本研究建立了一用以執行智慧抓取任務之欠致動自適應夾爪，其可根據夾取時與物體之幾何關係，進行平行式或包覆式抓取。為最大化夾爪的適應能力，本研究建立抓取運動學模型，並透過最佳化來尋找最佳設計參數。此外，爪面上配置有接近與力量感測器，使夾爪能快速且安全地抓取脆弱物體。此夾爪安裝於一具六自由度之機器手臂上，作為執行智慧抓取任務之終端效應器。智慧抓取係基於三個以RGBD影像作為輸入的深度學習神經網路，分別用以偵測物體、估測物體形心位置及其姿態。最後透過實驗，驗證本研究所設計之夾爪與神經網路，可以成功用以抓取隨機放置之各式物體。

This thesis develops an under-actuated adaptive gripper for intelligent grasping. The gripper can perform either parallel or enveloping grasp depending on the geometry of the object to be grasped. To maximize the adaptability of the gripper, kinematic models for grasping are established and optimizations are performed on the models to seek the optimal design parameters. The gripper is equipped with a proximity sensor and a force sensor, which allows it to quickly and safely grasp delicate objects. The gripper is attached to a 6 DOF robot arm as an end effector to perform intelligent grasping. The intelligent grasping is based on three deep learning networks which adopt RGBD stereo images as the inputs. The three networks are respectively to detect the object, estimate its centroid location, and determine its orientation. Experiments verify that the developed gripper and neural networks can successful grasp various randomly placed objects.

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