況下提高座標檢測的成功率。工件放置的實驗證據證實,以低分辨率(640×480像素)相機即可實現±0.2 mm的平移精度;雙目鏡系統可以將旋轉誤差控制在±0.1°之內。在移動平台的20×20 mm^2空間公差範圍內,我們的系統在200個工件放置任務中實現了100%的成功率。整個工件放置任務可在60 秒內完成;精確定位和放置的平均時間少於20秒。

    Chih-Hung G. Li*, Yu-Ming Chang, "Automated visual positioning and precision placement of a workpiece using deep learning," The International Journal of Advanced Manufacturing Technology (SCI), 2019.

    Holistic-view Deep Learning for Automatic Windshield Wiper Activation

    雨刷自動控制之全景深度學習

    Deep Learning Application

    深度學習應用研究

    A windshield rain detection system using holistic-view deep learning is constructed in this project. A wiper control algorithm based on a time-series treatment is also presented. The video images of ordinary driving recorders were used to train a deep convolutional neural network for wiper activation classification. Overall, we achieved an average precision rate of 0.88 in our video-based rain detection experiments; our recall rate of 0.87 is significantly higher than the state-of-the-arts that averaged around 0.6. It is also proved that the proposed system is practical for real-time vehicle windshield rain detection and wiper control. In this film, a blue square indicates that our detection system recommends that the wiper should activate, and a yellow circle indicates it should not.

    在本專案中我們構建了採用整體視覺深度學習的擋風玻璃雨水探測系統,還提出了一種基於時間序列處理的雨刷控制算法。我們使用普通行車記錄儀的視頻圖像以訓練深度卷積神經網絡,以進行雨刷啟動分類。總體而言,我們在基於視頻的雨水檢測實驗中實現了0.88的平均精確率; 我們的召回率達到0.87,亦明顯高於平均約為0.6的現有一般水平。我們所提出的系統亦證明了實時車輛擋風玻璃雨水檢測和雨刷控制是可行的。在此影片中,藍色方塊表示我們的偵測系統建議雨刷應作動,而黃色圓圈表示雨刷不應作動。

    Chih-Hung G. Li*, Kuei-Wen Chen, Chi-Cheng Lai, and Yu-Tang Hwang, “Real-time rain detection and wiper control employing embedded deep learning,” IEEE Transactions on Vehicular Technology, vol. 70, no. 4, pp. 3256-3266, 2021.

    Chi-Cheng Lai, Chih-Hung G. Li*, "Video-Based Windshield Rain Detection and Wiper Control Using Holistic-View Deep Learning," in Proc. 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE 2019) Vancouver, BC, Canada.

    Real-time Object Coordinate Detection Using Deep Learning

    深度學習之物件實時座標偵測

    Deep Learning Application

    深度學習應用研究

    We use a deep learning framework for training object coordinate detection based on a single basis photo. As shown in the video, to train a coordinate detection scheme of a specific object, it only takes about 4 minutes on an ordinary notebook PC from taking the basis photo to completing the deep learning process.

    我們使用深度學習框架,只需基於一張基礎照片,即可訓練物件之座標位置偵測。如影片所示,訓練某特定物件之座標偵測,從開始拍攝一張基礎照片到完成全部深度學習,只需在一台普通筆記型電腦上運算約4分鐘。

    Development of Intelligent and Automated Personal Rapid Transit (PRT)

    智動化個人捷運系統開發

    休閒科技股份有限公司合作開發案

    現代休閒社區的生活藍圖中,藉由整合相關的服務、網通、運輸、商業、物流、智慧與自動化等理念與科技,得以實現絕對休閒生活的目標。其中最為特別的是,在此社區規劃中,單軌個人捷運將肩負起整個休閒社區的運輸動靜脈。物聯網將成為各地的眼耳,串聯起感應各個脈動的神經。大數據將成為資料與訊息的寶庫,成為許多生活或商業活動理性判斷的基礎。而人工智慧則是社區的大腦,自動無誤地管理社區內日常生活的大小事。這些科技將具體實現並活絡整體的人流、物流、商流、與資訊流之智慧自動化,使得絕對休閒社區生活的理念得以實現。

     

    在本開發案中,團隊從零開始,設計了單軌電車的車體架構、動力系統、控制系統、與機電配置等,並開發了世界第一套專利的單軌車快速換軌系統,各車可以在十秒鐘之內接連通過換軌點而不需等待。驅動與軌道系統並可適應高低起伏的地形,特別適合應用於山林保護區的代步系統。輕量化的高架單軌系統最大限度地降低對環境的破壞,並可提供乘坐者接近大自然的舒適體驗。

    Chih-Hung Li*, Zong Jun Lu, "An Innovative Straddle Monorail Track Switch Design for the Personal Rapid Transit," International Journal of Heavy Vehicle Systems, 28(3), 370-384 (SCI), 2021.

    Innovative Soft Actuator

    創新軟致動器

    基礎元件開發計畫

    In this research project, we have successfully developed an innovative electromagnetic actuator, which not only has the characteristics of quietness and softness, but also has the features of simple structure, moderate power, easy control, and low cost, ideal for applications such as the robots and automated machines that require superior quietness or human-machine collaboration. The specially designed tapered elastomer provides a highly nonlinear elastic response that achieves force equilibrium with the highly nonlinear electromagnetic force at various displacements and voltages. This actuator has the special characteristics that the input voltage is linearly proportional to the output displacement, and thus the motion control can be easily performed using a simple open circuit.

    本研究計畫成功開發出一種創新之電磁式致動器。不但具有靜音與軟的特點,並擁有構造簡單、力量適中、控制容易、與造價低廉等特性。非常適合應用在需要靜音或等需要人機協作之安全機器人或自動化機械上。特殊設計的錐形彈性體可發揮高度非線性彈性之功能,與高度非線性的電磁力在各個位移與電壓下達到力平衡。使得本致動器具有輸入電壓與輸出位移呈線性正比的特性,可輕易使用簡單之開迴路就能做運動控制。

    C. G. Li and H. P. Nguyen, “Development of a linearly responsive electromagnetic actuator,” presented at Int. Conf. Computer Science, Data Mining & Mechanical Eng., Bangkok, Thailand, Apr. 20–21, 2015.

    Quiet Guitar Robot

    靜音吉他機器人

    先導型研究計畫

    In order to solve the noise problems often associated with robots or automation equipment, we developed an innovative silent electromagnetic actuator. In addition to providing silent linear actuation, simple control of linear voltage response was also achieved. In this project, a guitar robot was created; the experimental evidence has shown that the mechanical noise of the guitar robot is much lower than that of conventional actuators such as pneumatic cylinders, servo motors, stepping motors, solenoids, et al., and is much lower than the guitar sound itself.

    為了解決機器人或自動化設備經常伴隨的噪音問題,本研究計畫開發了一種創新的靜音電磁致動器。除了可提供安靜無聲的線性致動,並具備線性電壓立即反應的簡單控制功能。為了展現此新型致動器之優點,本計畫特地創作了一具吉他演奏機器人。經由實驗證明,該吉他機器人之機械噪音遠低於一般傳統致動器如氣壓缸、伺服馬達、步進馬達、或電磁閥等所製造的噪音,並遠低於吉他本身所發出之樂音,因此得以彈奏出純淨之樂音。

    Chih-Hung G. Li*, Ming-Chang Lin, Basil A. Bautista, and Bettina E. To, "A Low-Noise Guitar Robot Featuring a New Class of Silent Actuators," IEEE ASME Transactions on Mechatronics (SCI), 2019.

    C. G. Li and B. P. Bautista, “On the compression of a stack of truncated elastomeric cones as a nonlinearly responsive spring,” Mech. Res. Commun, vol. 69, pp. 146–149 (SCI), 2015.

    中華民國發明專利 / 可撥弦之機械手指裝置 / 發明人李志鴻、包提達巴希爾 / 2017 / I582752

    Design Optimization for Monorail Chassis Structure

    單軌車架結構最佳化設計

    休閒科技股份有限公司合作開發案

    本案應用進化式結構最佳化(ESO)分析技術,對單軌車架進行最佳化設計。首先規劃出車架設計範圍,再運用進化式結構最佳化分析的精神,逐一去除較不重要的材料,而精煉成型出最佳之車架結構設計。由於車架在車輛運行中,無可避免會經歷加減速及左右轉等動態負載,因此本計畫特地著重於在計算中,反映出所有複雜的動態負載,以獲得可承受所有這些負載的綜合最佳化設計。此設計將有效減低車體重量,大量節省車輛運行所耗費的能量,成為環保節能的示範設計。

    Chih-Hung G. Li*, "Design of the lower chassis of a monorail personal rapid transit (MPRT) car using the evolutionary structural optimization (ESO) method," Structural and Multidisciplinary Optimization, 54 (1): 165-175 (SCI), 2016.

    Analysis and Testing of Huge Equipment in Amusement Parks

    大型遊戲設備測試與分析

    產學合作案

    • 代表作品1:本案對摩天輪模型進行高速風洞測試,量測模型在各級風速下之受力,以預測真實的120米摩天輪是否能承受預定之風力。本實驗室製作一座二百分之一的摩天輪模型置於風洞中接受試驗,並透過相似性理論推測出實體摩天輪所可能承受之力量。最後用有限元素分析估算在各級風力下摩天輪主結構所承受之應力程度,並確認摩天輪主結構之強度。
    • 代表作品2:超大型遊戲設備飛行平台具有六軸自由度,且極限操作項目達三百餘種,再加上其架構非常複雜,因此各部位結構之應力與疲勞分析難度相當高。本專案以有限元素分析軟體建構擁有一千七百萬元素之模型,並透過自行撰寫之分析自動化程式,使得本分析得以在短時間內完成。

    Patented Automobile Suspension Strut Featuring Constant Frequency

    專利車用定頻懸吊柱

    休閒科技股份有限公司合作開發案

    為了符合單軌個人捷運電車輕量化的需求,並提升乘坐的舒適度,特別成立本計畫,以開發一款不受乘客人數與載重影響,而能自動維持在舒適彈跳頻率的懸吊柱。捨棄傳統金屬彈簧的設計,本設計採用彈性體高度非線性的特性,利用大變形與接觸有限元素分析,成功地設計出具有高度彈性變異性的懸吊柱。並透過模型製作與測試,驗證所預定的性能。因此,即便車體淨重極低,而不論載重多少,車輛運行時都可自適應地維持在舒適的彈跳頻率。本案已獲得專利並與相關廠商洽談技術授權中。

    Chih-Hung G. Li*, "A novel suspension strut featuring constant resonance frequency," International Journal of Heavy Vehicle Systems, 22 (4): 293-310 (SCI), 2015.

    Fatigue Analysis of High Speed Pump Shaft高速幫浦轉軸疲勞分析

    知名高速幫浦製造廠

    本分析專案使用有限元素分析法,進行一系列轉軸外型對應力分佈影響之探討,並據以進行疲勞分析。該疲勞分析不僅證實疲勞破裂的產生位置,並提供了後續幾何外型改善的建議方案,幫助合作廠商釐清與改善問題。

    Patented Retractable Carriage Design

    專利車廂伸縮機構

    特殊用途車輛開發專案

    為了滿足本案業主對伸縮車廂的設計需求,在該伸縮車廂的機構開發中,特別著重於伸縮車廂展開後,地板的平整無落差,以及收縮後,地板的有效收納。伸縮車廂結構與支撐結構,以及所有相關零組件的強度與可能變形,亦透過有限元素分析法及理論公式的計算與分析加以驗證。該成果已獲得專利保護,並技術授權予業主製造。

    中華民國新型專利 / 可伸縮之車廂機構 / 發明人李志鴻 / 2015 / M496590

    Improvement on the brim profile of thick spin-coating layers

    厚層旋轉塗佈邊緣厚度改善方案

    知名藍光碟片製造商

    本計畫針對高厚度塗佈層邊緣易堆積,而產生厚度不均之現象,進行量測與分析,並提出改善方案。透過對塗料升溫可降低其黏滯性與表面張力的原理,對邊緣進行局部加溫,以降低塗料堆積的現象。我們運用有限元素分析對邊緣加溫的暫態反應進行計算,以掌握加溫功率對溫度變化的影響。並製作實驗驗證了邊緣局部加溫,對去除堆積現象所獲得的效果。

    Patented bus slide door

    專利巴士滑門開發

    國科會補助產學合作案

    本開發案針對巴士門的開關方式進行安全檢討,並獲得緊貼平移為最安全設計的結論。在此基礎上,本案對巴士門進行結構改善設計,除了利用滑軌與連桿建立緊貼平移的運動方式,並將動力傳動機構放置於門後之可利用空間內。該計畫獲得科技部產學合作研究補助。本案獲得美國及台灣在內之多國專利,並已技術授權予合作業者進行生產與銷售。

    美國發明專利 / Longitudinal-Slide Door Controlling Mechanism /發明人Chih-Hung Li / 2012 / US 8292349

    中華民國新型專利 / 巴士之橫移式門體連動機構 / 發明人李志鴻 / 2011 / M418828

    Patented lub-rubber dampers

    專利橡潤式緩衝棒開發

    櫃門滑軌與鉸練製造商

    櫃門的緩衝多半運用油壓的原理設計緩衝棒。多家國際知名五金製造商均有供應類似產品。然而油壓式的設計經常遭遇漏油的問題,並可能汙染居家環境造成困擾。基於除去此漏油問題的理念,本專案開發了專利的橡膠潤滑式緩衝棒,並取得包含美國、大陸、台灣等多國專利。由於緩衝機制來自於潤滑的橡膠而非油壓,因此完全除去漏油的問題。在開發過程中,本案大量運用有限元素分析橡膠件的接觸壓力與變形,使用非線性材料模式與非線性求解過程,以精確計算出相關數據,並透過實體模型之耐久性能測試,證實此產品之實用性。部分產品設計已技術授權予相關廠商進行量產銷售。

    美國發明專利 / Cabinet Door Buffer Bar / 發明人Chih-Hung Li / US 7076834

    中華民國發明專利 / 緩衝棒 / 發明人李志鴻 / 2003 / 538202

    中華民國發明專利 / 櫃門緩衝棒 / 發明人李志鴻 / 2004 / I225533

    活動剪影

  • For prospective graduate students

    欲加入本實驗室之研究生請看這裏

    1

    Contact

    We welcome motivated prospective graduate students to join us every year. However, due to the capacity of the laboratory, only those most qualified can be admitted. Thus, I suggest whoever interested should act as soon as possible after receiving the formal admission notification from the graduate institutes of mechatronic engineering or manufacturing technology by sending me an email for scheduling an interview. For information of the laboratory, please refer to this website.

    2

    Interview

    An interview will be given to prospective graduate students individually or in groups. Candidates may prepare documents or certificates for demonstrating his/her professional capabilities. However, those who exhibit the following characteristics are usually more likely to succeed in the lab and thus may be admitted: 1. good verbal communication and writing skills in Mandarin and English, 2. enthusiasm in learning new things, 3. a love for hands-on works, e.g., machine assembly or computer programming, and 4. impeccable professional ethics.   

    3

    Announcement

    After the interview, announcement of acceptance will be made by email. Those who are admitted will immediately be asked to make a final decision on joining the lab. If one opts to give up the opportunity, it will be reserved for the next round of selection. There may be a few rounds of selection before the quota have been filled. Please note that financial support is not guaranteed and only provided when available at admission. For international students, please refer to the office of international affairs for information on grants and scholarships.

  • Contact

    聯繫我們

    Welcome to contact us regarding your need for collaborative R&D or engineering service.

    歡迎您與我們聯繫任何產學合作或工程服務的需求。

    實驗室網址:http://site-1287590-1324-5334.strikingly.com/

    台北市忠孝東路三段一號 綜科館714-2室
    +886-2-2771-2171 ext. 2092