Identification of contact formations: Resolving ambiguous force torque information

Hertkorn, Katharina; Roa, Máximo A.; Preusche, Carsten; Borst, Christoph; Hirzinger, Gerd

doi:10.1109/icra.2012.6225148

Cited by 13 publications

(4 citation statements)

References 18 publications

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“…We define the containment relationship between two different system configurations over their capability of resisting external wrenches, i.e. contact wrench space [22], [23]. A system configuration q specifies the geometric relationships among the object, robot and environment, while its corresponding contact region(s) qualitatively indicates its capability of resisting external forces that can be applied on the object.…”

Section: ) Containment Relationshipmentioning

confidence: 99%

Manipulation Planning Using Environmental Contacts to Keep Objects Stable under External Forces

Chen

Figueredo

Dogar

2019

2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids)

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This paper addresses the problem of sequential manipulation planning to keep an object stable under changing external forces. Particularly, we focus on using objectenvironment contacts. We present a planning algorithm which can generate robot configurations and motions to intelligently use object-environment, as well as object-robot, contacts, to keep an object stable under forceful operations such as drilling and cutting. Given a sequence of external forces, the planner minimizes the number of different configurations used to keep the object stable. An important computational bottleneck in this algorithm is due to the static stability analysis of a large number of configurations. We propose a containment relationship between configurations, to prune the stability checking process.

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Section: ) Containment Relationshipmentioning

confidence: 99%

Manipulation Planning Using Environmental Contacts to Keep Objects Stable under External Forces

Chen

Figueredo

Dogar

2019

2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids)

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“…Finally, a particle filter is employed in computing the likeness that a certain wrench vector belongs to a CS. The results shown in Hertkorn et al 35 are excellent for the computation time wise; however, the sequence of the CSs is still needed to be known. Support vector machine (SVM) was successfully employed in generating the fuzzy inference mechanism (FIM) for classifying the contacts in a force-controlled robotic peg-in-hole assembly process.…”

Section: Introductionmentioning

confidence: 98%

“…Cabras et al 34 were capable of using the stochastic gradient boosting (SGB) classifier in modelling different CSs without the need for knowing the task sequence or task graph. Hertkorn et al 35 used only the force and torque signals in recognising different CSs for compliant motion robot systems by computing the wrench space based on the CS graph that describes the sequence of different CSs in a certain task. Then, a similarity index is augmented showing the amount of overlap between wrenches that belong to different CSs.…”

Section: Introductionmentioning

confidence: 99%

Contact-state modelling in force-controlled robotic peg-in-hole assembly processes of flexible objects using optimised Gaussian mixtures

Jasim

Plapper

Voos

2015

Proceedings of the Institution of Mechanical Engineers, Part B:

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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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“…This work also relied on designed features which were pre-selected by designers thus making the method less autonomous. Hertkorn et al ( 2012 ) generated a wrench matrix based on the CAD models of the assembly parts with a particle filter to recognize the CS based on the F/T measurements. This method was implemented to resolve the ambiguity of the force measurements and recognize the contact formation of a rectangular workpiece on a flat surface.…”

Section: Introductionmentioning

confidence: 99%

Symbolic-Based Recognition of Contact States for Learning Assembly Skills

Al-Yacoub

Zhao²,

Lohse

et al. 2019

Front. Robot. AI

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Imitation learning is gaining more attention because it enables robots to learn skills from human demonstrations. One of the major industrial activities that can benefit from imitation learning is the learning of new assembly processes. An essential characteristic of an assembly skill is its different contact states (CS). They determine how to adjust movements in order to perform the assembly task successfully. Humans can recognize CSs through haptic feedback. They execute complex assembly tasks accordingly. Hence, CSs are generally recognized using force and torque information. This process is not straightforward due to the variations in assembly tasks, signal noise and ambiguity in interpreting force/torque (F/T) information. In this research, an investigation has been conducted to recognize the CSs during an assembly process with a geometrical variation on the mating parts. The F/T data collected from several human trials were pre-processed, segmented and represented as symbols. Those symbols were used to train a probabilistic model. Then, the trained model was validated using unseen datasets. The primary goal of the proposed approach aims to improve recognition accuracy and reduce the computational effort by employing symbolic and probabilistic approaches. The model successfully recognized CS based only on force information. This shows that such models can assist in imitation learning.

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Identification of contact formations: Resolving ambiguous force torque information

Cited by 13 publications

References 18 publications

Manipulation Planning Using Environmental Contacts to Keep Objects Stable under External Forces

Manipulation Planning Using Environmental Contacts to Keep Objects Stable under External Forces

Contact-state modelling in force-controlled robotic peg-in-hole assembly processes of flexible objects using optimised Gaussian mixtures

Symbolic-Based Recognition of Contact States for Learning Assembly Skills

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