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This Copy of the Thesis Has Been Supplied on Condition That Anyone University of Plymouth PEARL https://pearl.plymouth.ac.uk 04 University of Plymouth Research Theses 01 Research Theses Main Collection 2013 Distributed Control for Collective Behaviour in Micro-unmanned Aerial Vehicles Ruini, Fabio http://hdl.handle.net/10026.1/1549 University of Plymouth All content in PEARL is protected by copyright law. Author manuscripts are made available in accordance with publisher policies. Please cite only the published version using the details provided on the item record or document. In the absence of an open licence (e.g. Creative Commons), permissions for further reuse of content should be sought from the publisher or author. This copy of the thesis has been supplied on condition that anyone who consults it is understood to recognise that its copyright rests with its author and that no quotation from the thesis and no information derived from it may be published without the author's prior consent. DISTRIBUTED CONTROL FOR COLLECTIVE BEHAVIOUR IN MICRO-UNMANNED AERIAL VEHICLES by FABIO RUINI A thesis submitted to the University of Plymouth in partial fulfilment for the degree of DOCTOR OF PHILOSOPHY School of Computing and Mathematics Faculty of Science and Technology June 2011 This page has been intentionally left blank Distributed Control for Collective Behaviour in Micro-unmanned Aerial Vehicles by Fabio Ruini Abstract The work presented herein focuses on the design of distributed autonomous con- trollers for collective behaviour of Micro-unmanned Aerial Vehicles (MAVs). Two alternative approaches to this topic are introduced: one based upon the Evo- lutionary Robotics (ER) paradigm, the other one upon flocking principles. Three computer simulators have been developed in order to carry out the required exper- iments, all of them having their focus on the modelling of fixed-wing aircraft flight dynamics. The employment of fixed-wing aircraft rather than the omni-directional robots typically employed in collective robotics significantly increases the complex- ity of the challenges that an autonomous controller has to face. This is mostly due to the strict motion constraints associated with fixed-wing platforms, that require a high degree of accuracy by the controller. Concerning the ER approach, the experimental setups elaborated have resulted in controllers that have been evolved in simulation with the following capabilities: (1) navigation across unknown environments, (2) obstacle avoidance, (3) tracking of a moving target, and (4) execution of cooperative and coordinated behaviours based on implicit communication strategies. The design methodology based upon flocking principles has involved tests on computer simulations and subsequent experimentation on real-world robotic plat- forms. A customised implementation of Reynolds' flocking algorithm has been de- veloped and successfully validated through flight tests performed with the swinglet MAV. It has been notably demonstrated how the Evolutionary Robotics approach could be successfully extended to the domain of fixed-wing aerial robotics, which has never received a great deal of attention in the past. The investigations performed have also shown that complex and real physics-based computer simulators are not a compulsory requirement when approaching the domain of aerial robotics, as long as proper autopilot systems (taking care of the "reality gap" issue) are used on the real robots. Contents Abstract 5 Table of Contents 6 List of Figures 14 List of Tables 16 List of Algorithms 17 1 Introduction 23 1.1 Contribution to knowledge . 26 1.2 Thesis outline . 29 2 Evolutionary Robotics: Neural Networks and Evolutionary Com- putation Working Together 33 2.1 Towards Evolutionary Robotics (ER) . 33 2.1.1 Autonomous robotics . 34 2.1.2 The classical approach to autonomous robotics: Shakey the robot and the problem of planning . 35 2.1.3 Behaviour-based Robotics: embodiment and situatedness . 38 2.1.4 Evolutionary Robotics . 41 2.1.5 A \cognitive" approach? The link with epigenetic robotics . 49 2.2 The basics of control theory . 51 2.2.1 Feedback (closed loop) control . 51 2.2.2 Feedforward (open loop) control . 57 2.3 Neural Networks (NNs) . 57 2.3.1 McCulloch and Pitts' artificial neuron (TLU) . 58 2.3.2 Hebbian learning . 59 2.3.3 The properties of TLU networks: a classification example . 61 2.3.4 The limitations of perceptrons: dealing with non-linearly sep- arable classes . 63 2.3.5 Multi-layer perceptrons and the backpropagation algorithm . 66 2.3.6 Networks with memory . 67 2.3.7 Activation/transfer functions . 69 2.4 Evolutionary Computation . 70 2.4.1 The terminology . 70 2.4.2 On natural/biological evolution . 71 2.4.3 Evolutionary Computation: an overview . 73 2.4.4 Evolution Strategies (ESs) . 74 2.4.5 Genetic Algorithms (GAs) . 76 2.4.6 Evolutionary Programming (EP) . 87 2.5 Incremental evolution . 89 2.5.1 Incremental evolution in autonomous robotics . 91 3 Aerial Robotics and Intelligent Control: History and Technologies 95 3.1 Unmanned flight: a brief history . 96 3.1.1 The drives for the UAVs in the military . 98 3.2 UAVs and MAVs . 102 3.2.1 Unmanned Aerial Vehicles (UAVs) . 102 3.2.2 Micro-unmanned Aerial Vehicles (MAVs) . 109 3.2.3 Applications . 113 3.3 Aerial robotics . 124 3.3.1 Fixed-wing MAVs: basic aerodynamics, design issues, charac- terisation and control techniques . 125 3.3.2 Characterisation and attitude determination . 133 3.3.3 Autopilots and autonomous control . 135 4 Distributed Control for Collective Behaviour in MAV Teams: Method- ologies, Challenges, Ethical Considerations and Safety Issues 139 4.1 Intelligent autonomous controllers for collective aerial robots: the scientific literature . 140 4.2 Design methodologies . 148 4.2.1 Incremental geometric flight . 150 4.2.2 Flocking . 151 4.3 The main challenges . 153 4.3.1 Fixed-wing aircraft: motion constraints . 153 4.3.2 Obstacle avoidance . 155 4.3.3 Target tracking . 155 4.3.4 Collective behaviour, distributed control, and cooperation based upon implicit communication . 156 4.3.5 Computation time issues . 158 4.3.6 The reality gap . 159 4.4 Ethical considerations on the military employment of lethal autonomous robots . 160 4.4.1 The laws of war and the ethics of modern conflicts . 161 4.4.2 The experts' point of view on autonomous robotics . 163 4.5 Safety issues . 168 4.6 Plan for the experiments . 171 4.6.1 Defining \success" . 173 4.6.2 On the comparison with alternative design methodologies . 174 5 Simulation Experiments in Urban Layouts 177 5.1 Software simulator . 177 5.1.1 Common features of all simulation setups . 184 5.2 Neural network controllers . 187 5.2.1 Encoding of the input information . 188 5.3 Evolutionary algorithm . 189 5.4 Experiments . 190 5.4.1 Basic scenario (simulations A) . 190 5.4.2 Obstacle avoidance (simulations B) . 202 5.4.3 Moving target (simulations C) . 214 5.4.4 Implicit cooperation (simulations D) . 221 5.5 Conclusions . 233 6 Simulation Experiments in 3D Environments 237 6.1 Software simulator . 237 6.1.1 The evolutionary engine . 238 6.1.2 The viewer . 241 6.1.3 Computation issues . 243 6.1.4 Moving from 2D to 3D . 243 6.1.5 Common features of all simulation setups . 244 6.2 Neural network controllers . 246 6.2.1 Encoding of the input information . 247 6.3 Evolutionary algorithm . 252 6.4 Experiments . 253 6.4.1 Basic scenario (simulations A) . 253 6.4.2 Moving target (simulations B) . 268 6.4.3 Implicit cooperation (simulations C) . 273 6.5 Experiments on incremental evolution . 275 6.5.1 Basic results for A, B, and C setups . 277 6.5.2 The incremental approach . 281 6.6 Conclusions . 287 6.6.1 Multi-threading . 289 6.6.2 Incremental evolution . 289 7 Flocking Behaviour: Towards Experiments on Physical Robots 291 7.1 Robotics platform used: senseFly's swinglet . 292 7.2 Software simulator . 296 7.2.1 Initial formation . 299 7.2.2 Navigation algorithms . 301 7.2.3 Flocking algorithms . 304 7.3 Moving from simulations to reality: overcoming the reality gap . 311 7.3.1 Test field and coordinate systems . 313 7.3.2 Leader following behaviour through explicit communication (rendezvous) . 315 7.3.3 Flocking behaviour . 322 7.4 Conclusions . 328 8 Conclusions and Future Work 331 8.1 Conclusions . 331 8.2 Future work . 336 Bibliography 368 Appendices I A Micro-unmanned Aerial Vehicles: a review I A.1 AeroVironment Inc. .I A.2 Lockheed Martin . VII A.3 AAI Corporation . VIII A.4 Israel Aerospace Industries (IAI) . .XI A.5 Insitu . XIII A.6 Elbit Systems . .XV A.7 AerialRobotics . XVI A.8 Miscellaneous . XVIII A.9 Classification . .XX B Autopilot systems XXIII B.1 SBP400/MNAV . XXIII B.2 Procerus Kestrel System . XXV B.3 MicroPilot MPxx28 . XXVII B.4 Cloud Cap Piccolo . ..
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