SOURCE-LINKED INTELLIGENCE
Designing an Efficient Excavator Bucket for Lunar ISRU: A Comparative Study with Vision-Based Fill and Displacement Analysis
This paper present a spiral-cavity wheel for lunar regolith excavation and a sensor-light evaluation stack that jointly estimates fill ratio (vision), sinkage (vision), and specific energy from actuator logs. In benchtop tests (four revolutions at 5, 10, and 15~RPM) against two literature baselines, the proposed wheel achieved higher excavated mass and fill ratio, delivering 2.2-3.0 times higher excavation rate while reducing specific energy by 29 % relative to a bucket-drum baseline. Normalized sinkage (mm/kg) was also lower, indicating stable traction without bogging. Effort-time traces show
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-22T05:53:56.000Z
First collected: 2026-09-23T04:21:13.910Z. This is not the publication date.