An Improved Real-Time Path Planning Method Based on Dragonfly Algorithm for Heterogeneous Multi-Robot System
Bibliographic record
Abstract
Heterogeneous multi-robot system is one of the most important research directions in the robotic field. Real-time path planning for heterogeneous multi-robot system under unknown 3D environment is a new challenging research and a hot spot in this field. In this paper, an improved real-time path planning method is proposed for a heterogeneous multi-robot system, which is composed of many unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). In the proposed method, the 3D environment is modelled as a neuron topology map, based on the grid method combined with the bio-inspired neural network. Then a new 3D dynamic movement model for multi-robots is established based on an improved Dragonfly Algorithm (DA). Thus, the movements of the robots are optimized according to the activities of the neurons in the bio-inspired neural network to realize the real-time path planning. Furthermore, some simulations have been carried out. The results show that the proposed method can effectively guide the heterogeneous UAV/UGV system to the target, and has better performance than traditional methods in the real-time path planning tasks.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".