An Approach for Peg-in-Hole Assembling using Intuitive Search Algorithm based on Human Behavior and Carried by Sensors Guided Industrial Robot

Abdullah, Mustafa W.; Roth, Hubert; Weyrich, Michael; Wahrburg, Jürgen

doi:10.1016/j.ifacol.2015.06.295

Cited by 45 publications

(20 citation statements)

References 5 publications

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“…It was not possible to rely on previous works consisting in standardized assembly set-ups that are mainly designed for "pickand-place" tasks, and where the occlusion issue is not addressed (Tang et al, 2003;Funk et al, 2015;Blattgerste et al, 2017). Instead, a blind assembly process was designed based on the "peg-in-hole" manipulation, where an object must be inserted in another without direct visibility on the insertion area (Chhatpar and Branicky, 2001;Park et al, 2013;Abdullah et al, 2015;Zhang et al, 2017). Insertions are an important aspect of assembly: tight tolerances between objects involved in the insertion, as well as positioning accuracies, require some level of compliance, and trajectory control (Lim et al, 2007).…”

Section: Blind Insertion Taskmentioning

confidence: 99%

Effectiveness of Augmented Reality Guides for Blind Insertion Tasks

Khenak

Vézien

Bourdot

2020

Front. Virtual Real.

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Although many augmented reality (AR)-based assembly support systems have been proposed in academic research and industry, the effectiveness of AR to resolve the occlusion issue in the context of a blind assembly process remains an unexplored topic. Therefore, the present work investigates how AR can assist operators during the execution of blind manual assembly tasks. Specifically, an AR research set-up was designed to provide assistance in occlusion situations during a peg-in-hole task. The set-up featured a see-through device (HoloLens), which provides operators with two modes of visual augmentations that directly overlay on the assembly objects. The first mode referred to as the “wireframe overlay” displays the inner part of the objects, providing an inside view of the occluded parts, and the second one referred to as the “axes overlay,” displays the axes of the objects and their slots, indicating how to align the different parts during the assembly. The effectiveness of these AR visualizations was compared to a baseline augmentation-free situation in a controlled experiment. Thus, following a within-subject design, 30 participants performed a two-stages blind insertion task. Their performances represented by task completion time, insertion errors, and smoothness of the insertions were recorded. In addition, a post-questionnaire reported their subjective perception of task difficulty during the task and their preferences. Results indicated a strong acceptance of participants for AR visualizations that they rated as allowing them to perform the task more easily. However, no statistically significant differences in terms of objective performance measures were found. Yet, it was found that axes overlay produced smoother trajectories compared to the wireframe overlay, highlighting the potential effect of more abstract visualization aids.

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Section: Blind Insertion Taskmentioning

confidence: 99%

Effectiveness of Augmented Reality Guides for Blind Insertion Tasks

Khenak

Vézien

Bourdot

2020

Front. Virtual Real.

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“…For example, Navarro-Gonzalez et al used a neural network algorithm to process data from F/T sensors and to mitigate the effect of misalignment between the vision system and the robot's wrist [24]. Abdullah et al investigated peg-in-hole operations and used data models constructed from F/T feedback to search for the hole center using a strategy that mimics human intuitive behavior [25]. Xu et al divided the contact zone around the hole into sixteen sectors and applied a k-dimensional (KD) Tree algorithm to partition the sectors efficiently, and to more accurately determine the location of the hole center [26].…”

Section: Related Workmentioning

confidence: 99%

Improving 3D vision-robot registration for assembly tasks

Franaszek¹,

Cheok²,

Wyk³

et al. 2020

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Good registration between the coordinate frames of a perception system and a robot is important for the efficient operation of autonomous systems in vision-guided assembly lines. Rigid-body registration, which is based on the measurement of corresponding points (fiducials) in both frames, is a commonly used method. Noise and possible bias in the measured points degrade the quality of registration as gauged by the Target Registration Error (TRE). A method to restore the rigid body condition (RRBC) was recently developed to reduce TRE when the bias is larger than the noise. This was achieved by applying corrections to target locations in the vision sensor frame. This paper presents two procedures to improve the performance of the RRBC method by: 1) selecting the location of fiducials used for calculating correction to the target location and 2) selecting the correct arm configuration for fiducial from multiple configurations as calculated by inverse kinematics. Experiments performed with a motion tracking system and two different robot arms show that these two procedures can further reduce the Root Mean Square of TRE (RMST) to less than 20 % of the uncorrected RMST.

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“…With the aim to improve the flexibility of robotic assembly, adaptive control techniques [7], such as hybrid position/force control algorithms have been proposed [8]- [10]. The general idea among the existing control techniques is to implement a position control along the lateral direction while pushing the robot towards the direction of the insertion.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Control Approach to Robotic Assembly with Uncertainties in Vision and Dynamics

Mobedi

Villa

Kim

et al. 2020

2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

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The objective of this paper is to propose an adaptive impedance control framework to cope with uncertainties in vision and dynamics in robotic assembly tasks. The framework is composed of an adaptive controller, a vision system, and an interaction planner, which are all supervised by a finite state machine. In this framework, the target assembly object's pose is detected through the vision module, which is then used for the planning of the robot trajectories. The adaptive impedance control module copes with the uncertainties of the vision and the interaction planner modules in alignment of the assembly parts (a peg and a hole in this work). Unlike the classical impedance controllers, the online adaptation rule regulates the level of robot compliance in constrained directions, acting on and responding to the external forces. This enables the implementation of a flexible and adaptive Remote Center of Compliance (RCC) system, using active control. We first evaluate the performance of the proposed adaptive controller in comparison to classical impedance control. Next, the overall performance of the integrated system is evaluated in a pegin-hole setup, with different clearances and orientation mismatches.

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An Approach for Peg-in-Hole Assembling using Intuitive Search Algorithm based on Human Behavior and Carried by Sensors Guided Industrial Robot

Cited by 45 publications

References 5 publications

Effectiveness of Augmented Reality Guides for Blind Insertion Tasks

Effectiveness of Augmented Reality Guides for Blind Insertion Tasks

Improving 3D vision-robot registration for assembly tasks

An Adaptive Control Approach to Robotic Assembly with Uncertainties in Vision and Dynamics

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