scholarly journals The Suitable Timing of Visual Sensing in Error Recovery Using Task Stratification and Error Classification

2017 ◽  
Vol 4 (2) ◽  
pp. 134
Author(s):  
Akira Nakamura ◽  
Kazuyuki Nagata ◽  
Kensuke Harada ◽  
Natsuki Yamanobe
Keyword(s):  
Error Recovery ◽  
Visual Sensing ◽  
Error Classification

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

2017 ◽  
Vol 22 ◽  
pp. 217-220
Author(s):  
Akira Nakamura ◽  
Kazuyuki Nagata ◽  
Kensuke Harada ◽  
Natsuki Yamanobe
Keyword(s):  
Error Recovery ◽  
Error Classification

scholarly journals Technique of Recovery Process and Application of AI in Error Recovery Using Task Stratification and Error Classification

2018 ◽  
Vol 5 (1) ◽  
pp. 56 ◽  
Author(s):  
Akira Nakamura ◽  
Kazuyuki Nagata ◽  
Kensuke Harada ◽  
Natsuki Yamanobe
Keyword(s):  
Recovery Process ◽  
Error Recovery ◽  
Error Classification

scholarly journals Using Importance Ranks to Derive Suitable Timing of Visual Sensing in Manipulation Task Containing Error Recovery

2019 ◽  
Vol 5 (4) ◽  
pp. 283
Author(s):  
Akira Nakamura ◽  
Kazuyuki Nagata ◽  
Kensuke Harada ◽  
Yukiyasu Domae
Keyword(s):  
Error Recovery ◽  
Visual Sensing ◽  
Manipulation Task

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.

Visual Sensing Is Seeing: Why Mindsight, in Hindsight, Is Blind

2004 ◽  
Author(s):  
Daniel J. Simons ◽  
Gabriel Nevarez ◽  
Walter R. Boot
Keyword(s):  
Visual Sensing
Sign in / Sign up

Export Citation Format

Share Document