scholarly journals A Novel Strategy for Automatic Error Classification and Error Recovery for Robotic Assembly in Flexible Production

2020 ◽  
Vol 100 (3-4) ◽  
pp. 863-877
Author(s):  
Ewa Kristiansen ◽  
Emil Krabbe Nielsen ◽  
Lasse Hansen ◽  
David Bourne
Keyword(s):  
Error Detection ◽  
Active Vision ◽  
Error Recovery ◽  
Robotic Assembly ◽  
Classification Rate ◽  
A Cell ◽  
Error Classification ◽  
Novel Strategy ◽  
Assembly Tasks ◽  
Automatic Error

AbstractIn this article, we develop a novel strategy for automatic error classification and recovery in robotic assembly tasks. The strategy does not require error diagnosis. It allows for effective reduction of an undetermined number of error states to 4, without the need for further operator updates of error space. The strategy integrates existing methods for computer vision, active vision and active manipulation. Our solution is implemented in a generic software framework, which is independent from software and hardware for implementing error detection and allows for application in other assembly types and components. The value of our strategy was experimentally validated on a simple case, where we inserted a battery into a cell phone. The experiment was performed on 1500 assembly attempts and included 500 detected errors. The whole experiment ran for 42 hours, with no need for operator assistance or supervision. The resulting classification rate is 99.6% and the resulting recovery rate is 98.8%. The 6 unrecovered errors were successfully resolved in a successive assembly attempt.

The error detection and recovery of robotic assembly tasks

1994 ◽  
Vol 27 (14) ◽  
pp. 891-896
Author(s):  
B.J. McCarragher
Keyword(s):  
Error Detection ◽  
Robotic Assembly ◽  
Assembly Tasks

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

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.

Automatic Error Detection

1976 ◽  
pp. 68-80 ◽  
Author(s):  
H. Kopetz
Keyword(s):  
Error Detection ◽  
Automatic Error

Automatic Error Detection

1979 ◽  
pp. 68-80
Author(s):  
H. Kopetz
Keyword(s):  
Error Detection ◽  
Automatic Error

scholarly journals Estimation and Categorization of Errors in Error Recovery Using Task Stratification and Error Classification

2017 ◽  
Vol 4 (2) ◽  
pp. 163
Author(s):  
Akira Nakamura ◽  
Kazuyuki Nagata ◽  
Kensuke Harada ◽  
Natsuki Yamanobe
Keyword(s):  
Error Recovery ◽  
Error Classification

scholarly journals A Probabilistic Approach to Error Detection&Correction for Tree-Mapping Grammars

2018 ◽  
Vol 111 (1) ◽  
pp. 97-112
Author(s):  
Tim vor der Brück
Keyword(s):  
Error Detection ◽  
Basic Assumption ◽  
Probabilistic Approach ◽  
Large Set ◽  
Generation Process ◽  
Input Structure ◽  
Error Detection And Correction ◽  
Complete Failure ◽  
Automatic Error

Abstract Rule-based natural language generation denotes the process of converting a semantic input structure into a surface representation by means of a grammar. In the following, we assume that this grammar is handcrafted and not automatically created for instance by a deep neural network. Such a grammar might comprise of a large set of rules. A single error in these rules can already have a large impact on the quality of the generated sentences, potentially causing even a complete failure of the entire generation process. Searching for errors in these rules can be quite tedious and time-consuming due to potentially complex and recursive dependencies. This work proposes a statistical approach to recognizing errors and providing suggestions for correcting certain kinds of errors by cross-checking the grammar with the semantic input structure. The basic assumption is the correctness of the latter, which is usually a valid hypothesis due to the fact that these input structures are often automatically created. Our evaluation reveals that in many cases an automatic error detection and correction is indeed possible.

scholarly journals A Cell-Based High-Throughput Assay for the Screening of Small-Molecule Inhibitors of p53–MDM2 Interaction

2011 ◽  
Vol 16 (4) ◽  
pp. 450-456 ◽  
Author(s):  
Jing Li ◽  
Shuyong Zhang ◽  
Linghuan Gao ◽  
Ying Chen ◽  
Xin Xie
Keyword(s):  
High Throughput ◽  
Luciferase Reporter ◽  
Reporter System ◽  
Direct Inhibition ◽  
A Cell ◽  
Luciferase Reporter System ◽  
Novel Strategy ◽  
High Throughput Assay ◽  
Two Hybrid ◽  
Rule Out

The p53 tumor suppressor is a potent transcription factor that regulates cell growth inhibition and apoptosis. The oncoprotein MDM2 suppresses p53 activity by direct inhibition of its transcriptional activity and enhances the degradation of p53 via the ubiquitin–proteosome pathway. Overexpression of MDM2, found in many human tumors, impairs p53-mediated cell death effectively. Inhibition of the p53–MDM2 interaction can stabilize p53 and may offer a novel strategy for cancer therapy. To search for new inhibitors of the p53–MDM2 interaction, the authors developed a cell-based high-throughput assay system based on mammalian two-hybrid technology. They also used a dual-luciferase reporter system to rule out false- positive hits due to the cytotoxic effect of compounds. Using this assay, they screened a library consisting of 3840 compounds and identified one compound that activates p53 pathway and induces growth arrest in tumor cells.

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