Sun-Synchronous Robotic Exploration: Technical Description and Field Experimentation
Bibliographic record
Abstract
Sun-synchronous robotic exploration is accomplished by reasoning about sunlight: where the Sun is in the sky, where and when shadows will fall, and how much power can be obtained through various courses of action. We conducted experiments in the Canadian high arctic using a solar-powered rover to prove the concept of Sun-synchronous exploration. Using knowledge of orbital mechanics, local terrain, and locomotion power, the rover Hyperion planned Sun-synchronous routes to visit designated sites while obtaining the necessary solar power for continuous operation. Hyperion executed its plan, beginning and ending each 24-h period with batteries fully charged, after traveling two circuits of more than 6 km in barren, Mars-like terrain. The objective of the Sun-Synchronous Navigation project (http://www.frc.ri.cmu.edu/sunsync) was to create hardware and software technologies needed to realize Sun-synchronous exploration and to validate these technologies in field experimentation. In the process, we learned important technical lessons regarding rover mechanism, motion control, planning algorithms, and system architecture. In this paper we describe the concept of Sun-synchronous exploration. We overview the design of the robot Hyperion and the software system that enables it to operate in synchrony with the Sun. We then discuss results and lessons from analysis of our field experiments. This paper describes rover and planetary exploration research at Carnegie Mellon during 2000-2002.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".