Towards the Development of a Robust Path Planner for Autonomous Drones
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 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 | 0.001 | 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