Symmetry-Aware Surrogate-Assisted Testing of UAVs
Overview
This research introduces a symmetry-aware surrogate-assisted testing method for UAV autopilots that exploits geometric symmetries to transform obstacle configurations into a shared canonical coordinate frame.
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Event evidence and corrections
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Environment: simulation
“cing the amount of high-fidelity simulation data required for training. The surrogate model is trained to predict the minimum clearance distance between a UAV and surrounding obstacles and is used to guide test generation towards safety-critical scenarios. We evaluate this approach using the Aerialist UAV test bench and benchmark its performance against techniques from the 2024 to 2026 UAV Testing Competitions. Resul”
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Reported quantity · simulations: 2500 other · Basis not reported
“-path characteristics, highlighting the boundaries of geometric canonicalization. To support reproducibility and future research, we provide the trained surrogate model and a dataset comprising more than 2,500 high-fidelity simulations.”
Exact source · revision 1Source owner not reported
Artifact availability · dataset: reported pending
“-path characteristics, highlighting the boundaries of geometric canonicalization. To support reproducibility and future research, we provide the trained surrogate model and a dataset comprising more than 2,500 high-fidelity simulations.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalSymmetry-Aware Surrogate-Assisted Testing of UAVs
This research introduces a symmetry-aware surrogate-assisted testing method for UAV autopilots that exploits geometric symmetries to transform obstacle configurations into a shared canonical coordinate frame.
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