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Towards Evolved Time to Contact Neurocontrollers for Quadcopters

  • David HowardEmail author
  • Farid Kendoul
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9592)

Abstract

Bio-inspired controllers based on visual odometry — or time to contact — have been previously shown to allow vehicles to navigate in a way that simultaneously specifies both the spatial waypoint and temporal arrival time at the waypoint, based on a single variable, tau (\(\tau \)). In this study, we present an initial investigation into the evolution of neural networks as bio-inspired tau-controllers that achieve successful mappings between \(\tau \) and desired control outputs. As this mapping is highly nonlinear and difficult to hand-design, an evolutionary algorithm is used to progressively optimise a population of neural networks based on quality of generated behaviour. The proposed system is implemented on Hardware-in-the-loop setup and demonstrated for the autonomous landing of a quadcopter. Preliminary results indicate that suitable controllers can be successfully evolved.

Keywords

Neurocontroller Evolutionary algorithm Time to contact Tau theory UAV 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.CSIRO Autonomous Systems Program, QCATBrisbaneAustralia

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