Qualitative Functional Decomposition Analysis of Evolved Neuromorphic Flight Controllers

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. 1. Introduction Mosaforementionedt, if not all, existing bird-sized and insect-sized flapping-wing vehicles possess only a small number of actively controlled degrees of freedom. In these vehicles, the bulk of the wing motions are generated via a combination of actively driven linkages (motors and armatures, piezoelectric beams, etc.) and passively driven elements (wing flex or rotation via dynamic pressure loading, etc.) [1, 2]. 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. But it would require taking advantage of every possible degree of freedom available in the robot to achieve sophisticated maneuvers that are possible in their biological counterparts. Thus, there exists a possibility that applying a learning or adaptable controller techniques [1, 3–6] to the control of the insect-sized flapping wing vehicles, hereafter referred to as Microlevel Flapping Wing Robots (MFWRs), will likely to produce more biomimetic control and maneuver patterns that evade traditional controller design. One can imagine two basic approaches to the “adaptable controller” problem. First, one might attempt to hybridize an adaptive system to a traditional controller in the hope that the combined system could learn the specific needs of an individual vehicle by augmenting a base controller. Second, one might attempt to construct an adaptable controller that could learn acceptable control laws tabula rasa either all-at-once or via a