Developments to Increase the Performance, Operational Versatility and Automation of a Lunar Surface Manipulation System
Bibliographic record
Abstract
The objective of this paper is to describe and summarize the results of the development efforts for the Lunar Surface Manipulation System (LSMS) with respect to increasing the performance, operational versatility, and automation. Three primary areas of development are covered, including; the expansion of the operational envelope and versatility of the current LSMS test-bed, the design of a second generation LSMS, and the development of automation and remote control capability. The first generation LSMS, which has been designed, built, and tested both in lab and field settings, is shown to have increased range of motion and operational versatility. Features such as fork lift mode, side grappling of payloads, digging and positioning of lunar regolith, and a variety of special end effectors are described. LSMS operational viability depends on bei nagble to reposition its base from an initial position on the lander to a mobility chassis or fixed locations around the lunar outpost. Preliminary concepts are presented for the second generation LSMS design, which will perform this self-offload capability. Incorporating design improvements, the second generation will have longer reach and three times the payload capability, yet it will have approximately equivalent mass to the first generation. Lastly, this paper covers improvements being made to the control system of the LSMS test-bed, which is currently operated using joint velocity control with visual cues. These improvements include joint angle sensors, inverse kinematics, and automated controls.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".