Reconnaissance, surveillance and target acquisition tasks in near-Earth environments like forests, caves, tunnels and buildings is a grand challenge. Micro-air-vehicles are a future line of bird-sized flying assets designed to address such a challenge. Needed are light-weight and miniature sensor suites that can provide autonomous collision avoidance in complex environments. Our demonstrations with optic flow microsensors have been promising but controller gain-tuning is often tedious. This paper describes the use of neural nets to automate gain tuning. The overall effect delivers collision avoidance over wide ranges of lighting conditions, contrast and surface textures.

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