Contact-state monitoring of force-guided robotic assembly tasks using expectation maximization-based Gaussian mixtures models

This article proposes the distribution similarity measure–based Gaussian mixtures model for the contact-state (CS) modelling in force-guided robotic assembly processes of flexible rubber parts. The wrench (Cartesian force and torque) signals of the manipulated object are captured for different states of the given assembly process. The distribution similarity measure–based Gaussian mixtures model CS modelling scheme is employed in modelling the captured wrench signals for different CSs. The proposed distribution similarity measure–based Gaussian mixtures model CS modelling scheme uses the Gaussian mixtures model in modelling the captured signals. The parameters of the Gaussian mixtures models are computed using expectation maximisation. The optimal number of Gaussian mixtures model components for each CS model is determined by considering the classification success rate as an index for the similarity measure between the distribution of the captured signals and the developed models. The optimal number of Gaussian mixtures model components corresponds to the highest classification success rate; hence, object elasticity variation would be accommodated by properly choosing the optimal number of Gaussian mixtures model components. The performance of the proposed distribution similarity measure–based Gaussian mixtures model CS modelling strategy is evaluated by a test stand composed of a KUKA lightweight robot doing peg-in-hole assembly processes for flexible rubber objects. Two rubber objects with different elasticity are considered for two experiments; in the first experiment, an elastic peg of 30 Shore A hardness is considered and that of the second experiment has hardness of 6 Shore A which is even softer than the one used in experiment 1. Employing the proposed distribution similarity measure–based Gaussian mixtures model CS modelling strategy excellent classification success rate was obtained for both experiments. However, more Gaussian mixtures model components are required for the softer one that gives a strong impression of the non-stationarity behaviour increment for softer materials. Comparison is performed with the available CS modelling schemes and the distribution similarity measure–based Gaussian mixtures model is shown to provide the best classification success rate performance with a reduced computational time.

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Towards Autonomous Robotic Assembly: Using Combined Visual and Tactile Sensing for Adaptive Task Execution

Journal of Intelligent & Robotic Systems ◽

10.1007/s10846-020-01303-z ◽

2021 ◽

Vol 101 (3) ◽

Author(s):

Korbinian Nottensteiner ◽

Arne Sachtler ◽

Alin Albu-Schäffer

Keyword(s):

Autonomous Robots ◽

Vision System ◽

Dynamic Environments ◽

Bayesian Framework ◽

Experimental Results ◽

Robotic Assembly ◽

Tactile Sensing ◽

Task Execution ◽

Industrial Assembly ◽

Assembly Tasks

AbstractRobotic assembly tasks are typically implemented in static settings in which parts are kept at fixed locations by making use of part holders. Very few works deal with the problem of moving parts in industrial assembly applications. However, having autonomous robots that are able to execute assembly tasks in dynamic environments could lead to more flexible facilities with reduced implementation efforts for individual products. In this paper, we present a general approach towards autonomous robotic assembly that combines visual and intrinsic tactile sensing to continuously track parts within a single Bayesian framework. Based on this, it is possible to implement object-centric assembly skills that are guided by the estimated poses of the parts, including cases where occlusions block the vision system. In particular, we investigate the application of this approach for peg-in-hole assembly. A tilt-and-align strategy is implemented using a Cartesian impedance controller, and combined with an adaptive path executor. Experimental results with multiple part combinations are provided and analyzed in detail.

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System State Monitoring to Facilitate Safe and Efficient Human-Robot Collaboration in Hybrid Assembly Cells

Volume 1: 37th Computers and Information in Engineering Conference ◽

10.1115/detc2017-68269 ◽

2017 ◽

Cited By ~ 1

Author(s):

Carlos W. Morato ◽

Krishnanand N. Kaipa ◽

Satyandra K. Gupta

Keyword(s):

Hybrid Cell ◽

System State ◽

Hybrid Assembly ◽

State Monitoring ◽

Matching Algorithm ◽

Plan Generation ◽

3D Printed ◽

Assembly Tasks ◽

Human Robot Collaboration

Hybrid assembly cells allow humans and robots to collaborate on assembly tasks. We consider a model of the hybrid cell in which a human and a robot asynchronously collaborate to assemble a product. The human retrieves parts from a bin and places them in the robot’s workspace, while the robot picks up the placed parts and assembles them into the product. Realizing hybrid cells requires -automated plan generation, system state monitoring, and contingency handling. In this paper we describe system state monitoring and present a characterization of the part matching algorithm. Finally, we report results from human-robot collaboration experiments using a KUKA robot and a 3D-printed mockup of a simplified jet-engine assembly to illustrate our approach.

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Exploitation of SEM charging effects for monitoring robotic assembly tasks

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) ◽

10.1109/iros.2016.7759761 ◽

2016 ◽

Cited By ~ 2

Author(s):

Malte Bartenwerfer ◽

Sergej Fatikow

Keyword(s):

Robotic Assembly ◽

Assembly Tasks

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Underactuated Part Alignment System (UPAS) for Industrial Assembly Tasks

Volume 6: 35th Mechanisms and Robotics Conference, Parts A and B ◽

10.1115/detc2011-48267 ◽

2011 ◽

Author(s):

Brian J. Slaboch ◽

Philip Voglewede

Keyword(s):

Degrees Of Freedom ◽

Vertical Plane ◽

Cost Effective ◽

Robotic Assembly ◽

Robot Arm ◽

Planning Techniques ◽

Alignment System ◽

Compliant Assembly ◽

Industrial Assembly ◽

Assembly Tasks

This paper introduces the Underactuated Part Alignment System (UPAS) as a cost-effective and flexible approach to aligning parts in the vertical plane prior to an industrial robotic assembly task. The advantage of the UPAS is that it utilizes the degrees of freedom (DOFs) of a SCARA (Selective Compliant Assembly Robot Arm) type robot in conjunction with an external fixed post to achieve the desired part alignment. Three path planning techniques will be presented that can be used with the UPAS to achieve the proper part rotation.

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