Explainable Artificial intelligence for Autonomous UAV Navigation
Bibliographic record
Abstract
Unmanned Aerial Vehicles (UAVs) with limited computational, perception and power resources face significant challenges when navigating autonomously in unfamiliar environments. While artificial intelligence (AI)-assisted algorithms have been used to address these limitations, transparency of the underlying AI models remains a concern, hindering user trust. To address this limitation, this research study proposes a novel, explainable AI-based navigation approach for UAVs to navigate them through unknown environments autonomously. The soft actor-critic (SAC) algorithm and multilayer perceptron (MLP) policies integrated deep reinforcement learning algorithm is developed to derive control actions. This controller is integrated with a novel moving-window gradient-based explainable artificial intelligence (XAI) framework to shed light on the UAV’s decision-making process. The proposed XAI algorithm provides granular insights into how various factors, such as image segments and UAV state features, influence the UAV’s actions. It lays the groundwork for a novel visual explanation approach that segments input depth images to highlight critical navigational cues, augmented by a dynamic color map for precise obstacle identification. Additionally, the study introduces comprehensive textual explanations to provide an in-depth understanding of the UAV’s decision processes, thereby improving the model’s transparency and explainability. The simulation results indicate that the proposed DRL model achieves over 95% success rate. Moreover, evaluations conducted in two distinct environments demonstrate the model’s capability to generate effective and reliable explanations.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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 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".