Hopping mobility concept for search and rescue robots
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose This paper seeks to present recent work demonstrating the feasibility of Microbots' mobility in rough terrain. Microbots are a new search and rescue concept based on the deployment of teams of small spherical mobile robots. In this concept, hundreds to thousands of cm‐scale, sub‐kilogram Microbots are released over a search site such as collapsed building rubble or caves. Microbots use hopping, bouncing, and rolling to infiltrate subterranean spaces in search of possible survivors. Design/methodology/approach The feasibility of the Microbot mobility concept is evaluated through laboratory prototypes and mobility simulations. Findings Experimental studies have demonstrated the feasibility of using dielectric elastomer actuators (DEAs) to generate autonomous hops. High‐efficiency hydrogen fuel cells were shown to be able to power DEAs. Simulation results show that Microbots of proper diameter and hop height can successfully traverse very rough terrains. Research limitations/implications The implication of this research is that small hopping robots are appropriate for certain search and rescue missions. The limitation of the research to date is that issues of control, path planning, and communication have not yet been addressed. Practical implications Key technologies of the Microbot mobility, that use high‐energy‐density micro fuel cells combined with low cost and lightweight DEAs, are feasible. These technologies have the potential to make a significant impact on the search and rescue robots. Originality/value These results suggest that a team of Microbots‐based DEAs and micro fuel cells can be a useful and effective tool for search and rescue operations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it