Biblio

Evolvable and Adaptive Hardware (EAH) Systems have been a subject of study for about two decades. This paper argues that viewing EAH devices in isolation from the larger systems in which they serve as components is somewhat dangerous in that EAH devices can subvert the design hierarchies upon which designers base verification and validation efforts. The paper proposes augmenting EAH components with additional machinery to enable the application of model-checking and related Cyber-Physical Systems techniques to extract evolving intra-module relationships for formal verification and validation purposes.

On-going effective control of insect-scale Flapping-Wing Micro Air Vehicles could be significantly advantaged by active in-flight control adaptation. Previous work demonstrated that in simulated vehicles with wing membrane damage, in-flight recovery of effective vehicle attitude and vehicle position control precision via use of an in-flight adaptive learning oscillator was possible. A significant portion of the most recent approaches to this problem employed an island-of-fitness compact genetic algorithm (ICGA) for oscillator learning. The work presented in this paper provides the details of a domain specific search space reduction approach implemented with existing ICGA and its effect on the in-flight learning time. Further, it will be demonstrated that the proposed search space reduction methodology is effective in producing an error correcting oscillator configuration rapidly, online, while the vehicle is in normal service. The paper will present specific simulation results demonstrating the value of the search space reduction and discussion of future applications of the technique to this problem domain.

Insect-Scale Flapping-Wing Micro-Air Vehicles (FW-MAVs), can be particularly sensitive to control deficits caused by ongoing wing damage and degradation. Since any such degradation could occur during flight and likely in ways difficult to predict apriori, any automated methods to apply correction would also need to be applied in-flight. Previous work has demonstrated effective recovery of correct flight behavior via online (in service) evolutionary algorithm based learning of new wing-level oscillation patterns. In those works, Evolutionary Algorithms (EAs) were used to continuously adapt wing motion patterns to restore the force generation expected by the flight controller. Due to the requirements for online learning and fast recovery of correct flight behavior, the choice of EA is critical. The work described in this paper replaces previously used oscillator learning algorithms with an Evolution Strategy (ES), an EA variant never previously tested for this application. This paper will demonstrate that this approach is both more effective and faster than previously employed methods. The paper will conclude with a discussion of future applications of the technique within this problem domain.

Evolutionary Computation has been suggested as a means of providing ongoing adaptation of robot controllers. Most often, using Evolutionary Computation to that end focuses on recovery of acceptable robot performance with less attention given to diagnosing the nature of the failure that necessitated the adaptation. In previous work, we introduced the concept of Evolutionary Model Consistency Checking in which candidate robot controller evaluations were dual-purposed for both evolving control solutions and extracting robot fault diagnoses. In that less developed work, we could only detect single wing damage faults in a simulated Flapping Wing Micro Air Vehicle. We now extend the method to enable detection and diagnosis of both single wing and dual wing faults. This paper explains those extensions, demonstrates their efficacy via simulation studies, and provides discussion on the possibility of augmenting EC adaptation by exploiting extracted fault diagnoses to speed EC search.

Previously, we introduced Evolutionary Model Consistency Checking (EMCC) as an adjunct to Evolvable and Adaptive Hardware (EAH) methods. The core idea was to dual-purpose objective function evaluations to simultaneously enable EA search of hardware configurations while simultaneously enabling a model-based inference of the nature of the damage that necessitated the hardware adaptation. We demonstrated the efficacy of this method by modifying a pair of EAH oscillators inside a simulated Flapping-Wing Micro Air Vehicle (FW-MAV). In that work, we were able to show that one could, while online in normal service, evolve wing gait patterns that corrected altitude control errors cause by mechanical wing damage while simultaneously determining, with high precision, what the wing lift force deficits that necessitated the adaptation. In this work, we extend the method to be able to also determine wing drag force deficits. Further, we infer the now extended set of four unknown damage estimates without substantially increasing the number of objective function evaluations required. In this paper we will provide the outlines of a formal derivation of the new inference method plus experimental validation of efficacy. The paper will conclude with commentary on several practical issues, including better containment of estimation error by introducing more in-flight learning trials and why one might argue that these techniques could eventually be used on a true free-flying flapping wing vehicle.

Cyber Physical Systems (CPS) are composed of multiple physical and computing components that are deeply intertwined, operate on differing spatial and temporal scales, and interact with one another in fluid, context dependent, manners. Cyber Physical Systems often include smart components that use local adaptation to improve whole system performance or to provide damage response. Evolvable and Adaptive Hardware (EAH) components, at least conceptually, are often represented as an enabling technology for such smart components. This paper will outline one approach to applying CPS thinking to better address a growing need to address Verification and Validation (V&V) questions related to the use of EAH smart components. It will argue that, perhaps fortuitously, the very adaptations EAH smart components employ for performance improvement may also be employed to maintain V&V capability.

To date, work in evolvable and adaptive hardware (EAH) has been largely isolated from primary inclusion into larger design processes. Almost without exception, EAH efforts are aimed at creating systems whole cloth, creating drop-in replacements for existing components of a larger design, or creating after-the-fact fixes for designs found to be deficient. This paper will discuss early efforts in integrating EAH methods into the design of a controller for a flapping-wing micro air vehicle (FWMAV). The FWMAV project is extensive, multidisciplinary, and on going. Because EAH methods were in consideration during its earliest design stages, this project provides a rich environment in which to explore means of effectively combining EAH and traditional design methodologies. In addition to providing a concrete EAH design that addresses potential problems with FWMAV flight in a unique way, this paper will also provide a provisional list of EAH design integration principles, drawn from our experiences to date.