Flapping wing micro air vehicles (MAV) have recently attracted ample interest due to their capability of hovering and forward flight with high maneuverability. In our previous work, we dealt with the maximization of thrust through experimental optimization with adaptive sampling. During that study, many wing failures were attributed to overloading or high power consumption. This along with the general restriction of a limited power source on an MAV led us to look into a multi-objective set-up with power as an objective. We initially looked at a one-shot optimization using surrogates to predict Pareto fronts in order to understand the trade-offs between thrust and power. The main objective of this work is to use the knowledge from our previous work and implement a Multi-objective experimental optimization framework using adaptive sampling, based on surrogates fitted to actual experimental data. For this purpose we use a multi-objective implementation of the surrogate-based Efficient Global Optimization (EGO) algorithm (MO-EGO) with multiple surrogates and multiple sampling criteria.
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