Barrier Certificates (BCs) provide safety guarantees for dynamical systems. In this work, we attempt to synthesize a neural network BC for a collision avoidance system for a remotely controlled aircraft. We use a counterexample-guided (CEGIS) procedure composed of two elements: a learner that synthesizes a candidate BC, and a sound verifier that either certifies the candidate's validity or generates counterexamples to further guide the learner.
Barrier Certificates (BCs) provide safety guarantees for dynamical systems. In this work, we attempt to synthesize a neural network BC for a collision avoidance system for a remotely controlled aircraft. We use a counterexample-guided (CEGIS) procedure composed of two elements: a learner that synthesizes a candidate BC, and a sound verifier that either certifies the candidate's validity or generates counterexamples to further guide the learner.
Barrier Certificate Synthesis for Airborne Collision Avoidance
VERONESE, LUCA
2025/2026
Abstract
Barrier Certificates (BCs) provide safety guarantees for dynamical systems. In this work, we attempt to synthesize a neural network BC for a collision avoidance system for a remotely controlled aircraft. We use a counterexample-guided (CEGIS) procedure composed of two elements: a learner that synthesizes a candidate BC, and a sound verifier that either certifies the candidate's validity or generates counterexamples to further guide the learner.| File | Dimensione | Formato | |
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veronese_luca.pdf
Accesso riservato
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2.38 MB
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2.38 MB | Adobe PDF |
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https://hdl.handle.net/20.500.12608/108175