Model predictive cooperative localization control of multiple UAVs using potential function sensor constraints

The global localization of multiple mobile robots can be achieved cost efficiently by localizing one robot globally and the others in relation to it using local sensor data. However, the drawback of this cooperative localization is the requirement of continuous sensor information. Due to a limited sensor perception space, the tracking task to continuously maintain this sensor information is challenging. To address this problem, this contribution is presenting a model predictive control (MPC) approach for such cooperative localization scenarios. In particular, the present work shows a novel workflow to describe sensor limitations with the help of potential functions. In addition, a compact motion model for multi-rotor drones is introduced to achieve MPC real-time capability. The effectiveness of the presented approach is demonstrated in a numerical simulation, an experimental indoor scenario with two quadrotors as well as multiple indoor scenarios of a quadrotor obstacle evasion maneuver.

[1]  Mohamed Darouach,et al.  Nonlinear model predictive extended eco-cruise control for battery electric vehicles , 2016, 2016 24th Mediterranean Conference on Control and Automation (MED).

[2]  Maarouf Saad,et al.  Nonlinear coordination control for a group of mobile robots using a virtual structure , 2011 .

[3]  Randal W. Beard,et al.  A coordination architecture for spacecraft formation control , 2001, IEEE Trans. Control. Syst. Technol..

[4]  H. Isil Bozma,et al.  Artificial potential functions based camera movements and visual behaviors in attentive robots , 2012, Auton. Robots.

[5]  Alberto Bemporad,et al.  Decentralized linear time-varying model predictive control of a formation of unmanned aerial vehicles , 2011, IEEE Conference on Decision and Control and European Control Conference.

[6]  Nicolas Mansard,et al.  Trajectory generation for quadrotor based systems using numerical optimal control , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[7]  Vijay Kumar,et al.  Controlling formations of multiple mobile robots , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[8]  Fumitoshi Matsuno,et al.  Model Predictive Formation Control Using Branch-and-Bound Compatible With Collision Avoidance Problems , 2013, IEEE Transactions on Robotics.

[9]  Vijay Kumar,et al.  Decentralized formation control with variable shapes for aerial robots , 2012, 2012 IEEE International Conference on Robotics and Automation.

[10]  Shuzhi Sam Ge,et al.  Leader-follower formation control of underactuated AUVs with leader position measurement , 2009, 2009 IEEE International Conference on Robotics and Automation.

[11]  Knut Graichen,et al.  A Real-Time Gradient Method for Nonlinear Model Predictive Control , 2012 .

[12]  Sourabh Bhattacharya,et al.  Controllability and properties of optimal paths for a differential drive robot with field-of-view constraints , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[13]  H. Jin Kim,et al.  Nonlinear Model Predictive Formation Flight , 2009, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[14]  Andrea Maria Zanchettin,et al.  Constraint-based Model Predictive Control for holonomic mobile manipulators , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[15]  Miguel A. Olivares-Méndez,et al.  A real-time model predictive position control with collision avoidance for commercial low-cost quadrotors , 2016, 2016 IEEE Conference on Control Applications (CCA).

[16]  Toshiyuki Ohtsuka,et al.  A continuation/GMRES method for fast computation of nonlinear receding horizon control , 2004, Autom..

[17]  Shuzhi Sam Ge,et al.  Dynamic Motion Planning for Mobile Robots Using Potential Field Method , 2002, Auton. Robots.

[18]  Hasan Mehrjerdi,et al.  A survey on multiple unmanned vehicles formation control and coordination: Normal and fault situations , 2013, 2013 International Conference on Unmanned Aircraft Systems (ICUAS).

[19]  Nicholas R. Gans,et al.  Homography-Based Control Scheme for Mobile Robots With Nonholonomic and Field-of-View Constraints , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[20]  António Paulo Moreira,et al.  Iterative weighted tuning for a nonlinear model predictive formation control , 2014, 2014 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC).

[21]  Suseong Kim,et al.  Aerial grasping of cylindrical object using visual servoing based on stochastic model predictive control , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[22]  Hans Joachim Ferreau,et al.  Efficient Numerical Methods for Nonlinear MPC and Moving Horizon Estimation , 2009 .

[23]  Richard M. Murray,et al.  INFORMATION FLOW AND COOPERATIVE CONTROL OF VEHICLE FORMATIONS , 2002 .

[24]  Holger Voos,et al.  A modularization approach for nonlinear model predictive control of distributed fast systems , 2016, 2016 24th Mediterranean Conference on Control and Automation (MED).

[25]  K. Graichen.  0 A Real-Time Gradient Method for Nonlinear Model Predictive Control , 2012 .

[26]  Kevin M. Passino,et al.  Swarm Coordination and Control Problems , 2011 .

[27]  Miguel A. Olivares-Méndez,et al.  Implementation and validation of an event-based real-time nonlinear model predictive control framework with ROS interface for single and multi-robot systems , 2017, 2017 IEEE Conference on Control Technology and Applications (CCTA).

[28]  Peter I. Corke.  Robotics, Vision and Control - Fundamental Algorithms In MATLAB® Second, Completely Revised, Extended And Updated Edition, Second Edition , 2017, Springer Tracts in Advanced Robotics.

[29]  Jian Wang,et al.  Formation control of unmanned surface vehicles with vision sensor constraints , 2015, OCEANS 2015 - MTS/IEEE Washington.

[30]  Mingxin Kang,et al.  Continuation/GMRES Method based Nonlinear Model Predictive Control for IC Engines , 2014 .

[31]  Rogelio Lozano,et al.  An adaptive vision-based autopilot for mini flying machines guidance, navigation and control , 2009, Auton. Robots.

[32]  Domenico Prattichizzo,et al.  Discussion of paper by , 2003 .

[33]  Dirk Abel,et al.  Centralized non-convex model predictive control for cooperative collision avoidance of networked vehicles , 2014, 2014 IEEE International Symposium on Intelligent Control (ISIC).

[34]  Ilya Kolmanovsky,et al.  Nonlinear Model Predictive Control of a Diesel Engine Air Path: A Comparison of Constraint Handling and Computational Strategies , 2015 .

[35]  Rafael Murrieta-Cid,et al.  Optimal Paths for Landmark-Based Navigation by Differential-Drive Vehicles With Field-of-View Constraints , 2007, IEEE Transactions on Robotics.

[36]  Marcio de Queiroz,et al.  Adaptive Rigidity-Based Formation Control for Multirobotic Vehicles With Dynamics , 2015, IEEE Transactions on Control Systems Technology.

[37]  Jay A. Farrell,et al.  Formation control of multiple underactuated surface vessels , 2008 .

[38]  King Hann Lim,et al.  Investigation of activation functions in deep belief network , 2017, 2017 2nd International Conference on Control and Robotics Engineering (ICCRE).

[39]  Moritz Diehl,et al.  A real‐time algorithm for nonlinear receding horizon control using multiple shooting and continuation/Krylov method , 2009 .

[40]  Sylvain Bertrand,et al.  MPC Strategies for Cooperative Guidance of Autonomous Vehicles , 2014 .

[41]  António Paulo Moreira,et al.  Multi-robot nonlinear model predictive formation control: Moving target and target absence , 2013, Robotics Auton. Syst..

[42]  Beno Benhabib,et al.  Visual-model-based, real-time 3D pose tracking for autonomous navigation: methodology and experiments , 2008, Auton. Robots.

[43]  Camillo J. Taylor,et al.  A vision-based formation control framework , 2002, IEEE Trans. Robotics Autom..

[44]  Wei Ding,et al.  Real-time model predictive control for keeping a quadrotor visible on the camera field-of-view of a ground robot , 2016, 2016 American Control Conference (ACC).

[45]  Randal W. Beard,et al.  A decentralized approach to formation maneuvers , 2003, IEEE Trans. Robotics Autom..

[46]  M. Diehl,et al.  Nonlinear receding horizon control of an underactuated hovercraft with a multiple-shooting-based algorithm , 2006 .