Article

Interruption-aware recurrent reinforcement learning improves UAS navigation safety for temporary overrides

To address the increased collision risk when unmanned aircraft systems (UAS) resume autonomous navigation after temporary takeover by teleoperation or safety mechanisms, the study proposes three interruption-aware recurrent reinforcement learning architectures that incorporate interruption context into recurrent processing through independent feature streams, dedicated interruption memory, or gated environmental features.

Part of the event: Proposes three interruption-aware recurrent reinforcement learning architectures for UAS navigation

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