Biblio

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.