Bibliography

Table of contents

• Books
• Quadrotor simulation
• Quadrotor swarms
  • Thoery
  • Using Model Predictive Control
  • Using Particle Swarm Optimization
  • Using Machine learning
• Trajectory generation for quadrotors
• Design and modeling for quadrotors
• Swarm of UAV's and UGV's
  • Using MPC
  • Vision based
• SLAM
• Survery
• Model Predictive Control
• Particle Swarm Optimization
• Positioning Systems
• Failure mitigation in quadrotors
• Quadrotors landing
• Quadrotors Applications

Books

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• Handbook of Unmanned Aerial Vehicles K. P. ValavanisGeorge, J. Vachtsevanos (2014).

Quadrotor simulation

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• Comprehensive Simulation of Quadrotor UAVs Using ROS and Gazebo J. Meyer, A. Sendobry, S. Kohlbrecher, U. Klingauf, O. von Stryk (2012).
• RotorS – A Modular Gazebo MAV Simulator Framework F. Furrer, M. Burri M. Achtelik, R.Siegwart (2016).
• SwarmLab: a Matlab Drone Swarm Simulator E. Soria, F. Schiano, D. Floreano (2020).
• Evaluation Platform for Micro Aerial Indoor Swarm Robotics C. Steup, S. Mostaghim, S. Mai (2016).
• OpenUAV: A UAV Testbed for the CPS and Robotics Community M. Schmittle, A. Lukina, L. Vacek, J. Das, C. P. Buskirk, S. Rees, J. Sztipanovits, R. Grosu, V. Kumar (2018).
• AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles S. Shah, D. Dey, C. Lovett, A. Kapoor (2017).
• Cyberbotics Ltd. Webots™: Professional Mobile Robot Simulation O. Michel (2004).
• Design and use paradigms for Gazebo, an open-source multi-robot simulator N. Koenig, A. Howard (2003).

Quadrotor swarms

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• The GRASP Multiple Micro UAV Testbed N. Michael, D. Mellinger, Q. Lindsey, V. Kumar (2010).
• Towards A Swarm of Agile Micro Quadrotors A. Kushleyev, D. Mellinger, V. Kumar (2013).
• Optimized flocking of autonomous drones in confined environmentsG. Vásárhelyi, Youtube, Code (2018).
• Outdoor flocking and formation flight with autonomous aerial robots Gábor Vásárhelyi, Csaba Virágh, Gergő Somorjai, Norbert Tarcai, Tamás Szörényi, T. Nepusz, T. Vicsek (2014)
• Flocking algorithm for autonomous flying robotsCsaba Virágh, Gábor Vásárhelyi, Norbert Tarcai, Tamás Szörényi, Gergő Somorjai, Tamás Nepusz, Tamás Vicsek (2014).
• Swarms of micro aerial vehicles stabilized under a visual relative localization M. Saska, J. Vakula and L. Přeućil (2014).
• A swarm of flying smartphones G. Loianno, Y. Mulgaonkar, C. Brunner, D. Ahuja, A. Ramanandan, M. Chari. S. Diaz, V. Kumar (2016).
• Crazyswarm: A large nano-quadcopter swarm J. A. Preiss, W. Honig, G. S. Sukhatme, N. Ayanian (2017).
• Design of small, safe and robust quadrotor swarms Y. Mulgaonkar, G. Cross, V. Kumar (2015).
• A platform for aerial robotics research and demonstration: The Flying Machine Arena S. Lupashin,M. Hehn, M. W. Mueller, A. P. Schoellig, M. Sherback, R. D’Andrea (2014).
• SFly: Swarm of micro flying robots M. Achtelik et al. (2012).
• Visual Inertial Odometry Swarm: An Autonomous Swarm of Vision-Based Quadrotors A. Weinstein, A. Cho, G. Loianno, V. Kumar (2018)
• Fast and In Sync: Periodic Swarm Patterns for Quadrotors X. Du, C. E. Luis, M. Vukosavljev, A. P. Schoellig (2018).
• Generation of collision-free trajectories for a quadrocopter fleet: A sequential convex programming approachF. Augugliaro, A. P. Schoellig and R. D'Andrea (2012).

Theory

• Flocking for Multi-Agent Dynamic Systems:Algorithms and Theory Reza Olfati-Saber (2006).
• Collective motion T. Vicsek, A. Zafeiris (2012).
• Flocks, herds and schools: A distributed behavioral model C. W. Reynolds (1987).
• The influence of limited visual sensing on the Reynolds flocking algorithm E. Soria, F. Schiano, D. Floreano (2019).
• Novel Type of Phase Transition in a System of Self-Driven Particles T. Vicsek, A. Czirók, E. Ben-Jacob, I. Cohen, O. Shochet (1995).
• Stable Flocking of Mobile Agents, Part I: Fixed Topology H. G. Tanner, A. Jadbabaie, G. J. Pappas (2003)
• Stable Flocking of Mobile Agents, Part II: Dynamic Topology H. G. Tanner, A. Jadbabaie, G. J. Pappas (2003)

Using Model Predictive control (MPC)

• Nonlinear Model Predictive Control for Multi-Micro Aerial Vehicle Robust Collision Avoidance M. Kamel, J. Alonso-Mora, R. Siegwart, J. Nieto (2017).
• Motion planning and control of formations of micro aerial vehicles M. Saska, Z. Kasl, L. Preucil (2014).
• Embedded Model Predictive Control of Unmanned Micro Aerial Vehicles T. Báča, G. Loianno, M. Saska (2016).
• Nonlinear Model Predictive Formation Flight J. Shin and H. J. Kim (2009).
• Formations of unmanned micro aerial vehicles led by migrating virtual leaderM. Saska, T. Baca and D. Hert (2016).

Using Particle Swarm Optimization (PSO)

• Autonomous deployment of swarms of micro-aerial vehicles in cooperative surveillance M. Saska, J. Chudoba, L. Precil, J. Thomas, G. Loianno, A. Tresnak, V. Vonásek, V. Kumar (2014).
• Swarm Distribution and Deployment for CooperativeSurveillance by Micro-Aerial Vehicles M. Saska, Vojtech Vonásek, J. Chudoba, Justin Thomas, Giuseppe Loianno, V. Kumar (2016).

Using machine learning

• Learning Vision-based Cohesive Flight in Drone Swarms F. Schilling, J. Lecoeur, F. Schiano, D. Floreano (2018).
• Learning Vision-based Flight in Drone Swarms by Imitation F. Schilling, J. Lecoeur, F. Schiano, D. Floreano (2019).
• Deep Reinforcement Learning for Swarm Systems M. Hüttenrauch, A. Šošić, G. Neumann (2019).
• Guided Deep Reinforcement Learning for Swarm Systems, M. Hüttenrauch, A. Šošić, G. Neumann (2017).
• Vision-based flocking in outdoor environments F. Schilling, F. Schiano, D. Floreano (2020).

Trajectory generation for quadrotors

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• Minimum snap trajectory generation and control for quadrotors D. Mellinger and V. Kumar (2011).
• Real-Time Trajectory Generation for Quadrocopters M. Hehn and R. D’Andrea (2015).
• Trajectory Design and Control for Aggressive Formation Flight with Quadrotors M. Turpin, N. Michael, V. Kumar (2012).
• Quadrocopter Trajectory Generation and Control M. Hehn, R. D’Andrea (2011).
• CAPT: Concurrent assignment and planning of trajectories for multiple robots M. Turpin, N. Michael, V. Kumar (2014).
• Polynomial Trajectory Planning for Aggressive Quadrotor Flight in Dense Indoor Environments C. Richter, A. Bry, N. Roy (2016).
• Estimation, Control, and Planning for Aggressive Flight With a Small Quadrotor With a Single Camera and IMU G. Loianno, C. Brunner, G. McGrath and V. Kumar (2017).
• A computationally efficient motion primitive for quadrocoptertrajectory generation M.W. Mueller, M. Hehn, R. D’Andrea (2015).
• A computationally efficient algorithm for state-to-state quadrocopter trajectory generation and feasibility verification M. W. Mueller, Markus Hehn, R. D'Andrea (2013).
• Mixed integer programming for multi-vehicle path-planning T. Schouweaaars (2001).
• Multi-vehicle path planning for non-line of sight communication T. Schouweaaars (2006).

Design and modeling for quadrotors

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• Autonomous helicopter control using reinforcement learning policy search methods J.A Bagnell, J. G. Schneider (2001).
• Autonomous Helicopter Flight via Reinforcement Learning H. Kim, M. Jordan, S. Sastry, A. Ng (2003).
• An Application of Reinforcement Learning to Aerobatic Helicopter Flight P. Abbeel et al (2007).
• Neuroflight: next generation flight control firmware W. Koch, R. Mancuso, A. Bestavros (2019).
• Control of a Quadrotor with Reinforcement Learning J.Hwangbo, I. Sa, R. Siegwart, M. Hutter (2017).
• Reinforcement Learning for UAV Attitude Control W. Koch, R. Mancuso, R. West, A. Bestavros (2018).
• PIXHAWK: A micro aerial vehicle design for autonomous flight using onboard computer vision L. Meier, P. Tanskanen, L. Heng, G. Hee Lee, F. Fraundorfer, M. Pollefeys (2012).
• PIXHAWK: A System for Autonomous Flight using Onboard Computer Vision L. Meier, P. Tanskanen, F. Fraundorfer, M. Pollefeys (2011).
• PX4: A node-based multithreaded open source robotics framework for deeply embedded platforms L. Meier, D. Honegger, M. Pollefeys (2015).
• PID vs LQ control techniques applied to an indoor micro quadrotor S. Bouabdallah, A. Noth and R. Siegwart (2004).
• Design of a four-rotor aerial robot P. Pounds, R. Mahony, P. Hynes & J. Roberts (2002).
• DroNet: Learning to Fly by Driving A. Loquercio, A.I. Maqueda, C.R. Del Blanco, D. Scaramuzza (2018).
• An Open Source and Open Hardware Embedded Metric Optical FlowCMOS Camera for Indoor and Outdoor Applications D. Honegger, L. Meier, P. Tanskanen, M. Pollefeys (2013).
• Design and Control of an Indoor Micro Quadrotor S. Bouabdallah, P. Murrieri, R. Siegwart (2004).
• Design and control of quadrotors with application to autonomous flying S. Bouabdallah (2007).
• Fundamentals of Small Unmanned Aircraft FlightJ. D. Barton (2012).
• Energy-efficient Autonomous Four-rotor Flying Robot Controlled at 1 kHz D. Gurdan, J. Stumpf, M. Achtelik, K. Doth, G. Hirzinger, D. Rus (2007).
• A Scripted Printable Quadrotor: Rapid Design and Fabrication of a Folded MAV A. Mehta, D. Rus, K. Mohta, Yash Mulgaonkar, M. Piccoli, V. Kumar (2013).
• Computational Multicopter Design T. Du, A. Schulz, B. Zhu, B. Bickel, W. Matusik (2013).
• The Navigation and Control technology inside the AR.Drone micro UAV P.J. Bristeau, F. Callou, D. Vissière, N.Petit (2011).
• CAD2RL: Real Single-Image Flight without a Single Real Image F. Sadeghi, S. Levine (2017).
• Learning Monocular Reactive UAV Control in Cluttered Natural Environments S. Ross, N. Melik-Barkhudarov, K. S. Shankar, A. Wendel, D. Dey, J. A. Bagnell, M. Hebert (2012).
• Quadrotor Helicopter Flight Dynamics and Control: Theory and Experiment G. M. Homann, H. Huang, S. L. Waslander, C. Tomlin (2007).
• A 64mW DNN-based Visual Navigation Engine for Autonomous Nano-Drones D. Palossi et al (2019).
• Towards Open-Source , Printable Pico-Quadrotors Y. Mulgaonkar, V. Kumar (2014).

Swarm of UAV's and UGV's

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using MPC

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• Navigation, localization and stabilization of formations of unmanned aerial and ground vehicles M. Saska et al (2013).
• Coordination and navigation of heterogeneous MAV–UGV formations localized by a ‘hawk-eye’-like approach under a model predictive control scheme M. Saska, V. Vonásek, T. Krajník, L. Preucil (2014).
• Complex manoeuvres of heterogeneous MAV-UGV formations using a model predictive control V. Spurný, T. Báca, M. Saska (2016).

vision based

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• A vision-guided autonomous quadrotor in an air-ground multi-robot system W. Li, T. Zhang, K. Kühnlenz (2011).

Simultaneous localization and mapping (SLAM)

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• Onboard IMU and monocular vision based control for MAVs in unknown in- and outdoor environments M. Achtelik, M. Achtelik, S. Weiss, R. Siegwart (2011).
• Vision-Based Autonomous Mapping and Exploration Using a Quadrotor MAV F. Fraundorfer, L. Heng, D. Honegger, G. H. Lee, L. Meier, P. Tanskanen, M. Pollefeys (2012).
• Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High Speed Scenarios A. R. Vidal, H. Rebecq, T. Horstschaefer, D. Scaramuzza (2017).
• Cooperative localization and mapping of MAVs using RGB-D sensors G. Loianno, J. Thomas and V. Kumar (2015).
• MAV-swarms: Unmanned aerial vehicles stabilized along a given path using onboard relative localization M. Saska (2015).
• Camera-Based Navigation of a Low-Cost Quadrocopter J. Engel, J. Sturm, D. Cremers (2012).
• System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization M. Saska, T. Baca, J. Thomas, J. Chudoba, L. Preucil, T. Krajnik, J. Faigl, G. Loianno, V. Kumar (2016).
• A System for the Design and Development of Vision-based Multi-robot Quadrotor Swarm J. L. Sanchez-Lopez, J. Pestana, P. de la Puente, R. Suarez-Fernandezand P. Campoy ().
• MAV Navigation through Indoor Corridors Using Optical Flow S. Zingg, D. Scaramuzza, S. Weiss, R.Siegwart (2010).
• Optimal surveillance coverage for teams of micro aerial vehicles in GPS-denied environments using onboard visionL. Doitsidis, S. Weiss, A. Renzaglia, M. W. Achtelik, E. Kosmatopoulos, R. Siegwart, D. Scaramuzza (2012).
• UAV Formation Flight Based on Nonlinear Model Predictive ControlZ. Chao, S.L. Zhou, L. Ming, W.G. Zhang (2012).
• Visual tracking and following of a quadrocopter by another quadrocopter K. E. Wenzel, A. Masselli and A. Zell (2012).
• Vision-Controlled Micro Flying Robots From System Design to Autonomous Navigation and Mapping in GPS-Denied Environments D. Scaramuzza, M. C. Achtelik, L. Doitsidis, F. Fraundorfer, E. Kosmatopoulos, A. Martinelli, M. W. Achtelik, M. Chli, S. Chatzichristofis, L. Kneip, D. Gurdan, L. Heng, G. Hee Lee, S. Lynen, L. Meier, M. Pollefeys, A. Renzaglia, R. Siegwart, J. C. Stumpf, P. Tanskanen, C. Troiani, S. Weiss

Survey

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• A Survey on Aerial Swarm Robotics S. Chung, A. A. Paranjape, P. Dames, S. Shen, V. Kumar (2018).
• Distributed Processing Applications for UAV/drones: A Survey G. Chmaj, H. Selvaraj (2014).

Positioning systems

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• 3-D relative positioning sensor for indoor flying robots J. F. Roberts, T. Stirling. J. C. Zufferey, D. Floreano (2011).
• Audio-based localization for swarms of micro air vehicles M. Basiri, F. Schill, D. Floreano and P. U. Lima (2014).
• Audio-based Positioning and Target Localization for Swarms of Micro Aerial Vehicles M. Basiri (2015).
• Audio-based Relative Positioning System forMultiple Micro Air Vehicle Systems M. Basiri, F. Schill, D. Floreano, P. U.Lima (2014).
• Practical Multirobot Localization System T. Krajník, M. Nitsche, J. Faigl, P. Vaněk, M. Saska, L. Přeučil, T. Duckett, M. Mejail (2014).
• On-Board Relative Bearing Estimation for Teams of Drones Using Sound M. Basiri, F. Schill, P. Lima, D. Floreano (2016).
• On-board Communication-based Relative Localization for CollisionAvoidance in Micro Air Vehicle teams M. Coppola, K. McGuire, K.Y.W. Scheper, G. C.H.E. de Croon (2017).
• A robot self-localization system using one-way ultra-wideband communication A. Ledergerber, M. Hamer, R. D'Andrea (2015).
• Drone-aided Localization in LoRa IoT Networks V. Delafontaine, F. Schiano, G. Cocco, A. Rusu, D. Floreano (2020).
• Visual Tracking and Control of a Quadcopter Using a Stereo Camera System and Inertial Sensors M. Achtelik, T. Zhang, K. Kuhnlenz , M. Buss (2009).

Failure mitigation in quadrotors.

• Critical subsystem failure mitigation in an indoor UAV testbed M. W. Mueller, R. D’Andrea (2012).
• Relaxed hover solutions for multicopters: application to algorithmic redundancy and novel vehicles M. W. Mueller, R. D’Andrea (2015).
• Stability and control of a quadrocopter despite the complete loss of one, two, or three propellers Youtube M. W. Mueller, R. D’Andrea (2014).

Quadrotors landing

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• Vision-based Control of a Quadrotor for Perching on Lines K. Mohta, V. Kumar and K. Daniilidis (2014).
• A Controller for Autonomous Landing of AR.Drone R. Barták, A. Hraško, D. Obdržálek (2014).
• Deep Reinforcement Learning Strategy for UAV Autonomous Landing on a Moving Platform A. Rodriguez-Ramos, C. Sampedro, H. Bavle, P. D. L. Puente, P. Campoy (2019).
• Autonomous landing on a moving vehicle with an unmanned aerial vehicle T. Baca et al (2019).

Quadrotors applications

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• Quadrocopter Ball Juggling M. Muller, Se. Lupashin and Ra. D’Andrea (2012).
• Quadrocopter Pole Acrobatics D. Brescianini, M. Hehn, R. D’Andrea (2013).
• Building tensile structures with flying machines F. Augugliaro, A. Mirjan, F. Gramazio, M. Kohler, R. D'Andrea (2013).
• Localization, Grasping, and Transportation of Magnetic Objects by a Team of MAVs in Challenging Desert-Like Environments G. Loianno, V. Spurny, J. Thomas, T. Baca, D. Thakur, D. Hert, R. Penicka, T. Krajnik, A. Zhou, A. Cho, M. Saska, V. Kumar (2018).
• The application of small unmanned aerial systems for precision agriculture: a review C. Zhang, John M. Kovacs (2012).
• Documentation of dark areas of large historical buildings by a formation of unmanned aerial vehicles using model predictive control M. Saska, V. Krátký, V. Spurný, T. Báca (2017).
• Opportunities and Challenges with Autonomous Micro Aerial Vehicles V. Kumar, N. Michael (2016).
• Detecting, localizing, and tracking an unknown number of moving targets using a team of mobile robots P. Dames, Pratap Tokekar, V. Kumar (2017).
• The use of unmanned aerial vehicles for an interdisciplinary undergraduate education: Solving quadrotors limitations C. Molina et al. (2014).
• Cooperative quadrocopter ball throwing and catching R. Ritz, M. W. Mueller, M. Hehn, R. D'Andrea (2012).