Dynamic Programming-Based Multi-Spot Path Planning and LQR Control for Autonomous UAV Firefighting
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
Wildfires pose escalating threats to ecosystems, infrastructure, and human safety. This paper presents an integrated autonomous UAV-based wildfire suppression system designed to execute multiple fire-spot extinguishing missions efficiently. The proposed framework assumes prior detection of wildfire locations and comprises two main modules: a trajectory planning algorithm using dynamic programming and a Linear Quadratic Regulator (LQR) controller for optimal trajectory tracking. The dynamic programming algorithm minimizes the flight distance of the multiple fire-spots firefighting trajectory. The LQR controller is developed based on a linearized model of the quadrotor UAV and ensures accurate trajectory tracking. The effectiveness of the proposed system is demonstrated through MATLAB simulations and validated via outdoor experiments using the DJI M300 UAV platform equipped with a custom-designed water-dropping mechanism. Results confirm the feasibility of the system in suppressing multiple wildfire spots autonomously.
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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 it