Evolution and analysis of neuromorphic flapping-wing flight controllers.
详细信息
文摘
The control of insect-sized flapping-wing micro air vehicles is attracting increasing interest. Solution of the problem requires construction of a controller that is physically small, extremely power efficient, and capable. In addition, process variation in the creation of very small wings and armatures as well as the potential for accumulating damage and wear over the course of a vehicle's lifetime suggest that controllers be able to self-adapt to the specific and possibly changing nature of the vehicles in which they are embedded. Previous work with Evolvable Hardware Continuous Time Recurrent Neural Networks (CTRNNs) as applied to adaptive control of walking in legged robots suggests that CTRNNs may provide a suitable control solution for flapping-wing micro air vehicles. However, upon complete analysis, it can be seen that perceived similarities between the two problems are somewhat superficial, and that flapping-wing vehicle control requires its own study. This dissertation constitutes the first attempt to apply evolved CTRNN devices to the control of a feasible flapping-wing micro air vehicle. It is organized as a sequence of control experiments of increasing difficulty and explores the following issues, development of behavior-based analog circuit modules, architectures to combine those modules into multi-functional controllers, low-level circuit analyses to explain how evolved modules operate and interact. Also included are experiments in the creation of physically polymorphic behavior modules that combine multiple flight functions into a monolithic analog device. In addition to providing first-of-its-kind feasibility results, this dissertation develops a new frequency-grouping based analysis method to explain the operation of evolved devices.