Performance evaluation of path planning algorithms for autonomous UAV deployment using two urban scenarios
Bibliographic record
Abstract
In this work, three path planning algorithms for autonomous unmanned aerial vehicles (UAVs) are proposed to be implemented in two urban scenarios. The first scenario is a simple environment without obstacles and the second scenario is a realistic environment in which an urban environment with its infrastructures such as bridge is modeled. In the first step, the test environments are modeled using Gazebo 3D with Robot Operating System. Then, 3D occupancy grid mapping using the Octomap library. Lastly, the path planning simulation is conducted for three sampling-based algorithms, namely, Probabilistic Roadmap (PRM), Rapidly Exploring Random Tree-star (RRT*), and Expansive Space Tree (EST). Three performance objectives from each algorithm are evaluated, i.e., planning time, path length, and number of subpoints. The results show that in a simpler environment with fewer disturbances, PRM or EST is more suited as they take less computational time. In complex environments with more obstacles, EST is more suitable to be implemented as it generates shorter paths with reasonable time but when the shortest path distance is mandatory, selecting RRT* is preferable. Overall, the selected methods can be easily applied in an actual environment by programming the instructions in a microcontroller of a UAV equipped with similar depth camera sensors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".