Abstract
In the previous work, it was demonstrated that one can effectively employ CTRNN-EH (a neuromorphic variant of EH method) methodology to evolve neuromorphic flight controllers for a flapping wing robot. This paper describes a novel frequency grouping-based analysis technique, developed to qualitatively decompose the evolved controllers into explainable functional control blocks. A summary of the previous work related to evolving flight controllers for two categories of the controller types, called autonomous and nonautonomous controllers, is provided, and the applicability of the newly developed decomposition analysis for both controller categories is demonstrated. Further, the paper concludes with appropriate discussion of ongoing work and implications for possible future work related to employing the CTRNN-EH methodology and the decomposition analysis techniques presented in this paper.
Highlights
Most, if not all, existing bird-sized and insect-sized flappingwing vehicles possess only a small number of actively controlled degrees of freedom
The CTRNN-EH framework is a neuromorphic variant of the standard Evolvable Hardware paradigm using Continuous Time Recurrent Neural Networks (CTRNNs) as the reconfigurable hardware substrate
We have summarized author’s prior efforts using the Neuromorphic Evolvable Hardware (CTRNN-EH) framework to successfully evolve locomotion and different flight mode controllers, with detailed emphasis on the flight mode controllers
Summary
If not all, existing bird-sized and insect-sized flappingwing vehicles possess only a small number of actively controlled degrees of freedom. The number of controlled degrees of freedom is often minimized to simplify control and to limit the number of bulky actuators carried on board In theory, both bird-sized [1] and insect-sized [2] robots can sustain stable flight with controllers generating actuation signals for only few degrees of freedom. In previous work [4, 7,8,9], the authors were able to demonstrate controllers could be “learned from scratch” by verifying the idea within a framework of neuromorphic evolvable hardware This previous work demonstrated the feasibility of neuromorphic adaptive hardware implementations that provide computational advantage over existing adaptive control techniques using similar neural substrates [10, 11].
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