Hybrid Framework for UAV Motion Planning and Obstacle Avoidance: Integrating Deep Reinforcement Learning with Fuzzy Logic
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
Utilizing Uncrewed Aerial Vehicles (UAVs) offers a cost-effective and flexible option for various applications. However, achieving collision-free autonomous navigation requires advanced technology and safety assurances. This paper introduces a novel intelligent hybrid control scheme for UAV autonomous cruising and obstacle avoidance tasks. The new hybrid controller leverages deep reinforcement learning algorithms from our previous work with significant upgrades and incorporates a fuzzy logic model, greatly enhancing training efficiency. This paper presents the simulation results in 2D cases, which demonstrate the effectiveness of this approach. The results are also compared with the RL-only method presented in earlier works, highlighting the advantages of the new hybrid method. This research advances the field of safe autonomous navigation for UAVs under challenging airspace conditions.
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 | 0.000 | 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 | 0.001 | 0.001 |
| 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