Towards the Development of a Robust Path Planner for Autonomous Drones
Bibliographic record
Abstract
Path planning is a major challenge surrounding the development of autonomous drones. For a practical solution, a computationally inexpensive and efficient path planning algorithm needs to be utilized to ensure the smooth operation of drones during long distance missions. Randomly Exploring Random Trees (RRT) and RRT* are sampling based path planning algorithms that have been widely used to solve high dimensional complex problems. RRT* ensures asymptotic optimality; however, it requires a long time to converge to a near optimum solution. RRT* variants have been proposed to improve the rate of convergence. Although many RRT* variants have been proposed, to the best of our knowledge, there has not been a comprehensive analysis comparing the performance of these algorithm. In this study, we perform a detailed comparison of a select group of RRT* variants with RRT and RRT* to determine its potential to be used as a path planner for autonomous drones. We review each algorithm and evaluate its performance by investigating the path cost, execution time and the number of nodes required to generate a path. Experimental results suggest that the performance of the RRT* variants is generally dependent on the type of the environment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".