A novel bio-inspired approach with multi-resolution mapping for the path planning of multi-robot system in complex environments
Why this work is in the frame
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Bibliographic record
Abstract
Abstract For multi-robot systems (MRSs), conventional path planning with single resolution mapping is challenging to balance information and computation. Regarding path planning of MRS, the previous research lacked systematic definition, quantitative evaluation, and the consideration of complex environmental factors. In this paper, a new systematic formulation is proposed to redefine the multi-robot path planning problem in complex environments, and evaluate the related solutions of this problem. To solve this problem, a novel bio-inspired approach based on reaction-diffusion system is given to deal with the path planning of MRS in complex environments, such as electromagnetic interference, ocean currents, and so on. Furthermore, a multi-layer neural dynamic network is proposed to describe environments with multiple resolutions, which can improve time performance while ensuring the integrity of environmental information. Comparative experimental results indicate that the proposed approach shows the excellent path planning performance of MRS in complex environments. The stability of the proposed method is determined by the mathematical basis.
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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