Print Email Facebook Twitter Evolved Neuromorphic Control for High Speed Divergence-based Landings of MAVs Title Evolved Neuromorphic Control for High Speed Divergence-based Landings of MAVs Author Hagenaars, Jesse (TU Delft Aerospace Engineering) Contributor de Croon, G.C.H.E. (mentor) Paredes-Vallés, F. (mentor) Degree granting institution Delft University of Technology Programme Aerospace Engineering Date 2020-02-13 Abstract Flying insects are capable of autonomous vision-based navigation in cluttered environments, reliably avoiding objects through fast and agile manoeuvres. Meanwhile, insect-scale micro air vehicles still lag far behind their biological counterparts, displaying inferior performance at a fraction of the energy efficiency. In light of this, it is in our interest to try and mimic flying insects in terms of their vision-based navigation capabilities, and consequently apply gained knowledge to a manoeuvre of relevance. This thesis does so through evolving spiking neural networks for controlling divergence-based landings of micro air vehicles, while minimising the network's spike rate. We demonstrate vision-based neuromorphic control for a real-world, continuous problem, as well as the feasibility of extending this controller to one that is end-to-end-learnt, and can work with an event-based camera. Furthermore, we provide insight into the resources required for successfully solving the problem of divergence-based landing, showing that high-resolution control can be learnt with only a single spiking neuron. Finally, we look at evolving only a subset of the spiking neural network's available hyperparameters, suggesting that the best results are obtained when all parameters are affected by the learning process. Subject spiking neural networksoptical flowmicro air vehiclesneuroevolution To reference this document use: http://resolver.tudelft.nl/uuid:48040e88-f507-4676-a5da-2b701a07f387 Embargo date 2021-02-13 Part of collection Student theses Document type master thesis Rights © 2020 Jesse Hagenaars Files PDF finalthesis_JesseHagenaars.pdf 29.4 MB Close viewer /islandora/object/uuid:48040e88-f507-4676-a5da-2b701a07f387/datastream/OBJ/view