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Arrays of bioinspired artificial hair-cell airflow velocity sensors canenable flight-by-feel of small, unmanned aircraft. Natural fliers - bats,insects, and birds - have hundred or thousands of velocity sensors distributedacross their wings. Aircraft designers do not have this luxury due to size,weight, and power constraints. The challenge is to identify the best locationsfor a small set of sensors to extract relevant information from the flow fieldfor the prediction of flight control parameters. In this paper, we introducethe data-reducing Sparse Sensor Placement Optimization for Prediction algorithmwhich locates near-optimal sensor placement on airfoils and wings. For two ormore sensors this algorithm finds a set of sensor locations (design point)which predicts angle of attack to within 0.10 degrees and ranks within the top1% of all possible design points found by brute force search. We demonstratethis algorithm on several variations of airfoil sections of infinite and finitewings in clean and noisy data, evaluate model sensitivities, and show that thealgorithm can be used to identify an appropriate number of sensors for a givenaccuracy requirement. Applications for this algorithm are explored for aircraftdesign and flight-by-feel control.

Artificial Hair Sensor Placement Optimization on Airfoils for Angle of Attack Prediction