scholarly journals Dual-Arm Peg-in-Hole Assembly Using DNN with Double Force/Torque Sensor

2021 ◽  
Vol 11 (15) ◽  
pp. 6970
Author(s):  
David Ortega-Aranda ◽  
Julio Fernando Jimenez-Vielma ◽  
Baidya Nath Saha ◽  
Ismael Lopez-Juarez
Keyword(s):  
Discrete Event ◽  
Machine Learning Algorithms ◽  
Torque Sensor ◽  
Working Space ◽  
Linear Behavior ◽  
The Difference ◽  
Dual Arm Robot ◽  
Failure Conditions ◽  
Assembly Tasks ◽  
Dual Arm

Assembly tasks executed by a robot have been studied broadly. Robot assembly applications in industry are achievable by a well-structured environment, where the parts to be assembled are located in the working space by fixtures. Recent changes in manufacturing requirements, due to unpredictable demanded products, push the factories to seek new smart solutions that can autonomously recover from failure conditions. In this way, new dual arm robot systems have been studied to design and explore applications based on its dexterity. It promises the possibility to get rid of fixtures in assembly tasks, but using less fixtures increases the uncertainty on the location of the components in the working space. It also increases the possibility of collisions during the assembly sequence. Under these considerations, adding perception such as force/torque sensors have been done to produce useful data to perform control actions. Unfortunately, the interaction forces between mating parts produced non-linear behavior. Consequently, machine learning algorithms have been considered an alternative tool to avoid the non-linearity. In this work we introduce an assembly strategy for an industrial dual arm robot based on the combination of a discrete event controller and Deep Neural Networks (DNN) to solve the peg-in-hole assembly. Our results show the difference between the use of DNN with one and with two force/torque sensors during the assembly task and demonstrate a 30% increase in the assembly success ratio when using a double force/torque sensor.

A RRT-based motion planning of dual-arm robot for (Dis)assembly tasks

IEEE ISR 2013 ◽  
2013 ◽  
Author(s):  
Dong-Hyung Kim ◽  
Sung-Jin Lim ◽  
Duck-Hyun Lee ◽  
Ji Yeong Lee ◽  
Chang-Soo Han
Keyword(s):  
Motion Planning ◽  
Dual Arm Robot ◽  
Assembly Tasks ◽  
Dual Arm

Peg-in-hole assembly based on master-slave coordination for a compliant dual-arm robot

2020 ◽  
Vol 40 (2) ◽  
pp. 189-198
Author(s):  
Yanjiang Huang ◽  
Yanglong Zheng ◽  
Nianfeng Wang ◽  
Jun Ota ◽  
Xianmin Zhang
Keyword(s):  
Design Methodology ◽  
Position Control ◽  
Control Model ◽  
Torque Control ◽  
Human Beings ◽  
Content Type ◽  
Different Shapes ◽  
Dual Arm Robot ◽  
Assembly Tasks ◽  
Dual Arm

Purpose The paper aims to propose an assembly scheme based on master–slave coordination for a compliant dual-arm robot to complete a peg-in-hole assembly task. Design/methodology/approach The proposed assembly scheme is inspired by the coordinated behaviors of human beings in the assembly process. The left arm and right arm of the robot are controlled to move alternately. The fixed arm and the moving arm are distinguished as the slave arm and the master arm, respectively. The position control model is used at the uncontacted stage, and the torque control model is used at the contacted stage. Findings The proposed assembly scheme is evaluated through peg-in-hole assembly experiments with different shapes of assembly piece. The round, triangle and square assembly piece with 0.5 mm maximum clearance between the peg and the hole can be assembled successfully based on the proposed method. Furthermore, three assembly strategies are investigated and compared in the peg-in-hole assembly experiments with different shapes of assembly piece. Originality/value The contribution of this study is that the authors propose an assembly scheme for a compliant dual-arm robot to overcome the low positioning accuracy and complete the peg-in-hole assembly tasks with different shapes parts.

scholarly journals AUTOMATION OF ASSEMBLY OPERATION BASED ON DUAL-ARM ROBOT

2019 ◽  
pp. 111-116
Author(s):  
Marek Vagas ◽  
Simsik Dusan
Keyword(s):  
Assembly Process ◽  
Assembly Sequence ◽  
Suitable Material ◽  
Assembly Area ◽  
Innovative Idea ◽  
Robotic Devices ◽  
Dual Arm Robot ◽  
Robot Cooperation ◽  
Assembly Tasks ◽  
Dual Arm

Urgency of the research. Automation as a whole, together with increasing of demands from customer push companies at all levels to the implementation of new and innovative solutions of robotic devices. This reason we consider as sufficient for realization of special customized solution for deployment at assembly operations. Target setting. Purpose of article is to give an example how to increase a level of automation based on specific requirements that consists from assembly area. Actual scientific researches and issues analysis. Actual research is nowadays focused at such problematics, because return of investments based on robotic devices seems to be reliable and people at workplace can realize and focus to another type of tasks. Uninvestigated parts of general matters defining. Specific automated solution based on dual arm robot implementation into the assembly process brings us a new possibility for assembly flow realization together with required assembly sequence for whole operation. The research objective. The aim of article is to provide an idea how to automate such manual assembly tasks with focus to robotic devices implementation. The statement of basic materials. For realization of automated solutions is good to have a suitable material how to solve assembly sequence and assembly process. Conclusions. Published article presents an innovative idea for dual arm robot implementation into the assembly process. Proposed assembly sequence based on human – robot cooperation at this specific workplace gives an example and view how automation of assembly processes can be solved.

scholarly journals Machine Learning Model of Dimensionless Numbers to Predict Flow Patterns and Droplet Characteristics for Two-Phase Digital Flows

2021 ◽  
Vol 11 (9) ◽  
pp. 4251
Author(s):  
Jinsong Zhang ◽  
Shuai Zhang ◽  
Jianhua Zhang ◽  
Zhiliang Wang
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Digital Microfluidics ◽  
Flow Patterns ◽  
Machine Learning Algorithms ◽  
Dimensionless Numbers ◽  
Two Phase ◽  
The Difference ◽  
Input Variables ◽  
Digital Microfluidic

In the digital microfluidic experiments, the droplet characteristics and flow patterns are generally identified and predicted by the empirical methods, which are difficult to process a large amount of data mining. In addition, due to the existence of inevitable human invention, the inconsistent judgment standards make the comparison between different experiments cumbersome and almost impossible. In this paper, we tried to use machine learning to build algorithms that could automatically identify, judge, and predict flow patterns and droplet characteristics, so that the empirical judgment was transferred to be an intelligent process. The difference on the usual machine learning algorithms, a generalized variable system was introduced to describe the different geometry configurations of the digital microfluidics. Specifically, Buckingham’s theorem had been adopted to obtain multiple groups of dimensionless numbers as the input variables of machine learning algorithms. Through the verification of the algorithms, the SVM and BPNN algorithms had classified and predicted the different flow patterns and droplet characteristics (the length and frequency) successfully. By comparing with the primitive parameters system, the dimensionless numbers system was superior in the predictive capability. The traditional dimensionless numbers selected for the machine learning algorithms should have physical meanings strongly rather than mathematical meanings. The machine learning algorithms applying the dimensionless numbers had declined the dimensionality of the system and the amount of computation and not lose the information of primitive parameters.

scholarly journals On the Juridical Relevance of the Phenomenological Notion of Person in Max Scheler and Edith Stein

2021 ◽  
Author(s):  
Francesco Galofaro
Keyword(s):  
Artificial Intelligence ◽  
Edmund Husserl ◽  
Phenomenological Analysis ◽  
Machine Learning Algorithms ◽  
Decision Makers ◽  
Edith Stein ◽  
Max Scheler ◽  
Human Decision ◽  
The Difference ◽  
Social Units

AbstractThe paper presents a semiotic interpretation of the phenomenological debate on the notion of person, focusing in particular on Edmund Husserl, Max Scheler, and Edith Stein. The semiotic interpretation lets us identify the categories that orient the debate: collective/individual and subject/object. As we will see, the phenomenological analysis of the relation between person and social units such as the community, the association, and the mass shows similarities to contemporary socio-semiotic models. The difference between community, association, and mass provides an explanation for the establishment of legal systems. The notion of person we inherit from phenomenology can also be useful in facing juridical problems raised by the use of non-human decision-makers such as machine learning algorithms and artificial intelligence applications.

scholarly journals Data mining of coronavirus: SARS-CoV-2, SARS-CoV and MERS-CoV

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Jung Eun Huh ◽  
Seunghee Han ◽  
Taeseon Yoon
Keyword(s):  
Machine Learning ◽  
Amino Acid ◽  
Amino Acid Sequence ◽  
Decision Tree ◽  
Machine Learning Algorithms ◽  
High Similarity ◽  
Incubation Periods ◽  
Initial Question ◽  
The Difference ◽  
Blast Program

Abstract Objective In this study we compare the amino acid and codon sequence of SARS-CoV-2, SARS-CoV and MERS-CoV using different statistics programs to understand their characteristics. Specifically, we are interested in how differences in the amino acid and codon sequence can lead to different incubation periods and outbreak periods. Our initial question was to compare SARS-CoV-2 to different viruses in the coronavirus family using BLAST program of NCBI and machine learning algorithms. Results The result of experiments using BLAST, Apriori and Decision Tree has shown that SARS-CoV-2 had high similarity with SARS-CoV while having comparably low similarity with MERS-CoV. We decided to compare the codons of SARS-CoV-2 and MERS-CoV to see the difference. Though the viruses are very alike according to BLAST and Apriori experiments, SVM proved that they can be effectively classified using non-linear kernels. Decision Tree experiment proved several remarkable properties of SARS-CoV-2 amino acid sequence that cannot be found in MERS-CoV amino acid sequence. The consequential purpose of this paper is to minimize the damage on humanity from SARS-CoV-2. Hence, further studies can be focused on the comparison of SARS-CoV-2 virus with other viruses that also can be transmitted during latent periods.

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