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/ 2023 Achievements /

 光機電技術研發中心研究成果 
Research Achievements of Opto-Mechatronics Technology Center

內建荷重計的創新型球擠光工具​

Innovative Ball Burnishing Tool Embedded with a Load Cell

  • 由修芳仲教授帶領光機電中心研究團隊完成研製，已整合於機械系五軸加工中心，並成功進行工件的球擠光及球拋光表面精加工研究。

  • Professor Fang-Jung Shiou led the research team at the Opto-Mechatronics Technology Center in developing and integrating an innovative ball burnishing tool into a five-axis machining center. Their research successfully explored surface finishing techniques for workpieces using ball squeezing and polishing.

  • 專利申請已於112年11月7日通過臺科大技轉中心，專利案號112142895。

  • The patent application was approved by the Technology Transfer Center of NTUST on November 7, 2023, with the patent number 112142895.

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此圖為擠光工具系統整合於五軸加工機

This figure shows the integration of the polishing tool system into a five-axis machining center.

 智慧型機器人研究中心研究成果 
Research Achievements of Center for Intelligent Robotics

使用觸覺回饋開發機械手臂於手術環境之應用​

Development of Haptic Feedback-Enabled Robotic Arm for Surgical Environment Applications

  • 顏家鈺校長專注於「使用觸覺回饋開發機械手臂於手術環境之應用」之研發，透過蒙特卡羅演算法和機器學習修正卡布希演算法，成功在受限空間內精準穿刺病灶，降低操作者對準所需時間。

  • Principal Jia-Yush Yen focuses on the research and development of "Development of Haptic Feedback-Enabled Robotic Arm for Surgical Environment Applications." Through Monte Carlo algorithms and machine learning to refine the Caboshi algorithm, the team successfully achieves precise lesion puncture in confined spaces, reducing the time required for operators to align.

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此圖為研究中確認搓到病灶點的狀況

This figure depicts the confirmation of reaching the lesion point during the research.

高負載自主移動機器人

High PayLoad Autonomous Mobile Robot (HAMR)

  • 林柏廷教授專注於「高負載自主移動機器人」的研發，成功開發高負載機器人可有效負載70公斤，透過多點定位量測ArUco碼，實現HAMR精確定位，並使用差分演算法進行機械手臂的高效取物操作。

  • Professor Po Ting Lin focuses on the research and development of "High PayLoad Autonomous Mobile Robot." He has successfully developed a high payload robot capable of efficiently carrying a payload of 70 kilograms. Through multi-point positioning measurement of ArUco codes, precise localization of HAMR is achieved, enabling efficient pick-and-place operations of robotic arms using differential algorithms.

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此圖為使用DIC進行高負載自主移動機器人位移量的實驗架設

This figure shows the experimental setup for measuring displacement using DIC in the high payload autonomous mobile robot.

人機協作的適應性避障技術

Adaptive Obstacle Avoidance Technology for Human-Robot Collaboration

  • 林柏廷教授專注於「人機協作的適應性避障技術」的研發，成功開發一套人機協作避障系統，利用最佳化演算法迭代運算干涉路徑，縮短路徑規劃時間。

  • Professor Po Ting Lin focuses on the research and development of "Adaptive Obstacle Avoidance Technology for Human-Robot Collaboration." He has successfully developed a human-robot collaboration obstacle avoidance system that utilizes optimization algorithms to iteratively compute interference paths, thus reducing path planning time.

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此圖為機械手臂動態避障的分析結果

This figure shows the analysis results of dynamic obstacle avoidance by the robotic arm.

 物聯網創新中心研究成果 

Research Achievements of Center for IOT Innovation

設計自動導引車路線及交通控制策略

Designing Automated Guided Vehicle Routes and Traffic Control Strategies

  • 周碩彥教授的研究團隊透過簡單的路由規則來改善分配過程。研究中主要聚焦於避免碰撞、及檢測自動導引車的周圍環境等問題。藉由曼哈頓距離，可以使用許多數量的AGV和Pod來完成分配，從而使分配過程更加有效。透過偵測路口的交通狀況並儲存資訊來實現交通控制和協調。與 FCFS 方式移動相比，該策略將交通擁堵次數顯著減少11.4%。

  • Professor Shuo-Yan Chou's research team improves distribution using simple routing rules. The study focuses on collision avoidance and detecting the surrounding environment of AGVs. Using Manhattan distance, numerous AGVs and Pods efficiently complete the allocation process. Traffic control and coordination are achieved by detecting traffic conditions at intersections and storing information. Compared to FCFS movement, this strategy reduces traffic congestion significantly by 11.4%.

即時動作識別系統

Real-time Action Recognition System

  • 周碩彥教授的研究團隊開發了一款輕量級的即時動作識別系統，採用深度學習模型處理多目標動作識別，同時利用目標檢測和追蹤演算法實現多人動作識別，提升準確度並大幅縮短執行時間。研究成果表明，所提出的模型比基礎模型實現了快1.6倍的執行時間和快1.1倍的訓練時間，而沒有犧牲太大的準確性。

  • Professor Shuo-Yan Chou's research team developed a lightweight real-time action recognition system using deep learning models for multi-target action recognition. Target detection and tracking algorithms are utilized for multi-person action recognition, improving accuracy and significantly reducing execution time. The study findings indicate that the proposed model achieves 1.6 times faster execution time and 1.1 times faster training time compared to the baseline model, without sacrificing accuracy too much.

即時動作識別系統

Real-time Action Recognition System

  • 周碩彥教授的研究團隊致力於解決製造業碳排放所帶來的挑戰。他們的研究主要包括設計數據結構、數據匯總器和數據存儲，以跟蹤和管理碳排放數據，確保其完整性和安全性。此外，為了應對氣候變化的緊迫性，他們提出了整合區塊鏈技術的解決方案，以提高排放交易系統的透明度和可靠性。同時，他們開發了Simheuristics框架，這是一種結合機器學習演算法和自適應粒子群優化的系統動力學方法，旨在優化能源結構，以實現能源供應的可持續發展。該研究的重要結果之一是發現，改進後的自適應粒子群優化算法(APSO)在解決能源結構問題方面性能明顯優於原始粒子群優化算法(PSO)。他們的研究還進行了針對美國不同情境的實驗，揭示了不同策略下能源結構調整的成本和排放效果。

  • Professor Shuo-Yan Chou's research team is dedicated to addressing the challenges posed by carbon emissions in the manufacturing industry. Their research primarily involves designing data structures, data aggregators, and data storage to track and manage carbon emission data, ensuring its integrity and security. Additionally, to address the urgency of climate change, they propose an integrated solution utilizing blockchain technology to enhance the transparency and reliability of emission trading systems. Moreover, they have developed the Simheuristics framework, a system dynamics approach combining machine learning algorithms and Adaptive Particle Swarm Optimization (APSO) to optimize energy structures for sustainable energy supply. One of the key findings of their research is the significant performance improvement of the enhanced APSO algorithm in addressing energy structure problems compared to the original Particle Swarm Optimization (PSO) algorithm. Their study also conducted experiments in different scenarios in the United States, revealing the cost and emission effects of energy structure adjustments under various strategies.

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