Jamming Analysis and Force Control for Flexible Dual Peg-in-Hole Assembly

Zhang, Kuangen; Xu, Jing; Chen, Heping; Zhao, Jianguo; Chen, Ken

doi:10.1109/tie.2018.2838069

Cited by 58 publications

(25 citation statements)

References 18 publications

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“…For the same purpose, Tang et al [119] established a mapping between the force/torque feedback and contact states, which were classified into four categories: line contact, single-point contact, two-point contact and three-point contact. Using the same method, Zhang et al [120] provided the mapping model with complex logic computations to estimate the actual contact state for the dual peg-in-hole mating process, and validated the effectiveness through an experimental assembly platform with a force/torque sensor (Figure 14). In this method, the mapping was always obtained based on the force analysis and geometric analysis of all contact states, which relies largely on the specific shape and fit tolerance of the peg and hole.…”

Section: B Active Control Methodsmentioning

confidence: 99%

A Review on Significant Technologies Related to the Robot-Guided Intelligent Bolt Assembly Under Complex or Uncertain Working Conditions

Zhang

Liu

et al. 2019

IEEE Access

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In most existing bolt assembling operations, the human-machine collaboration (HMC) is used to guarantee both the operational efficiency and the connection quality. However, the huge labor intensity and costs are still needed when there exist bolts with huge quantity and multiple models in some special working environments, for example locomotive maintenance and automobile assembly. Actually, the robotic application can better solve the above problem. The main reason why this application is limited in the bolt assembly is due to the technological barriers related to the intelligent control. This paper firstly introduces the HMC-based and the robot-guided bolt assembly processes in detail, then summarizes the latest researches related to the significant technologies needed in the robot-guided assembly to show their present status and future trends. Finally based on the technology limitations this paper further presents some suggestions for future studies of each technology, which attempts to help readers broaden their research thoughts and speed up the development of the robot-guided bolt assembly.INDEX TERMS Automatic bolt assembly, robotic application, technological barrier, intelligent control.

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Section: B Active Control Methodsmentioning

confidence: 99%

A Review on Significant Technologies Related to the Robot-Guided Intelligent Bolt Assembly Under Complex or Uncertain Working Conditions

Zhang

Liu

et al. 2019

IEEE Access

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“…Strategies for peg-in-hole insertion, or more generally robot assembly tasks, have been studied from numerous viewpoints for several decades raising many challenges [1]. Insertion strategies for pegs of various geometries have been previously studied and attempted via a variety of possible techniques and system models: standard cylinders [20,21,22], multiple-peg objects [23,24], soft pegs [25], industrial inserts [26], and open world objects [27,28,29,30,31]. This work seeks to generalize insertion for various geometries.…”

Section: Related Workmentioning

confidence: 99%

“…Model-Based Insertion: Approaches using contact models reason about state conditions and optimal insertion trajectories while controlling the manipulator [24,25]. These techniques typically require expensive force-torque sensing to detect peghole interactions [21,32].…”

Section: Related Workmentioning

confidence: 99%

Vision-driven Compliant Manipulation for Reliable; High-Precision Assembly Tasks

Morgan¹,

Wen²,

Liang³

et al. 2021

Robotics: Science and Systems XVII

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Highly constrained manipulation tasks continue to be challenging for autonomous robots as they require high levels of precision, typically less than 1mm, which is often incompatible with what can be achieved by traditional perception systems. This paper demonstrates that the combination of state-of-the-art object tracking with passively adaptive mechanical hardware can be leveraged to complete precision manipulation tasks with tight, industrially-relevant tolerances (0.25mm). The proposed control method closes the loop through vision by tracking the relative 6D pose of objects in the relevant workspace. It adjusts the control reference of both the compliant manipulator and the hand to complete object insertion tasks via within-hand manipulation. Contrary to previous efforts for insertion, our method does not require expensive force sensors, precision manipulators, or timeconsuming, online learning, which is data hungry. Instead, this effort leverages mechanical compliance and utilizes an objectagnostic manipulation model of the hand learned offline, offthe-shelf motion planning, and an RGBD-based object tracker trained solely with synthetic data. These features allow the proposed system to easily generalize and transfer to new tasks and environments. This paper describes in detail the system components and showcases its efficacy with extensive experiments involving tight tolerance peg-in-hole insertion tasks of various geometries as well as open-world constrained placement tasks.

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“…The task of peg-in-hole is a classic assembly task. It is one of the basics for many complex assembly tasks [36]. In recent years, the research of the peg-in-hole assembly has also made many novel methods.…”

Section: Introductionmentioning

confidence: 99%

A Visual Grasping Strategy for Improving Assembly Efficiency Based on Deep Reinforcement Learning

et al. 2021

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The adjustment times of the attitude alignment are fluctuated due to the fluctuation of the contact force signal caused by the disturbing moments in the compliant peg-in-hole assembly. However, these fluctuations are difficult to accurately measure or definition as a result of many uncertain factors in the working environment. It is worth noting that gravitational disturbing moments and inertia moments significantly impact these fluctuations, in which the changes of the peg concerning the mass and the length have a crucial influence on them. In this paper, a visual grasping strategy based on deep reinforcement learning is proposed for peg-in-hole assembly. Firstly, the disturbing moments of assembly are analyzed to investigate the factors for the fluctuation of assembly time. Then, this research designs a visual grasping strategy, which establishes a mapping relationship between the grasping position and the assembly time to improve the assembly efficiency. Finally, a robotic system for the assembly was built in V-REP to verify the effectiveness of the proposed method, and the robot can complete the training independently without human intervention and manual labeling in the grasping training process. The simulated results show that this method can improve assembly efficiency by 13.83%. And, when the mass and the length of the peg change, the proposed method is still effective for the improvement of assembly efficiency.

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Jamming Analysis and Force Control for Flexible Dual Peg-in-Hole Assembly

Cited by 58 publications

References 18 publications

A Review on Significant Technologies Related to the Robot-Guided Intelligent Bolt Assembly Under Complex or Uncertain Working Conditions

A Review on Significant Technologies Related to the Robot-Guided Intelligent Bolt Assembly Under Complex or Uncertain Working Conditions

Vision-driven Compliant Manipulation for Reliable; High-Precision Assembly Tasks

A Visual Grasping Strategy for Improving Assembly Efficiency Based on Deep Reinforcement Learning

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