Evolving neuromorphic flight control for a flapping‐wing mechanical insect

Abstract

If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The purpose of this paper is to present an approach to employ evolvable hardware concepts, to effectively construct flapping-wing mechanism controllers for micro robots, with the evolved dynamically complex controllers embedded in a, physically realizable, micro-scale reconfigurable substrate. Design/methodology/approach -In this paper, a continuous time recurrent neural network (CTRNN)-evolvable hardware (a neuromorphic variant of evolvable hardware) framework and methodologies are employed in the process of designing the evolution experiments. CTRNN is selected as the neuromorphic reconfigurable substrate with most efficient Minipop Evolutionary Algorithm, configured to drive the evolution process. The uniqueness of the reconfigurable CTRNN substrate preferred for this study is perceived from its universal dynamics approximation capabilities and prospective to realize the same in small area and low power chips, the properties which are very much a basic requirement for flapping-wing based micro robot control. A simulated micro mechanical flapping insect model is employed to conduct the feasibility study of evolving neuromorphic controllers using the above-mentioned methodology.Findings -It has been demonstrated that the presented neuromorphic evolvable hardware approach can be effectively used to evolve controllers, to produce various flight dynamics like cruising, steering, and altitude gain in a simulated micro mechanical insect. Moreover, an appropriate feasibility is presented, to realize the evolved controllers in small area and lower power chips, with available fabrication techniques and as well as utilizing the complex dynamics nature of CTRNNs to encompass various controls ability in a architecturally static hardware circuit, which are more pertinent to meet the constraints of micro robot construction and control. Originality/value -The proposed neuromorphic evolvable hardware approach along with its modules intact (CTRNNs and Minipop) can provide a general mechanism to construct/evolve dynamically complex and optimal controllers for flapping-wing mechanism based micro robots for various environments with least human intervention. Further, the evolved neuromo...

Qualitative Functional Decomposition Analysis of Evolved Neuromorphic Flight Controllers

“…Further, the dynamical module analysis [6,16] that was successful to understand the evolved locomotion controllers might not be applicable for the possible CTRNN-EH flight controllers. Nonetheless, the capabilities of CTRNN-EH controllers to produce smooth dynamics and provide provisions to adapt and modulate those produced dynamics observed in the previous work [5] sufficiently justifies the needed efforts presented in the authors published paper [4,[7][8][9] to evolve flapping flight controllers. Although providing the indetail description of varied possible modes of the CTRNN-EH controllers is beyond the scope of this paper, two basic modes are defined below, which are more pertinent to understand the experiments presented here in the paper.…”

“…Two kinds of non-autonomous flight controllers were successfully evolved, namely, adaptive cruise mode controllers and polymorphic controllers [7,8]. The adaptive cruise mode controllers were similar to their autonomous counterpart except that they were forced to sense the altitude of the MFWR and adapt accordingly during the evolution.…”

“…These are neural network configurations that can produce appropriate oscillatory patterns only when coupled to some other oscillatory system. They are more completely discussed in [8]. As shown in the concept illustrative Figure 2(b), these configurations can generate varied dynamics in the neural network only in sync with specific external triggers/sensor inputs provided to them, thus these would be referred as nonautonomous neuromorphic controllers in the later sections.…”

“…of control, other two autonomous controllers, Altitude-gain and Steer, were also evolved successfully [8,9]. Further details of the autonomous flight mode controller experiments can be found in [7][8][9].…”

“…Even if tabula rasa methods could be made to work, one would incur a responsibility to explain the operation of controllers that, though functional, might operate in ways not conformant with existing explanatory paradigms. 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. Further, this previous work also demonstrated the feasibility of neuromorphic adaptive hardware implementations that provide computational advantage over existing adaptive control techniques using similar neural substrates [10,11].…”