Towards swarms of communication-enabled and intelligent sensotaxis-based bacterial microrobots capable of collective tasks in an aqueous medium
Why this work is in the frame
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Bibliographic record
Abstract
Experimental data and proofs of concepts are used to show the feasibility of providing the basic components and functionalities required for the implementation of intelligent untethered 150 × 300µm bacterial microrobots capable of sophisticated collective tasks under computer supervision and coordination. More specifically, we show that it is possible to embed within such microrobots, photovoltaic cells supplying ∼4µW necessary to power an internal microelectronic circuit providing embedded intelligence with the capability to communicate commands and data wirelessly to an external computer. We also show that such data or commands transmitted wirelessly could be used to instruct an external computer to send a swarm of flagellated bacteria to move such microrobots towards a specific target based on various sensory information acquired with specific sensors embedded in each microrobots. Similar to chemotaxis used by several species of flagellated bacteria, the algorithms used to move such microrobots could be governed by a larger range of sensory means, leading to what we refer to here as sensotaxis-based hybrid microrobots. The possibility of transmitting a request to a central computer to send a swarm of flagellated magnetotactic bacteria to provide propulsion and steering in order to move accurately to desired locations would allow such microrobots to perform collective tasks. A simple example suggesting the possibility of implementing accurate collective tasks by such hybrid microrobots is demonstrated experimentally where a microstructure emulating a V-shaped microrobot is moved and rotated autonomously using a swarm of approximately 3000 flagellated bacteria towards another similar V-shaped microstructure to form the character ‘M’ as in Microrobot.
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 | 0.000 | 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 | 0.000 |
| 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