Precision Peg-in-Hole Assembly Strategy Using Force-Guided Robot

Xu, Yin; Hu, Yue; Hu, Lei

doi:10.2991/icmmita-15.2015.260

Cited by 18 publications

(14 citation statements)

References 8 publications

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“…The peg-hole assembly was completed through a vision system estimation [27]. The interaction force was obtained by force sensors to guide the robot to complete the assembly [28,29]. Most of the assembly strategies are based on the idea of modeling.…”

Section: Introductionmentioning

confidence: 99%

A Robotic Automatic Assembly System Based on Vision

Song

et al. 2020

Applied Sciences

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At present, the production lines of mobile phones are mainly manual and semi-automatic. Robots are the most important tools used to improve the intelligence level of industry production. The design of an intelligent robotic assembly system is presented in this paper, which takes the assembly of screen components and shell components as examples. There are major factors restricting the application of robots, such as the calibration of diversified objects, the moving workpiece with incomplete information, and diverse assembly types. A method was proposed to solve these technological difficulties. The multi-module calibration method is used to transform the pose relationship between the robot, the conveyor belt, and the two cameras in the robot assembly system. Aiming at a workpiece moving with incomplete information, the minimum matrix method is proposed to detect the position. Then dynamic fetching is realized based on pose estimation of the moving workpiece. At last, the template matching method is used to identify the assembly area of the workpiece type. The proposed method was verified on a real platform with a LINKHOU LR4-R5 60 robot. Results showed that the assembly success rate was above 98%, and the intelligent assembly system of the robot can realize the assembly of mobile phone screens and back shells without any staff.

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Section: Introductionmentioning

confidence: 99%

A Robotic Automatic Assembly System Based on Vision

Song

et al. 2020

Applied Sciences

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“…However, it is not easy to manually design motor skills and ensure their optimal solutions. Numerous researchers, such as Xu et al [11], Park et al [12], Zhang et al [13], and Jokesch et al [14], have proposed methods for solving the peg-in-hole problem, based on this method. These studies are not learning-based approaches; instead, they use predesigned strategies after analyzing the peg-in-hole task.…”

Section: Introductionmentioning

confidence: 99%

Learning, Improving, and Generalizing Motor Skills for the Peg-in-Hole Tasks Based on Imitation Learning and Self-Learning

et al. 2020

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We propose a framework based on imitation learning and self-learning to enable robots to learn, improve, and generalize motor skills. The peg-in-hole task is important in manufacturing assembly work. Two motor skills for the peg-in-hole task are targeted: "hole search" and "peg insertion". The robots learn initial motor skills from human demonstrations and then improve and/or generalize them through reinforcement learning (RL). An initial motor skill is represented as a concatenation of the parameters of a hidden Markov model (HMM) and a dynamic movement primitive (DMP) to classify input signals and generate motion trajectories. Reactions are classified as familiar or unfamiliar (i.e., modeled or not modeled), and initial motor skills are improved to solve familiar reactions and generalized to solve unfamiliar reactions. The proposed framework includes processes, algorithms, and reward functions that can be used for various motor skill types. To evaluate our framework, the motor skills were performed using an actual robotic arm and two reward functions for RL. To verify the learning and improving/generalizing processes, we successfully applied our framework to different shapes of pegs and holes. Moreover, the execution time steps and path optimization of RL were evaluated experimentally. of motor skills. However, for acquiring complete motor skills, it has one evident limitation: it does not ensure that robots acquire motor skills that are optimized for their goals (that is, it generally provides near-optimal solutions) [5]. Furthermore, it is not easy for human performers to provide a demonstration dataset that can cover all situations arising during the execution of a motor skill [6]. Nonetheless, these human demonstrations can be used as a solid starting point for robots to acquire motor skills [7]. To obtain optimal motor skills, robots must be able to improve motor skills through self-learning. However, this self-learning is a time-consuming and expensive process in the absence of references. We attempt to obtain these optimal solutions with fewer trials-and-errors by providing near-optimal solutions learned from human demonstrations. In this paper, robots improve motor skills to optimize them-referred to as improvement-and generalize them so that they are widely applicable-known as generalization-through self-learning.The peg-in-hole task has also been addressed through several imitation learning studies [8,9]. However, the peg-in-hole task is not easy to learn with this method alone. The main reason is that it is difficult for human performers to provide a complete demonstration dataset to robots, because it is not feasible to prepare all possible reaction situations. In addition, unintended reaction information may be included in the dataset during the demonstration process. These problems may prevent robots from acquiring the complete motor skills. Thus, initial motor skills are learned to classify reaction force/moment signals and generate reaction motion trajectories from human demonstrations, and thei...

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“…However, it requires constant tracking of the end effector relative to the final destination point, which may be problematic in some applications, e.g., robotic surgery [15]. Active compliance control based on feedback from force/torque (F/T) sensors is another technique commonly used in part assembly [16][17][18]. Active control based on external sensors was also used for automated welding of aerospace components [19].…”

Section: Introductionmentioning

confidence: 99%

Using locally adjustable hand-eye calibrations to reduce robot localization error

Franaszek

Cheok

2020

SN Appl. Sci.

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Accurate pose of a robot end effector is required in many applications. Typically, this is achieved by robot calibration and then, registration of the robot frame to the world frame. In this paper, the registration of a poorly calibrated robot to a world frame was performed using many local hand-eye registrations and using one global registration. Both approaches were evaluated using a set of target poses. The use of the properly chosen local transformation applied to each target led to a tenfold reduction in the orientation and position error in comparison with a use of one global transformation. The median orientation error was reduced to 0.029° and the median position error was reduced to 0.221 mm which is approximately four times larger than robot specified position repeatability.

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Precision Peg-in-Hole Assembly Strategy Using Force-Guided Robot

Cited by 18 publications

References 8 publications

A Robotic Automatic Assembly System Based on Vision

A Robotic Automatic Assembly System Based on Vision

Learning, Improving, and Generalizing Motor Skills for the Peg-in-Hole Tasks Based on Imitation Learning and Self-Learning

Using locally adjustable hand-eye calibrations to reduce robot localization error

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