A novel bio-inspired approach with multi-resolution mapping for the path planning of multi-robot system in complex environments
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".