Reinforcement-Learning-Aided Safe Planning for Aerial Robots to Collect Data in Dynamic Environments
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Bibliographic record
Abstract
We study the data collection problem in an Internet of Things (IoT) network where an unmanned aerial vehicle (UAV) is utilized to aggregate data from a set of IoT devices. We formulate the scheduling and path planning problems for the UAV. The goal of the scheduling problem is to find the sequence of nodes that the UAV will visit to complete the data collection task in the shortest possible time, ensuring that it does not run out of energy during its mission. We express this problem as a mixed-integer nonlinear problem and propose an efficient algorithm to solve the aforementioned NP-hard problem in polynomial time. Path planning problem aims to find a collision-free path for the UAV. While the state-of-the-art schemes have focused on solving the path planning problem in static environments, we study the problem in a dynamic environment with moving obstacles. We develop an algorithm that works on both static and dynamic environments. Our method combines deep reinforcement learning (RL) with graph-based global path planning algorithms to find a collision-free path for the UAV. One important advantage of our RL-based method over the existing studies is its map independency, which allows us to transform the agent’s learning from one environment to another. Via simulation studies, we show that our method is significantly effective in improving the safety of the path planning algorithms in dynamic environments.
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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 | 0.000 | 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