臺灣博碩士論文加值系統

近年來機械手臂廣泛應用於各種取放作業，此類作業若在工作空間存在障礙物時需規劃路徑以避開之。一般是由操作人員事先規劃路徑來達到避障效果，但此種作法之缺點是每當環境改變便需重新規劃路徑，不僅耗時且浪費人力。改善方法之一為選擇在離線狀態下使用攝影機對障礙物取像並透過電腦視覺技術建立障礙物之位置資訊，接著使用避障演算法使機械手臂在進行取放動作時能避開障礙物。此外為使機械手臂能判斷是否碰觸到其他物體而採取適當措施以避免物體或機械手臂損壞，本論文將機械手臂之操作模式設定於轉矩模式而非位置模式或速度模式。然而在轉矩模式下開發演算法，需考慮各種非線性項如重力對機械手臂造成之影響。有鑑於此，本論文針對重力影響較大之手臂連桿，進行重力參數鑑別以作為重力補償之用。於避障演算法上，本論文使用位能場法引導機械手臂末端點在平面上朝著目標前進並避開障礙物，此外本論文所設計之位置控制器包含重力補償與PD控制。實驗結果證實，本論文所鑑別出之參數應用於重力補償有不錯效果，而將之結合PD控制器更能增進位置控制效能。實驗結果亦顯示使用位能場法能成功導引機械手臂末端點完成平面避障任務。

In recent years, manipulators have been used extensively in the pick-and-place task. This kind of task requires trajectory planning when there are obstacles in the workspace. Generally, trajectory planning for obstacle avoidance is performed by the operator/engineer in advance. However, one of the major drawbacks for this kind of approach is that trajectory planning must be performed again when there is change in the work environment, in which it is time-consuming and a waste of manpower. One of the possible solutions to the aforementioned problems is to use a camera to obtain the image of the obstacle offline, and the use computer vision technique to obtain the position information of the obstacle. Subsequently, we use an obstacle avoidance algorithm to help the manipulator avoid obstacle during a pick-and-place task. Furthermore, setting the operation mode of the manipulator to the torque mode, rather than position mode or velocity mode can help the manipulator detect whether it touches other objects so as to take further measures to prevent from damaging the manipulator. The influence caused by nonlinear terms such as gravity should be considered when the manipulator is operated in the torque mode. As a result, this thesis focuses on the identification of the gravity parameters of the links of the manipulator that experience significant influence by gravity, and subsequently uses these parameters to compensate for gravity. Regarding the obstacle avoidance algorithm, this thesis exploits the artificial potential field to guide the end-effector of the manipulator moving toward the target position in the plane and also avoid obstacles. In addition, the position controller employed in this thesis includes a gravity compensation term and a PD controller. Experimental results verify that the gravity compensation scheme with identified parameters has exhibited decent performances. In addition, the PD controller combined with gravity compensation can yield good position control. Experimental results also verify that using the artificial potential field successfully assists the end-effector of the manipulator in accomplishing plane obstacle avoidance.

中文摘要 i
Extended Abstract ii
誌謝 xi
目錄 xii
表目錄 xiv
圖目錄 xv
第一章 緒論 1
1.1 簡介 1
1.2 研究動機與目的 2
1.3 文獻回顧 3
1.3.1 機械手臂避障 3
1.3.2 動態模型鑑別 4
1.4 論文架構 5
第二章 機械手臂避障 7
2.1 位能場 7
2.1.1 吸引力位能場 7
2.1.2 排斥力位能場 9
2.1.3 排斥力位能場禁止區域之圓心及半徑 10
2.1.4 吸引力與排斥力位能場之結合 11
2.2 位能場避障演算法 12
2.2.1 梯度下降法 12
2.2.2 避開位能場之局部最小值 13
第三章 機械手臂控制架構 15
3.1 順向運動學 15
3.2 雅可比矩陣 18
3.2.1 角速度 19
3.2.2 平移速度 19
3.2.3 結合平移及角速度之雅可比矩陣 20
3.3 PD回授控制器與重力補償 22
3.4 速度估測法則 23
3.4.1 最小平方估速法 23
3.4.2 低加速度估測器 25
第四章 重力參數鑑別與位能場於末端點避障之應用 27
4.1 障礙物輪廓曲線座標建立 27
4.2 重力參數鑑別 29
4.2.1 重力向量之數學模型 29
4.2.2 馬達轉矩資訊取得 31
4.2.3 連桿質心偏移 31
4.2.4 參數鑑別 33
4.3 基於位能場之避障控制架構 35
第五章 實驗設備及實驗結果 36
5.1 實驗設備 36
5.1.1 硬體設備 37
5.1.2 軟體設備 40
5.2 實驗結果 40
5.2.1 重力補償實驗 40
5.2.2 重力補償及PD回授控制器之位置控制實驗 43
5.2.3 末端點平面避障實驗 50
第六章 結論與未來建議 55
6.1 結論 55
6.2 未來建議 55
附錄 57
參考文獻 64

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