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2024

Simulation-based Scenario Generation for Robust Hybrid AI for Autonomy

Keno, Hambisa, Pioch, Nicholas J., Guagliano, Christopher et al.

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Application of Unmanned Aerial Vehicles (UAVs) in search and rescue, emergency management, and law enforcement has gained traction with the advent of low-cost platforms and sensor payloads.

The emergence of hybrid neural and symbolic AI approaches for complex reasoning is expected to further push the boundaries of these applications with decreasing levels of human intervention.
However, current UAV simulation environments lack semantic context suited to this hybrid approach.
To address this gap, HAMERITT (Hybrid Ai Mission Environment for RapId Training and Testing) provides a simulation-based autonomy software framework that supports the training, testing and assurance of neuro-symbolic algorithms for autonomous maneuver and perception reasoning.

Simulation-based Scenario Generation for Robust Hybrid AI for Autonomy · Around

Built on

Anytime motion planning using the RRT
Sertac, K.; Walter, M.; Perez, A.; Frazzoli, E.; and Teller, S. 2011 · 2011
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PX4: A node-based multithreaded open source robotics framework for deeply embedded platforms
Lorenz, M.; Honegger, D.; and Pollefeys, M. 2015 · 2015
Earlier work this paper cites.
Airsim: High-fidelity visual and physical simulation for autonomous vehicles
Shah, S.; Dey, D.; Lovett, C.; and Kapoor, A. 2017 · 2017
Earlier work this paper cites.
Scenic: a language for scenario specification and scene generation
Fremont, D. J.; Dreossi, T.; Ghosh, S.; Yue, X.; Sangiovanni-Vincentelli, A. L.; and Seshia, S. A. 2019 · 2019
Earlier work this paper cites.

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