A Robotic Peg-in-Hole Assembly Strategy Based on Variable Compliance Center

Wang, Sen; Chen, Guodong; Xu, Hui; Wang, Zheng

doi:10.1109/access.2019.2954459

Cited by 45 publications

(15 citation statements)

References 29 publications

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“…Jiang et al (2017) employed PiH insertions for large aircraft components under small assembly misalignments. Wang et al (2019) investigated the PiH issue for micro-assembly and developed a five DOF RCC based end-effector using Pseudo Rigid Body Modelling (PRBM).…”

Section: Related Workmentioning

confidence: 99%

Preliminary Study of End-Effector Compliance for Reducing Insertion Force in Automated Fluid Coupling for Trains

Eshraghi

Jiang

Suraci

et al. 2022

JID

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Robotic assembly of mating parts (peg-in-hole (PiH)) inevitably encounters misalignments. Although passive end-effector compliance is key to successful alignment during the assembly, the literature does not propose many solutions for large misalignments, which is relevant to applications such as compliance of a robot end-effector for train fluid servicing. The results from physical experiments indicate insertion forces that are too large for practical applications, even with small misalignments. This preliminary study applies a hybrid approach combining physical experiments and simulation modelling for large motion PiH coupling with end-effector compliance. This provides a platform for investigating insertion force during misaligned coupling. The simulation model contains configurable parameters for robot compliance and PiH friction which are informed by the physical experiment results. The many robot compliances are lumped as two torsional springs on the pitch and yaw motion axis of the robot arm model. The simulation model is then calibrated using the physical results without having to conduct further intensive physical experiments. The calibrated model represents the physical measurements to a satisfactory degree, however its performance can still be improved.

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Section: Related Workmentioning

confidence: 99%

Preliminary Study of End-Effector Compliance for Reducing Insertion Force in Automated Fluid Coupling for Trains

Eshraghi

Jiang

Suraci

et al. 2022

JID

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“…The existing research work on robot hole finding is divided into passive and active hole finding. The former is achieved by adding passive flexible actuators to the robot's end and by performing a "blind search" for the hole location using Archimedean spirals and grating-type trajectories [1][2][3]. However, this approach suffers from low efficiency; therefore, its use for hole finding remains limited.…”

Section: Introductionmentioning

confidence: 99%

Hole-Finding Learning Strategy for a Robot Assembly With Keyed Circular Peg

et al. 2023

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Taking the gear assembly of the reducer assembly line as a research background, the holefinding strategy for robot assembly with keyed circular pegs is proposed to solve the problem of low holefinding efficiency and success rate. in the circular hole-finding task, deviation domains were divided based on a static mechanism, position vector trajectory optimization in the step distance and direction was designed, and a deviation domain-force mapping relationship was established using a genetic algorithm-support vector machine (GA-SVM) classification algorithm; the accuracy of this algorithm is approximately 90.00%. Thirty groups completed the circular hole-finding task in an average time of 5.4 s. For the square hole-finding task, dual monocular cameras were integrated to identify corner points of the flat key and keyway. Image semantic segmentation based on deep learning was used for the corner-point recognition of the flat key to suppress the effect of changes in light intensity; the recognition has an average error of 0.39 mm. Coarse and fine adjustment circumferential deflection strategies were adopted sequentially. The 30 groups of square holefinding tasks exhibited a 96.70% success rate in an average time of 10.2 s. The proposed hole-finding strategy improves the efficiency and success rate of the gear assembly.INDEX TERMS keyed circular peg; learning-based robot hole finding; support vector machine; image semantic segmentation based on deep learning; genetic algorithm

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“…Industrial robots are being used more and more extensively, especially in the field of assembly. Assembly tasks are a major part of industrial manufacturing, and the cost of assembly tasks accounts for more than half of the cost of industrial production [1]. The previous programming method in industrial robots was to perform assembly tasks by recording the key positions in the assembly process.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Non-diagonal Stiffness Matrix of Compliance Control for Robotic Assembly Using Deep Reinforcement Learning

Liu

Yao

et al. 2022

J. Phys.: Conf. Ser.

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With the advent of Industrial 4.0, industrial robots have been widely used in various sectors, e.g., in assembly. Peg-in-hole (PiH) assembly is the most typical one among assembly tasks. In PiH tasks, the robot transfers from a non-contact mode to a contact-rich mode. Instead of switching the position/force control mode with a pause when contact is detected, we deploy a non-diagonal stiffness compliance control for planning the adaptive trajectory to improve the task efficiency and ensure contact safety. In this paper, we proposed a deep reinforcement learning (DRL) method to achieve the above compliance. A compliance controller based on a virtual forward dynamic (FD) model is built. A DRL agent is deployed to optimize the parameters of the non-diagonal stiffness matrix for the built compliance controller to generate a trajectory that adapts to the changing contact condition. The experiment shows the proposed method can control the contact force within a safe range and improve the efficiency of assembly tasks.

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A Robotic Peg-in-Hole Assembly Strategy Based on Variable Compliance Center

Cited by 45 publications

References 29 publications

Preliminary Study of End-Effector Compliance for Reducing Insertion Force in Automated Fluid Coupling for Trains

Preliminary Study of End-Effector Compliance for Reducing Insertion Force in Automated Fluid Coupling for Trains

Hole-Finding Learning Strategy for a Robot Assembly With Keyed Circular Peg

Optimizing Non-diagonal Stiffness Matrix of Compliance Control for Robotic Assembly Using Deep Reinforcement Learning

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