Robust and safe traversal of doors is an important task for autonomous mobile robots. This paper presents a robust and inexpensive approach for recognizing and localizing doors based on monocular grey-level images and...
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ISBN:
(纸本)0780363485;0780363493
Robust and safe traversal of doors is an important task for autonomous mobile robots. This paper presents a robust and inexpensive approach for recognizing and localizing doors based on monocular grey-level images and minimalistic models. The traversal is achieved by servoing the robot with respect to the door-hypothesis. Robustness against pose errors, scene complexity, and sensing conditions as obtained by combining indexing on significant aggregated features with a multi-view approach that employs consistency over time: pose-hypotheses are filtered via the motion of the robot to reject incorrect ones. The traversal is implemented as a behavior and was evaluated in 150 experiments cat different doors under different conditions.
In this manuscript, we propose an approach that allows a team of robots to create new (emergent) behaviors at execution time. Basically, we improve the approach called N-Learning used for self-programming of robots in...
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In this manuscript, we propose an approach that allows a team of robots to create new (emergent) behaviors at execution time. Basically, we improve the approach called N-Learning used for self-programming of robots in a team, by modifying and extending its functioning structure. The basic capability of behavior sharing is increased by the catching of emergent behaviors at run time. With this, all robots are able not only to share existing knowledge, here represented by blocks of codes containing desired behaviors but also to creating new behaviors as well. Experiments with real robots are presented in order to validate our approach. The experiments demonstrate that after the human-robot interaction with one robot using Program by Demonstration, this robot generates a new behavior at run time and teaches a second robot that performs the same learned behavior through this improved version of the N-learning system.
New methods for producing avoidance behavior among moving obstacles within the context of reactive robotic control are described. These specifically include escape and dodging behaviors. Dodging is concerned with the ...
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New methods for producing avoidance behavior among moving obstacles within the context of reactive robotic control are described. These specifically include escape and dodging behaviors. Dodging is concerned with the avoidance of a ballistic projectile while escape is more useful within the context of chase. The motivation and formulation of these new reactive behaviors are presented. Both simulation and experimental results using a robot in a cluttered and moving world are provided.
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