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http://dx.doi.org/10.3745/KTSDE.2022.11.9.363

Grasping a Target Object in Clutter with an Anthropomorphic Robot Hand via RGB-D Vision Intelligence, Target Path Planning and Deep Reinforcement Learning  

Ryu, Ga Hyeon (경희대학교 전자정보융합공학과)
Oh, Ji-Heon (경희대학교 전자정보융합공학과)
Jeong, Jin Gyun (경희대학교 전자정보융합공학과)
Jung, Hwanseok (경희대학교 전자정보융합공학과)
Lee, Jin Hyuk (경희대학교 전자정보융합공학과)
Lopez, Patricio Rivera (경희대학교 전자정보융합공학과)
Kim, Tae-Seong (경희대학교 생체의공학과 및 전자정보융합공학과)
Publication Information
KIPS Transactions on Software and Data Engineering / v.11, no.9, 2022 , pp. 363-370 More about this Journal
Abstract
Grasping a target object among clutter objects without collision requires machine intelligence. Machine intelligence includes environment recognition, target & obstacle recognition, collision-free path planning, and object grasping intelligence of robot hands. In this work, we implement such system in simulation and hardware to grasp a target object without collision. We use a RGB-D image sensor to recognize the environment and objects. Various path-finding algorithms been implemented and tested to find collision-free paths. Finally for an anthropomorphic robot hand, object grasping intelligence is learned through deep reinforcement learning. In our simulation environment, grasping a target out of five clutter objects, showed an average success rate of 78.8%and a collision rate of 34% without path planning. Whereas our system combined with path planning showed an average success rate of 94% and an average collision rate of 20%. In our hardware environment grasping a target out of three clutter objects showed an average success rate of 30% and a collision rate of 97% without path planning whereas our system combined with path planning showed an average success rate of 90% and an average collision rate of 23%. Our results show that grasping a target object in clutter is feasible with vision intelligence, path planning, and deep RL.
Keywords
Anthropomorphic Robot Hand; Reinforcement Learning; Path Planning; Object Detection;
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Times Cited By KSCI : 2  (Citation Analysis)
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