Print Email Facebook Twitter Direct Learning of Home Vector Direction Title Direct Learning of Home Vector Direction: Incited by Existing Insect-Inspired Approaches for Local Navigation and Wayfinding Author Firlefyn, Michiel (TU Delft Aerospace Engineering) Contributor de Croon, G.C.H.E. (mentor) Hagenaars, J.J. (mentor) Degree granting institution Delft University of Technology Programme Aerospace Engineering Date 2023-09-28 Abstract Insects have long been recognized for their ability to navigate and return home using visual cues from their nest’s environment. However, the precise mechanism underlying this remarkable homing skill remains a subject of ongoing investigation. Drawing inspiration from the learning flights of honey bees and wasps, we propose a robot navigation method based on directly learning the home vector directions from visual percepts during the learning flight. Subsequently, the robot will travel away from the nest, come back by odometric means, and eliminate the resultant drift by inferring the home vector orientation from the currently experienced view. In this study, a convolutional neural network is employed as learning mechanism in both simulated and real forest environments. Additionally, a comprehensive performance analysis reveals that the network’s homing abilities closely resemble those observed in real insects, all while only utilizing visual and odometric senses. If all images contain sufficient texture and illumination, the average errorsof the inferred home vectors remain below 24°. Moreover, our investigation reveals a noteworthy insight: the trajectory followed during the initial learning flight, for sample image acquisition, exerts a pronounced impact on the network’s output. For instance, a higher density of sample points in proximity to the nest results in a more consistent return. Subject Insect-inspiredVisual homingBio-inspiredNavigation To reference this document use: http://resolver.tudelft.nl/uuid:98b694a1-72fc-4a4f-907b-d511cc7f9bdd Embargo date 2024-04-01 Part of collection Student theses Document type master thesis Rights © 2023 Michiel Firlefyn Files PDF finalthesis_MichielFirlef ... Report.pdf 339.04 MB Close viewer /islandora/object/uuid:98b694a1-72fc-4a4f-907b-d511cc7f9bdd/datastream/OBJ/view