A systematic review on metaheuristic approaches for autonomous path planning of unmanned aerial vehicles
Bibliographic record
Abstract
In the path planning of UAVs, autonomous decision-making and control are challenging tasks in the uncertain 3D environment consisting of static and dynamic obstacles. Hence, the selection of appropriate path-planning approaches is essential. In the proposed work, we have considered the meta-heuristic approaches only for an in-depth review. Metaheuristic approaches have been remarkably known for solving complex problems, optimal solutions, and lesser computational complexity compared to deterministic approaches that produce an inefficient solution. An in-depth review has been made by considering the approaches used for path planning, their advantages, disadvantages, applications, the type of time domain (offline or online), type of environment (simulation or real time), hybridization with other approaches, single or multiple UAV system, and obstacle handled (static or dynamic). It is observed that current meta-heuristic methods face constraints like inadequate convergence rates, entrapment in local optima, and complex operations, necessitating continuous development of novel approaches. Implementation of path-planning approaches are very much limited to simulation study over experimental analysis. Hybrid algorithms emerge as a potential solution for tackling these hurdles and optimizing UAV navigation, particularly in dynamic environments involving multiple UAVs. The paper highlights key research gaps, trends, along with prospects in the field of research.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".