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arXiv · 2309.01055

Integration of Vision-based Object Detection and Grasping for Articulated Manipulator in Lunar Conditions

Camille Boucher·Gustavo H. Diaz·Shreya Santra·Kentaro Uno·Kazuya Yoshida
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Abstract

The integration of vision-based frameworks to achieve lunar robot applications faces numerous challenges such as terrain configuration or extreme lighting conditions. This paper presents a generic task pipeline using object detection, instance segmentation and grasp detection, that can be used for various applications by using the results of these vision-based systems in a different way. We achieve a rock stacking task on a non-flat surface in difficult lighting conditions with a very good success rate of 92%. Eventually, we present an experiment to assemble 3D printed robot components to initiate more complex tasks in the future.

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Camille Boucher, Gustavo H. Diaz, Shreya Santra, Kentaro Uno, Kazuya Yoshida. 2023-09-03. Integration of Vision-based Object Detection and Grasping for Articulated Manipulator in Lunar Conditions. https://arxiv.org/abs/2309.01055

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