A fly-locust based neuronal control system applied to an unmanned aerial vehicle: the invertebrate neuronal principles for course stabilization,altitude control and collision avoidance

Abstract

The most versatile and robust flying machines are still those produced by nature through evolution. The solutions to the 6 DOF control problem faced by these machines are implemented in extremely small neuronal structures comprising thousands of neurons. Hence, the biological principles of flight control are not only very effective but also efficient in terms of their implementation. An important question is to what extent these principles can be generalized to man-made flying platforms. Here, this question is investigated in relation to the computational and behavioral principles of the opto-motor system of the fly and locust. The aim is to provide a control infrastructure based only on biologically plausible and realistic neuronal models of the insect opto-motor system. It is shown that relying solely on vision, biologically constrained neuronal models of the fly visual system suffice for course stabilization and altitude control of a blimp-based UAV. Moreover, the system is augmented with a collision avoidance model based on the Lobula Giant Movement Detector neuron of the Locust. It is shown that the biologically constrained course stabilization model is highly robust and that the combined model is able to perform autonomous indoor flight.

Keywords

biologically based insect vision optic flow neural model LGMD EMD UAV autonomous flight blimp-based altitude control course stabilization Reichardt correlation

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