Integration of Vision-based Object Detection and Grasping for Articulated Manipulator in Lunar Conditions
This work addresses robotic manipulation in extreme lunar environments, but it is incremental as it builds on existing vision and grasping methods.
The paper tackles the challenge of integrating vision-based object detection and grasping for articulated manipulators in lunar conditions, achieving a 92% success rate in rock stacking tasks on non-flat surfaces under difficult lighting.
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.