Vision Based Pose Estimation of Multiple Peg-in-Hole for Robotic Assembly

Pitchandi, Nagarajan; Perumaal, S. Saravana; Yogameena, B.

doi:10.1007/978-3-319-68124-5_5

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…Vision sensors are used to estimate the pose of mating components in a vision-assisted robotic peg-in-hole assembly. 1 Decoupled alignments for position and attitude are realized with the use of visual compliance and adoption of a light-weight 3degrees of freedom (DOF) active peg. 2 Besides, a lot of researchers utilize the force sensor mounted between the end-effector and the robot to achieve the peg-in-hole task.…”

Section: Introductionmentioning

confidence: 99%

A novel approach for flexible manipulator conducting screwing task based on robot–environment contact classification

Zhang

Xie

Liu

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part C:

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Bolt screwing assembly task is a crucial part for robot fine manipulation. For widely applied flexible joints robot with six-dimensional force/torque sensor, data collected could not be seen as the contact force since it consists of internal force caused by deformation of the manipulator joints. Force analysis and geometric analysis could not be applied to judge the relative pose between robot end-effector and the environment. To this end, logistic regression method was used to classify the contact state by force signals. Besides, the criterion that bolt has entered the thread hole is also significant, while a boundary condition was proposed to solve this problem. A bolt screwing experiment was conducted to evaluate the proposed strategy, and the results demonstrate the effectiveness of the contact state classification and the boundary condition for judging the bolt entering the thread hole.

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Section: Introductionmentioning

confidence: 99%

A novel approach for flexible manipulator conducting screwing task based on robot–environment contact classification

Zhang

Xie

Liu

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part C:

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show abstract

“…Huang et al [13] used highspeed cameras to align a peg to a hole. More recently, the visual coaxial system was used to perform precise alignment in peg-in-hole assembly [14] [15].…”

Section: Arxiv:190209157v1 [Csro] 25 Feb 2019mentioning

confidence: 99%

Quickly Inserting Pegs into Uncertain Holes using Multi-view Images and Deep Network Trained on Synthetic Data

Triyonoputro

Wan

Harada

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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This paper uses robots to assemble pegs into holes on surfaces with different colors and textures. It especially targets at the problem of peg-in-hole assembly with initial position uncertainty. Two in-hand cameras and a force-torque sensor are used to account for the position uncertainty. A program sequence comprising learning-based visual servoing, spiral search, and impedance control is implemented to perform the peg-in-hole task with feedback from the above sensors. Contributions are mainly made in the learning-based visual servoing of the sequence, where a deep neural network is trained with various sets of synthetic data generated using the concept of domain randomization to predict where a hole is. In the experiments and analysis section, the network is analyzed and compared, and a real-world robotic system to insert pegs to holes using the proposed method is implemented. The results show that the implemented peg-in-hole assembly system can perform successful peg-in-hole insertions on surfaces with various colors and textures. It can generally speed up the entire peg-in-hole process.

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“…In this work, we look at the most common problem of robotic assembly: peg-in-hole assembly, which is the basis of a wide range of component assemblies [1] [2]. Robotic peg-in-hole assembly has been extensively researched and applied in various fields from large-scale object assembly, such as aviation components [3] [4], engines [5] and windshields assembly to small-scale components, such as mold casting manufacturing [6], electronic components [7] and even microproduct [8] assembly.…”

Section: Introductionmentioning

confidence: 99%

Compare Contact Model-based Control and Contact Model-free Learning: A Survey of Robotic Peg-in-hole Assembly Strategies

Xu,

Hou,

Liu

et al. 2019

Preprint

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In this paper, we present an overview of robotic peg-in-hole assembly and analyze two main strategies: contact model-based and contact model-free strategies. More specifically, we first introduce the contact model control approaches, including contact state recognition and compliant control two steps. Additionally, we focus on a comprehensive analysis of the whole robotic assembly system. Second, without the contact state recognition process, we decompose the contact modelfree learning algorithms into two main subfields: learning from demonstrations and learning from environments (mainly based on reinforcement learning). For each subfield, we survey the landmark studies and ongoing research to compare the different categories. We hope to strengthen the relation between these two research communities by revealing the underlying links. Ultimately, the remaining challenges and open questions in the field of robotic peg-in-hole assembly community are discussed. The promising directions and potential future work are also considered.

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Vision Based Pose Estimation of Multiple Peg-in-Hole for Robotic Assembly

Cited by 6 publications

References 14 publications

A novel approach for flexible manipulator conducting screwing task based on robot–environment contact classification

A novel approach for flexible manipulator conducting screwing task based on robot–environment contact classification

Quickly Inserting Pegs into Uncertain Holes using Multi-view Images and Deep Network Trained on Synthetic Data

Compare Contact Model-based Control and Contact Model-free Learning: A Survey of Robotic Peg-in-hole Assembly Strategies

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