Visible to the public Biblio

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Conference Paper
J. C. Gallagher, S. Boddhu, E. Matson, G. Greenwood.  2014.  Improvements to Evolutionary Model Consistency Checking for a Flapping-Wing Micro Air Vehicle. 2014 IEEE International Conference on Evolvable Systems. :203-210.

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.

G. Greenwood, M. Podhradsky, J. Gallagher, E. Matson.  2015.  A Multi-Agent System for Autonomous Adaptive Control of a Flapping-Wing Micro Air Vehicle. 2015 IEEE Symposium Series on Computational Intelligence. :1073-1080.

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.