Conference proceeding
Neural Nets and Optic Flow for Autonomous Micro-Air-Vehicle Navigation
Dynamic Systems and Control, Parts A and B, v 73(2), pp 1279-1285
01 Jan 2004
Abstract
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.
Metrics
11 Record Views
17 citations in Scopus
Details
- Title
- Neural Nets and Optic Flow for Autonomous Micro-Air-Vehicle Navigation
- Creators
- Paul Y Oh - Drexel UniversityWilliam E Green - Drexel UniversityGeoffrey Barrows - Centeye (United States)
- Publication Details
- Dynamic Systems and Control, Parts A and B, v 73(2), pp 1279-1285
- Conference
- ASME 2004 International Mechanical Engineering Congress and Exposition (Anaheim, California, United States, 13 Nov 2004–19 Nov 2004)
- Publisher
- ASMEDC
- Resource Type
- Conference proceeding
- Language
- English
- Scopus ID
- 2-s2.0-19644371743
- Other Identifier
- 991019348755704721