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標題: | 基於位置與力複合誤差控制之雙機器手臂協同持物操作與學習演算法之應用 A Control Strategy for Dual-arm Object Manipulation Based on Fused Force/Position Errors and Learning Algorithms |
作者: | Bo-Hsun Chen 陳柏勳 |
指導教授: | 林沛群(Pei-Chun Lin) |
關鍵字: | 機器手臂,雙手機器人,雙機器手臂,雙手操作,力控制,卡曼濾波器,持物移動,迭代學習控制,強化學習,PPO, dual manipulators,dual arm robot,dual robot arm,dual-arm manipulation,Kalman filter,force control,iterative learning,reinforcement learning,proximal policy optimization, |
出版年 : | 2018 |
學位: | 碩士 |
摘要: | 在工業4.0中,關節型機器手臂在智慧化產線扮演關鍵角色,而雙手臂機器人具有許多優勢,包含更多的自由度、可以更牢固地抓取大型加工件。要操作雙手機器人,多種量測資訊是必要的,尤其是力回授。然而,過去較少有研究關於結合位置與力回授資訊,也較少有用單獨位置控制之機器手臂組成雙機器手臂系統,因此本研究嘗試用卡曼濾波器混合位置與力誤差回授以估測力資訊;並且由兩台獨立的位置控制機器手臂組成雙手機器人執行實驗,以符合實際工廠的需求。
雙手協同持物移動操作部分,提出了受控體之彈簧-阻尼-慣質模型的線性系統假設與識別方法,並基於此模型發展了可以結合力與位置誤差以估測混合力誤差的卡曼濾波器,以及將夾爪不同的夾持型態視為不同模型參數但為同一個模型的假設。接著透過實驗識別系統參數、檢驗卡曼濾波器、比較卡曼濾波器的優勢,並透過夾持不同物體沿著空間中的8字軌跡移動以驗證控制器架構的可行性。 學習演算法應用方面,在完整地介紹機器學習、強化學習與迭代學習控制的基礎知識後,將迭代學習控制運用於雙手協同持物移動的任務上,透過數學證明與實驗驗證改善。接著介紹PPO強化學習演算法與4個模擬範例,將一般常當作實時控制器的用法轉化成當作半即時輔助軌跡規劃器的用法,並透過單手推彈簧追力軌跡實驗與單手一維以及二維力控制打磨任務,驗證架構的可行性。 In Industry 4.0, the articulated robot arm plays an important role in intellectual manufacturing, and among robot arms, the dual arm system has many advantages, such as more degree of freedom and more firm grasping of large pieces. To operate dual arm robots, it needs multiple feedback especially force. However, there were few research about combining position and force feedback, and few using two independent position-controlled manipulators to compose the system. So in this thesis, Kalman filter is proposed to fuse position and force error measurement to estimate force feedback, and two independent position-controlled manipulators compose the dual arm robot to conduct experiments, which is more realistic in real manufacturing factories. In the part of the dual arm system coordinately grasping-and-moving objects, the controlled plant is assumed as a spring-inerter-damper model and the system identification method was proposed. Based on this model, the Kalman filter fusing measured force and position error to estimate the fusion force error was developed, and different grasping profiles are considered as different model parameters but the same model type. Then, model parameter identification, robustness of Kalman filter and comparison of the advantages of Kalman filter were carried out through experiments, and the dual arm system grasped and moved different objects along the spatial 8-figured trajectory to verify the controller structure. And in the part of the application of learning algorithm, after completely explaining the basic knowledge of machine learning, reinforcement learning and iterative learning control (ILC), ILC was applied on the task of dual arm system coordinately grasping-and-moving objects, and the improvement is proven by the theoretical and experimental method. Then, Proximal Policy Optimization algorithm and four simulation cases were illustrated, in which the usually usage of reinforcement learning to be a real-time controller is transformed to be the aided trajectory planner. The single arm pushing a spring to do force control experiments and the single arm 1D and 2D force control pseudo-grinding experiment were conducted to verify the proposed structure. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/72251 |
DOI: | 10.6342/NTU201803765 |
全文授權: | 有償授權 |
顯示於系所單位: | 機械工程學系 |
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