Modeling Contact State of Industrial Robotic Assembly Using Support Vector Regression

Li, Fengming; Jiang, Qi; Li, Yibin; Meng, Wei; Song, Rui

doi:10.1109/cyber.2018.8688071

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…Wan et al trained robots to perform object assembly tasks using multimodal three-dimensional (3D) vision [37]. In our previous work [38], [39], force/torque signals were used to represent the contact state. In the present paper, the position/posture, the force/torque of the end effector and the robot joint parameters were adopted to describe the contact state.…”

Section: System Overview a Assembly Systemmentioning

confidence: 99%

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Manipulation Skill Acquisition for Robotic Assembly Based on Multi-Modal Information Description

Li¹,

Jiang²,

Quan³

et al. 2020

IEEE Access

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Automatic assembly of elastic components is difficult because of the potential deformation of parts during the assembly process. Consequently, robots cannot adapt their manipulation to dynamic changes. Designing systems that learn assembly skills can help in alleviating the uncertain factor for industrialgrade assembly robots. This study proposes a skill acquisition method based on multi-modal information description to realize the assembly of systems with elastic components. This multi-modal information includes two-dimensional images, poses, forces/torques, and robot joint parameters. In this method, robots acquire searching, location determination, and pose adjustment skills using these multi-modal information parameters. As a result, robots can reach the assembly target by analyzing two-dimensional images with no position constraint. While acquiring pose adjustment skills, the reward function with depth and assembly steps is used to improve the learning efficiency. The deep deterministic policy gradient (DDPG) algorithm is applied for acquiring skills. Experiments using a KUKA iiwa robot demonstrated the effectiveness and conciseness of our method. Our results indicate that the robot acquired searching, location determination, and pose adjustment skills that allowed it to successfully complete elastic assembly. INDEX TERMS Industrial robots, acquisition of manipulation skills, deep reinforcement learning, multi-modal information description. I. INTRODUCTION A. MOTIVATION Robots are used for more efficient production assembly. Research on automation of assembly systems has focused on intelligent, automatic, and flexible robots. During an actual assembly process, force measurement data are not available owing to elastic deformation between workpieces. Elastic deformation can be neglected when dealing with assembly of rigid parts. However, in the case of weakly rigid components, such deformation causes a series of uncertain changes, such as position, posture, and depth changes. There are methods based on force-control and compliance control that are already facing the problem of assembly of elastic The associate editor coordinating the review of this article and approving it for publication was Okyay Kaynak.

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Section: System Overview a Assembly Systemmentioning

confidence: 99%

“…To demonstrate the effectiveness of the proposed method for skill acquisition, we compared the proposed method with knowledge-based methods [38], [39] and with the deep Q-learning network framework [15]. The robot could acquire pose adjustment skills in the frameworks of DQN and DDPG, with little prior knowledge.…”

Section: ) Different Framesmentioning

confidence: 99%