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
9/3/2023
cs.ROcs.AI

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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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Cite this paper

@article{boucher2023integration,
  title  = {Integration of Vision-based Object Detection and Grasping for Articulated Manipulator in Lunar Conditions},
  author = {Camille Boucher and Gustavo H. Diaz and Shreya Santra and Kentaro Uno and Kazuya Yoshida},
  year   = {2023},
  eprint = {2309.01055},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2309.01055v1}
}

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