Real-Time Efficient Exploration in Unknown Dynamic Environments Using MAVs
Bibliographic record
Abstract
Micro aerial vehicles (MAVs) have been acknowledged as an influential technology for indoor search and rescue operations. The time constraint is a crucial factor in most search and rescue operations. The employed MAVs in indoor environments are characterized by short endurance flight time and limited payload weights. Hence, adding more batteries to extend the flight time is practically not feasible. Typically, most of the indoor missions’ environments might not be accessed and remain unknown. Working in such environments requires effective exploration and information gathering to save time and maximize the coverage area. Furthermore, due to the dynamism of such environments, choosing the least risky trajectory is an important task. This paper proposes a real-time active exploration technique which is capable of efficiently generating paths that minimize the vehicle’s risk and maximize the coverage area. Furthermore, it accomplishes real-time monitoring of sudden changes in the estimated map, due to the dynamic objects, by reevaluating at real-time the destination and trajectory to minimize the risk on the chosen path and simultaneously preserving the maximization of the coverage area. Ultimately, recording the implemented trajectory of the vehicle also assists in time-saving as the vehicle depends on this trajectory during the exit process. The performance of the technique is studied under static and dynamic environments and is also compared with different algorithms.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.001 | 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".