Flapping flight provides high maneuverability for indoor environments. To achieve robust intelligence for tasks such as search and indoor navigation, the maneuverability of an ornithopter will be combined with a learning approach which makes minimal assumptions about the nature of disturbances and obstacles. We propose to develop algorithms for ornithopters to cooperate in sensing and navigation in typical indoor environments without prior maps. Our research will be verified with full three dimensional dynamic simulation, a multi-tethered laboratory test-bed, as well as with actual indoor flying ornithopters.
The key research issues to be addressed in this work are: 1) improved ornithopter mechanics and aerodynamics 2) robust ornithopter flight control strategies 3) learning algorithms for cooperative navigation of ornithopters using only simple sensor information
This research will advance understanding of high maneuverability flapping wing vehicles for indoor flight. By combining research from the levels of mechanics to learned behavior in a real indoor environment, we will test how performance at each level can be integrated to achieve robust intelligence.
Our project will provide interdisciplinary education for students in achieving robust intelligence through the combination of mechanics, sensing, control, and learning. This research can lead to flying robots which can robustly enter unknown and hazardous indoor environments, potentially keeping rescue workers out of harms way.
