Biblio

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.

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.

Biomimetic flapping wing vehicles have attracted recent interest because of their numerous potential military and civilian applications. In this paper we describe the design of a multi-agent adaptive controller for such a vehicle. This controller is responsible for estimating the vehicle pose (position and orientation) and then generating four parameters needed for split-cycle control of wing movements to correct pose errors. These parameters are produced via a subsumption architecture rule base. The control strategy is fault tolerant. Using an online learning process an agent continuously monitors the vehicle's behavior and initiates diagnostics if the behavior has degraded. This agent can then autonomously adapt the rule base if necessary. Each rule base is constructed using a combination of extrinsic and intrinsic evolution. Details on the vehicle, the multi-agent system architecture, agent task scheduling, rule base design, and vehicle control are provided.

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.

Those employing Evolutionary Algorithms (EA) are constantly challenged to engineer candidate solution representations that balance expressive power (I.E. can a wide variety of potentially useful solutions be represented?) and meta-heuristic search support (I.E. does the representation support fast acquisition and subsequent fine-tuning of adequate solution candidates). In previous work with a simulated insect-like Flapping-Wing Micro Air Vehicle (FW-MAV), an evolutionary algorithm was employed to blend descriptions of wing flapping patterns to restore correct flight behavior after physical damage to one or both of the wings. Some preliminary work had been done to reduce the overall size of the search space as a means of improving time required to acquire a solution. This of course would likely sacrifice breadth of solutions types and potential expressive power of the representation. In this work, we focus on methods to improve performance by augmenting EA search to dynamically restrict and open access to the whole space to improve solution acquisition time without sacrificing expressive power of the representation. This paper will describe some potential restriction/access control methods and provide preliminary experimental results on the efficacy of these methods in the context of adapting FW-MAV wing gaits.