A potential field-based PSO approach for cooperative target searching of multi-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
Multi-robot cooperation receives increasing attention. Collaboration among the robots can improve the efficiency and effectiveness for some complex tasks. Target searching in completely unknown environments is a challenging topic for multi-robot cooperation. In this paper, a novel potential field-based particle swarm optimization (PPSO) approach is proposed for a team of mobile robots to cooperatively search targets in unknown environments. The potential field function is the fitness function of the PSO, which is used to evaluate the exploration priority of the unknown area. The proper cooperation rules for the multi-robot system are defined in the proposed PPSO approach. In the simulation studies, various situations are investigated to test the flexibility and applicability of the proposed approach. In addition, the results are compared to the ones with other commonly used methods to demonstrate the advantage of the proposed method in exploration efficiency.
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 | 0.001 | 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