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Traversing seen and unseen corridors with Artificial Neural Networks/Context matching
Metadaten
Formale Metadaten
| Titel | Traversing seen and unseen corridors with Artificial Neural Networks/Context matching |
| Autor |
Narayanan, Krishna Kumar
|
| Lizenz |
CC-Namensnennung 3.0 Unported: Sie dürfen das Werk bzw. den Inhalt zu jedem legalen Zweck nutzen, verändern und in unveränderter oder veränderter Form vervielfältigen, verbreiten und öffentlich zugänglich machen, sofern Sie den Namen des Autors/Rechteinhabers in der von ihm festgelegten Weise nennen. |
| DOI | 10.5446/15422 |
| Herausgeber | TU Dortmund, Lehrstuhl für Regelungssystemtechnik |
| Erscheinungsjahr | 2011 |
| Sprache | Stummfilm |
| Produktionsjahr | 2011 |
| Produktionsort | Dortmund |
Inhaltliche Metadaten
| Fachgebiet | Technik |
| Abstract | Proof of concept experiments to navigate a corridor environment using Two level feature matching architecture. Layer 1 classifies the scenario from the shape and appearance of the environment into Corridor (C) or Open room (O) or Cluttered (L) environment. Layer 2 deploys scenario specific model to predict action and correspondingly navigate the environment. The two models used are: 1. Artificial Neural Networks and 2. Context matching and prediction. Timeline: 0:00 Scenario: Known corridor with Artificial Neural Networks 0:17 Scenario: Known corridor with Context matching and prediction 0:39 Scenario: Unknown corridor with Artificial Neural Networks 1:02 Scenario: Unknown corridor with Context matching and prediction 1:18 Scenario: Transition between trained and an untrained corridor using Artificial Neural Networks This video is the supplement to the paper: "Scenario and context specific visual robot behavior learning" presented at the 2011 IEEE International Conference on Robotics and Automation (ICRA2011), May 9-13 2011, Shanghai International Convention Center, Shanghai, China. For more information please visit: http://www.rst.e-technik.tu-dortmund.... |
| Schlagwörter |
navigation gaussian mixture model learning from demonstration visual behaviors obstacle avoidance homing |
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