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

Abstract Themostversatileandrobustflyingmachinesarestillthoseproducedbynaturethroughevolution.Thesolutionstothe6DOFcontrolprob-lemfaced bythesemachines areimplemented in extremelysmallneu-ronal structures comprisingthousandsof neurons.Hence,thebiolog-ical principles of flight control are not only very effective but alsoefficient in terms of their implementation. Animportant question is towhat extent these principles can be generalized to man-made flyingplatforms.Here,thisquestionisinvestigatedinrelationtothecompu-tational and behavioral principles of the opto-motor system of the flyand locust. The aim is to provide a control infrastructure based onlyon biologically plausible and realistic neuronal models of the insectopto-motor system. It is shown that relying solely on vision, biologi-cally constrained neuronal models of the fly visual system suffice forcoursestabilizationandaltitudecontrolofablimp-basedUAV.More-over,thesystemisaugmentedwithacollisionavoidancemodelbasedon the Lobula Giant Movement Detector neuron of the Locust. It isshown that the biologically constrained course stabilization model ishighly robust and that the combined model is able to perform au-tonomous indoor flight.