Embedding a wireless transmitter within the space and power constraints of an electronic untethered microrobot
Bibliographic record
Abstract
Standard transmission methods such as RF have been considered for untethered robots that need to send messages and information to an external computer. But, as the overall sizes of such robots decrease, the implementation of such transmission systems within the space and power constraints becomes more challenging. For microrobots, the challenge is even greater and furthermore, the overall size of such microrobots can often decrease to a level where the implementation of transmitters as we know them today, cannot be implemented due to several reasons. First, known transmitters require a relatively large amount of electrical power, which is a concern for microrobots. Indeed, a power source such as a battery cannot be embedded since it will be much larger than the microrobot itself. Power can be induced but again, the size of the reception coils capable of providing sufficient electrical power for the transmitter would also be much larger than the robot itself. Another constraint is the overall size of the transmission antennae which would be larger than a single microrobot. As such, a new transmission scheme that can be embedded in an intelligent microrobot with overall dimensions slightly larger than the human hair thickness is briefly introduced. When operating in proximity of an extremely sensitive magnetic receptor, a small electrical current provided by miniature photovoltaic cells on top of the microrobot can be used to generate a local electromagnetic field perturbation sufficiently high to be detected. By modulating such local electromagnetic field from sensory information captured by the microrobot, data and communication commands can be transmitted based on sensory information to an external central computer that could be used to coordinate a swarm of such intelligent microrobots.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".