Formation control of unmanned micro aerial vehicles for straitened environments

This paper presents a novel approach for control and motion planning of formations of multiple unmanned micro aerial vehicles (multi-rotor helicopters, in the literature also often called unmanned aerial vehicles—UAVs or unmanned aerial system—UAS) in cluttered GPS-denied on straitened environments. The proposed method enables us to autonomously design complex maneuvers of a compact Micro Aerial Vehicles (MAV) team in a virtual-leader-follower scheme. The results of the motion planning approach and the required stability of the formation are achieved by migrating the virtual leader along with the hull surrounding the formation. This enables us to suddenly change the formation motion in all directions, independently from the current orientation of the formation, and therefore to fully exploit the maneuverability of small multi-rotor helicopters. The proposed method was verified and its performance has been statistically evaluated in numerous simulations and experiments with a fleet of MAVs.

[1]  Maarouf Saad,et al.  Formation path following control of unicycle-type mobile robots , 2008, 2008 IEEE International Conference on Robotics and Automation.

[2]  Sylvain Bertrand,et al.  Reactive MPC for Autonomous MAV Navigation in Indoor Cluttered Environments: Flight Experiments , 2017 .

[3]  Andrea L'Afflitto,et al.  An Introduction to Nonlinear Robust Control for Unmanned Quadrotor Aircraft: How to Design Control Algorithms for Quadrotors Using Sliding Mode Control and Adaptive Control Techniques [Focus on Education] , 2018, IEEE Control Systems.

[4]  Maxim Likhachev,et al.  Planning for multi-agent teams with leader switching , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[5]  Klaus Schilling,et al.  Control and navigation of formations of car-like robots on a receding horizon , 2009, 2009 IEEE Control Applications, (CCA) & Intelligent Control, (ISIC).

[6]  Martin Saska,et al.  Formations of unmanned micro aerial vehicles led by migrating virtual leader , 2016, 2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV).

[7]  Libor Preucil,et al.  Simple yet stable bearing-only navigation , 2010, J. Field Robotics.

[8]  Toru Namerikawa,et al.  Formation control with collision avoidance for a multi-UAV system using decentralized MPC and consensus-based control , 2015, 2015 European Control Conference (ECC).

[9]  Hung Manh La,et al.  Formation control for autonomous robots with collision and obstacle avoidance using a rotational and repulsive force–based approach , 2019, International Journal of Advanced Robotic Systems.

[10]  Sergio Salazar,et al.  Adaptive consensus algorithms for real‐time operation of multi‐agent systems affected by switching network events , 2017 .

[11]  António Paulo Moreira,et al.  Multi-Robot nonlinear model predictive formation control: the obstacle avoidance problem , 2014, Robotica.

[12]  Thomas Gustafsson,et al.  Distributed model predictive control for unmanned aerial vehicles , 2015, 2015 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS).

[13]  Libor Preucil,et al.  Low-cost embedded system for relative localization in robotic swarms , 2013, 2013 IEEE International Conference on Robotics and Automation.

[14]  Hassan Shraim,et al.  A survey on quadrotors: Configurations, modeling and identification, control, collision avoidance, fault diagnosis and tolerant control , 2018, IEEE Aerospace and Electronic Systems Magazine.

[15]  T. Ohtsuka,et al.  Nonlinear Model Predictive Control of Position and Attitude in a Hexacopter with Three Failed Rotors , 2018 .

[16]  Martin Saska,et al.  Documentation of dark areas of large historical buildings by a formation of unmanned aerial vehicles using model predictive control , 2017, 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA).

[17]  A. Vacavant,et al.  Reconstructions of Noisy Digital Contours with Maximal Primitives Based on Multi-Scale/Irregular Geometric Representation and Generalized Linear Programming , 2017 .

[18]  Elder Moreira Hemerly,et al.  Reconfiguration Between Longitudinal and Circular Formations for Multi-UAV Systems by Using Segments , 2014, Journal of Intelligent & Robotic Systems.

[19]  Stjepan Bogdan,et al.  Multi-Agent Formation Control Based on Bell-Shaped Potential Functions , 2010, J. Intell. Robotic Syst..

[20]  Hyeonbeom Lee,et al.  Trajectory tracking control of multirotors from modelling to experiments: A survey , 2017 .

[21]  Libor Preucil,et al.  A Practical Multirobot Localization System , 2014, J. Intell. Robotic Syst..

[22]  Vijay Kumar,et al.  Cooperative autonomous search, grasping, and delivering in a treasure hunt scenario by a team of unmanned aerial vehicles , 2018, J. Field Robotics.

[23]  Long Wang,et al.  Finite-time formation control for multi-agent systems , 2009, Autom..

[24]  Christopher M. Clark,et al.  Motion planning for formations of mobile robots , 2004, Robotics Auton. Syst..

[25]  Andrea Monteriù Nonlinear Decentralized Model Predictive Control for Unmanned Vehicles Moving in Formation , 2015, Inf. Technol. Control..

[26]  Pedro U. Lima,et al.  Robot formation motion planning using Fast Marching , 2011, Robotics Auton. Syst..

[27]  Wenjie Dong,et al.  Robust Formation Control of Multiple Wheeled Mobile Robots , 2011, J. Intell. Robotic Syst..

[28]  Alisson V. Brito,et al.  Dynamic Leader Allocation in Multi-robot Systems Based on Nonlinear Model Predictive Control , 2020, J. Intell. Robotic Syst..

[29]  Martin Saska,et al.  Embedded model predictive control of unmanned micro aerial vehicles , 2016, 2016 21st International Conference on Methods and Models in Automation and Robotics (MMAR).

[30]  Roland Siegwart,et al.  Vision-Controlled Micro Flying Robots: From System Design to Autonomous Navigation and Mapping in GPS-Denied Environments , 2014, IEEE Robotics & Automation Magazine.

[31]  Vijay Kumar,et al.  Towards a swarm of agile micro quadrotors , 2012, Autonomous Robots.

[32]  André Dias,et al.  Formation control driven by cooperative object tracking , 2015, Robotics Auton. Syst..

[33]  Khac Duc Do,et al.  Practical Formation Control of Multiple Unicycle-Type Mobile Robots with Limited Sensing Ranges , 2011, J. Intell. Robotic Syst..

[34]  Yasushi Hada,et al.  Constrained Model Predictive Control , 2006 .

[35]  Leszek Ambroziak,et al.  Two stage switching control for autonomous formation flight of unmanned aerial vehicles , 2015 .

[36]  Andreas Zell,et al.  Robust nonlinear control approach to nontrivial maneuvers and obstacle avoidance for quadrotor UAV under disturbances , 2017, Robotics Auton. Syst..

[37]  Jeremy Birn,et al.  Digital Lighting and Rendering , 2006 .

[38]  Xiang Yu,et al.  Leader-follower formation control of unmanned aerial vehicles with fault tolerant and collision avoidance capabilities , 2015, 2015 International Conference on Unmanned Aircraft Systems (ICUAS).

[39]  Antonio Franchi,et al.  Fast Mutual Relative Localization of UAVs using Ultraviolet LED Markers , 2018, 2018 International Conference on Unmanned Aircraft Systems (ICUAS).

[40]  Humphrey A. Regis A Theoretical Framework for the Study of the Psychological Sense of Community of English-Speaking Caribbean Immigrants , 1988 .

[41]  Andrea Bonarini,et al.  Intelligent state changing applied to multi-robot systems , 2013, Robotics Auton. Syst..

[42]  Xiaoli Wang,et al.  Leader-Following Formation of Switching Multirobot Systems via Internal Model , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[43]  Vijay Kumar,et al.  Estimation, Control, and Planning for Aggressive Flight With a Small Quadrotor With a Single Camera and IMU , 2017, IEEE Robotics and Automation Letters.

[44]  S. Sathiya Keerthi,et al.  A fast procedure for computing the distance between complex objects in three-dimensional space , 1988, IEEE J. Robotics Autom..

[45]  Yang Liu,et al.  An iterative learning approach to formation control of multi-agent systems , 2012, Syst. Control. Lett..

[46]  Jiwon Seo,et al.  Multiple Leader Candidate and Competitive Position Allocation for Robust Formation against Member Robot Faults , 2015, Sensors.

[47]  Emo Welzl,et al.  Smallest enclosing disks (balls and ellipsoids) , 1991, New Results and New Trends in Computer Science.

[48]  David Q. Mayne,et al.  Constrained model predictive control: Stability and optimality , 2000, Autom..

[49]  Libor Preucil,et al.  Coordination and navigation of heterogeneous MAV–UGV formations localized by a ‘hawk-eye’-like approach under a model predictive control scheme , 2014, Int. J. Robotics Res..

[50]  Kwan-Liu Ma,et al.  Lighting Design for Globally Illuminated Volume Rendering , 2013, IEEE Transactions on Visualization and Computer Graphics.

[51]  Rafael Castro-Linares,et al.  Trajectory tracking for non-holonomic cars: A linear approach to controlled leader-follower formation , 2010, 49th IEEE Conference on Decision and Control (CDC).