Projects:2018s1-107 Evolution of Spiking Neural Networks for UAV Control

2018-04-11T10:32:03Z

A1686498: Edited reference list

= Introduction =
Every unmanned aerial vehicle (UAV) system contains a flight controller, which is responsile for generating corrective roll, pitch, yaw and thrust signals from information about where the UAV is and where it should be. A flight controller is often utilised by higher-level control structures to achieve truly autonomous navigation behaviours, such as path planning and object avoidance.

We are proposing to use a biologically-inspired computational model, called a spiking neural network (SNN), to perform UAV flight control. As the 'tuning' or optimisation of a flight controller depends heavily upon the physical aspects of the UAV, we are particularly interested in automating and reducing the time required to perform this controller optimisation in general, and not just for a single UAV.

We hence aim to culminate our research by developing an SNN flight controller that may be used to efficiently control a UAV system when provided with suitable state information.

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= Abstract =
Proportional-integral-differential (PID) flight controllers see widespread for the flight control of autonomous unmanned aerial vehicles (UAV)<ref>A. Zulu and S. John, “A review of control algorithms for autonomous quadrotors”, in ''ArXiv Computing Research Repository - Systems and Control'', 2016, submission 02622</ref>. while PID control schemes are generally easy to tune and implement, they are fundamentally linear control schemes. As quadcopter and hexacopter system dynamics are nonlinear<ref>K. Nemirsky, ''Simulated Annealing-based Optimal Proportional-Integral-Derivative (PID) Controller Design: A Case Study on Nonlinear Quadcopter Dynamics'', San Jose State University, 2017</ref>, they have been shown to benefit from nonlinear control schemes, improving flight control<ref> L. Sanchez, et al, “Nonlinear and optimal real-time control of a rotary-wing UAV”. In ''American Control Conference (ACC), June 27-29, 2012, Montreal, Canada'', 2012, pp. 3857-3862</ref>.

Spiking neural networks (SNNs) are a biologically-inspired, nonlinear computational model. An SNN consists of a network of small processing nodes called biological neurons, which communicate through precisely timed binary 'spikes'. A nonlinear system of differential equations governs the relationship between a neuron’s internal state, its inputs and its outputs.The processing power of spiking neural networks has is believed to be significantly greater than that of conventional artificial neural networks<ref> W. Maass, “Networks of spiking neurons: the third generation of neural network models” in ''Neural networks'', Vol 10, no 9, 1997, pp.1659-1671</ref>.

The nonlinearity of SNNs raises the possibility of improved flight controller performance in comparison to linear PID architectures. One such potential area of improvement is a common tradeoff between disturbance rejection and set-point tracking<ref>Y, Li, K. Ang, G Chong, “PID control system analysis and design” in ''IEEE Control Systems'', Vol 26, no. 1,, 2006, pp.32-41.</ref>.

The nonlinearity of SNNs however comes at a cost: conventional artificial neural networks training algorithms cannot be directly applied to optimise the control network. Fortunately, biologically-inspired search algorithms known as evolutionary algorithms (EAs) may be employed to search the network parameter space to develop a high performance controller.

We hence aim to culminate our research by developing a Spiking neural network flight controller that may be used to efficiently control a UAV system when provided with suitable state information.

= Proof of Concept =
Our first set of development milestones concern the development of a single-input, single-output (SISO) SNN controller for a relatively simple control problem, namely position control for a single DC motor. We aim to accurately control both a simulated and physical DC motor with SNNs.

It is envisaged that this subproject will enable us to compare evolutionary algorithms used to optimise the control networks as well as to provide an initial insight into the differences between simulated and physical control plants.

= Simulated UAV SNN Flight Control =
After we have solved a simple SISO control problem, we aim to then control a simulated UAV with a spiking neural network controller. This promises to be a more complicated, multiple-input multiple-output (MIMO) control problem, likely requiring a significantly more complicated control network to solve.

This subproject is predicted to provide insight into how a more difficult control problem will affect the required time to optimise an SNN controller.

= Control of a UAV in hardware with a SNN =
The control of a physical UAV represents the culmination of our research. A specialised testing chamber will be constructed which faithfully reports the state of the UAV under control to a SNN control network. The testing chamber shall also physically restrain the UAV for safety and provide feedback to an evolutionary algorithm regarding the performance of each flight controller.

SNN flight controllers will likely be deployed to a field-programmable gate array (FPGA) to achieve the required processing speed for the calculation of control signals several hundred times a second.

= Group members =
Lachlan Bateman

Paul Elson

Matthew Astachnowicz

Alexander Makarowsky

= Supervisors =
Dr Hong Gunn Chew

Dr Braden Phillips

Dr David Howard (CSIRO)

= Sponsor =
CSIRO

= References =
{{Reflist}}

Projects:2018s1-107 Evolution of Spiking Neural Networks for UAV Control

2018-04-04T12:23:10Z

A1686498: Initial set-up with a few headings

= Introduction =
Need to fill

__TOC__
= Abstract =
Need to fill

= Proof of Concept =
=== Introduction ===

= Control of a UAV in software with a SNN =

= Control of a UAV in hardware with a SNN =

= Group members =
Lachlan Bateman

Paul Elson

Matthew Astachnowicz

Alexander Makarowsky

= Supervisors =
Dr Hong Gunn Chew

Braden Phillips

David Howard (CSIRO)

= Sponsor =
CSIRO

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Projects:2018s1-107 Evolution of Spiking Neural Networks for UAV Control

Projects:2018s1-107 Evolution of Spiking Neural Networks for UAV Control