Biblio

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.

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.

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.