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

CoRR（2023）

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摘要

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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关键词

Object Detection,Lunar Conditions,Light Conditions,Resource Extraction,Robotic Applications,Instance Segmentation,Future Tasks,Neural Network,Imaging Data,Convolutional Neural Network,Validation Set,Workspace,Point Cloud,Robotic System,Semantic Segmentation,Depth Images,System Overview,Robot Model,Precise Manipulation,Body Joints,Vision-based System,Robot Operating System,Custom Dataset,Lunar Surface,Object Detection Results,Low-light Image

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