Abstract Dry stacking with found, minimally processed rocks is a useful capability when it comes to autonomous construction. However, it is a difficult planning
problem since both the state and action space are continuous, and structural stability is strongly affected by complex friction and contact constraints. We propose
an algorithmic approach for autonomous construction from a collection of irregularly shaped objects. The structure planning is calculated in simulation by first
considering geometric and physical constraints to find a small set of feasible actions
and then refined by using a hierarchical filter based on heuristics. These plans are
then executed open-loop with a robotic arm equipped with a wrist RGB-D camera.
Experimental results show that the proposed planning algorithm can significantly
improve the state of the art robotics dry-stacking techniques.
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Planning for Robotic Dry Stacking with Irregular Stones
The ability to build structures with autonomous robots using only found, minimally processed stones would be immensely useful, especially in remote areas. Assembly planning for dry-stacked structures, however, is difficult since both the state and action spaces are continuous, and stability is strongly affected by complex friction and contact constraints. We propose a planning algorithm for such assemblies that uses a physics simulator to find a small set of feasible poses and then evaluates them using a hierarchical filter. We carefully designed the heuristics for the filters to match our goal of building stable, free-standing walls. These plans are then executed open-loop with a robotic arm equipped with a wrist RGB-D camera. Experimental results show that the proposed planning algorithm can significantly improve the state of the art in robotic dry stacking.
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- NSF-PAR ID:
- 10321607
- Editor(s):
- Ishigami, G; Yoshida, K
- Date Published:
- Journal Name:
- Field and Service Robotics
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-981-15-9460-1_23
