Planetary Exploration With Robot Teams: Implementing Higher Autonomy With Swarm Intelligence
Bibliographic record
Abstract
Since the beginning of space exploration, Mars and the moon have been examined via orbiters, landers, and rovers. More than 40 missions have targeted Mars, and over 100 have been sent to the moon. Space agencies continue to focus on developing novel strategies and technologies for probing celestial bodies. Multirobot systems are particularly promising for planetary exploration, as they are more robust to individual failure and have the potential to examine larger areas; however, there are limits to how many robots an operator can control individually. We recently took part in the European Space Agency's (ESA's) interdisciplinary equipment test campaign (PANGAEA-X) at a lunar/Mars analog site in Lanzarote, Spain. We used a heterogeneous fleet of unmanned aerial vehicles (UAVs)-a swarm-to study the interplay of systems operations and human factors. Human operators directed the swarm via ad hoc networks and data-sharing protocols to explore unknown areas under two control modes: in one, the operator instructed each robot separately; in the other, the operator provided general guidance to the swarm, which self-organized via a combination of distributed decision making and consensus building. We assessed cognitive load via pupillometry for each condition and perceived task demand and intuitiveness via selfreport. Our results show that implementing higher autonomy with swarm intelligence can reduce workload, freeing the operator for other tasks such as overseeing strategy and communication. Future work will further leverage advances in swarm intelligence for exploration missions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".