Publication:
Learning Autonomous Flight Controllers with Spiking Neural Networks

dc.contributor.advisor Garratt, Matthew en_US
dc.contributor.advisor Anavatti, Sreenatha en_US
dc.contributor.advisor Howard, David en_US
dc.contributor.author Qiu, Huanneng en_US
dc.date.accessioned 2022-03-15T08:53:49Z
dc.date.available 2022-03-15T08:53:49Z
dc.date.issued 2021 en_US
dc.description.abstract The ability of a robot to adapt in-mission to achieve an assigned goal is highly desirable. This thesis project places an emphasis on employing learning-based intelligent control methodologies to the development and implementation of an autonomous unmanned aerial vehicle (UAV). Flight control is carried out by evolving spiking neural networks (SNNs) with Hebbian plasticity. The proposed implementation is capable of learning and self-adaptation to model variations and uncertainties when the controller learned in simulation is deployed on a physical platform. Controller development for small multicopters often relies on simulations as an intermediate step, providing cheap, parallelisable, observable and reproducible optimisation with no risk of damage to hardware. Although model-based approaches have been widely utilised in the process of development, loss of performance can be observed on the target platform due to simplification of system dynamics in simulation (e.g., aerodynamics, servo dynamics, sensor uncertainties). Ignorance of these effects in simulation can significantly deteriorate performance when the controller is deployed. Previous approaches often require mathematical or simulation models with a high level of accuracy which can be difficult to obtain. This thesis, on the other hand, attempts to cross the reality gap between a low-fidelity simulation and the real platform. This is done using synaptic plasticity to adapt the SNN controller evolved in simulation to the actual UAV dynamics. The primary contribution of this work is the implementation of a procedural methodology for SNN control that integrates bioinspired learning mechanisms with artificial evolution, with an SNN library package (i.e. eSpinn) developed by the author. Distinct from existing SNN simulators that mainly focus on large-scale neuron interactions and learning mechanisms from a neuroscience perspective, the eSpinn library draws particular attention to embedded implementations on hardware that is applicable for problems in the robotic domain. This C++ software package is not only able to support simulations in the MATLAB and Python environment, allowing rapid prototyping and validation in simulation; but also capable of seamless transition between simulation and deployment on the embedded platforms. This work implements a modified version of the NEAT neuroevolution algorithm and leverages the power of evolutionary computation to discover functional controller compositions and optimise plasticity mechanisms for online adaptation. With the eSpinn software package the development of spiking neurocontrollers for all degrees of freedom of the UAV is demonstrated in simulation. Plastic height control is carried out on a physical hexacopter platform. Through a set of experiments it is shown that the evolved plastic controller can maintain its functionality by self-adapting to model changes and uncertainties that take place after evolutionary training, and consequently exhibit better performance than its non-plastic counterpart. en_US
dc.identifier.uri http://hdl.handle.net/1959.4/71209
dc.language English
dc.language.iso EN en_US
dc.publisher UNSW, Sydney en_US
dc.rights CC BY-NC-ND 3.0 en_US
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/3.0/au/ en_US
dc.subject.other Evolutionary Robotics en_US
dc.subject.other Spiking Neural Network en_US
dc.subject.other Unmanned Aerial Vehicle en_US
dc.subject.other Neuroevolution en_US
dc.subject.other Hebbian Plasticity en_US
dc.subject.other Reality Gap en_US
dc.subject.other Online Adaptation en_US
dc.title Learning Autonomous Flight Controllers with Spiking Neural Networks en_US
dc.type Thesis en_US
dcterms.accessRights open access
dcterms.rightsHolder Qiu, Huanneng
dspace.entity.type Publication en_US
unsw.accessRights.uri https://purl.org/coar/access_right/c_abf2
unsw.date.embargo 2022-05-16 en_US
unsw.description.embargoNote Embargoed until 2022-05-16
unsw.identifier.doi https://doi.org/10.26190/unsworks/2388
unsw.relation.faculty UNSW Canberra
unsw.relation.originalPublicationAffiliation Qiu, Huanneng, School of Engineering and Information Technology, UNSW Canberra, UNSW en_US
unsw.relation.originalPublicationAffiliation Garratt, Matthew, School of Engineering and Information Technology, UNSW Canberra, UNSW en_US
unsw.relation.originalPublicationAffiliation Anavatti, Sreenatha, School of Engineering and Information Technology, UNSW Canberra, UNSW en_US
unsw.relation.originalPublicationAffiliation Howard, David, Data61, CSIRO en_US
unsw.relation.school School of Engineering and Information Technology *
unsw.thesis.degreetype PhD Doctorate en_US
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