Differential communication with distributed MPC based on occupancy grid

Abstract We introduce a Distributed Model Predictive Control (DMPC) algorithm, which is based on the novel idea of projecting predicted trajectories on a quantised spatial set to reduce the communication load. The scheme exploits advantages of continuous optimisation methods while only quantised data is broadcasted. Further, we set up a differential communication scheme, in which only altered cells instead of the full prediction are broadcasted. While the quantisation reduces the communication effort for the overall system, the differential communication further reduces the effort depending on the chosen cell size. The approach is evaluated in simulations using groups of holonomic and non-holonomic mobile robots.

[1]  Lorenz T. Biegler,et al.  On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming , 2006, Math. Program..

[2]  Marcello Farina,et al.  Application of distributed predictive control to motion and coordination problems for unicycle autonomous robots , 2015, Robotics Auton. Syst..

[3]  Yiguang Hong,et al.  Quantized Subgradient Algorithm and Data-Rate Analysis for Distributed Optimization , 2014, IEEE Transactions on Control of Network Systems.

[4]  Jan Lunze,et al.  Control theory of digitally networked dynamic systems , 2014 .

[5]  Karl Worthmann,et al.  Model Predictive Control of Nonholonomic Mobile Robots Without Stabilizing Constraints and Costs , 2016, IEEE Transactions on Control Systems Technology.

[6]  Christoph Walter,et al.  Operating articulated objects with force sensitive mobile manipulators , 2015, 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA).

[7]  Jonathan P. How,et al.  Robust distributed model predictive control , 2007, Int. J. Control.

[8]  Huan Zhang,et al.  Formation control of weak autonomous robots , 2011, IEEE Conference on Decision and Control and European Control Conference.

[9]  Jürgen Pannek,et al.  A prediction based control scheme for networked systems with delays and packet dropouts , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[10]  Jürgen Pannek,et al.  Occupancy grid based distributed MPC for mobile robots , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[11]  Jürgen Pannek,et al.  Nonlinear Model Predictive Control : Theory and Algorithms. 2nd Edition , 2017 .

[12]  Jürgen Pannek Parallelizing a state exchange strategy for noncooperative distributed NMPC , 2013, Syst. Control. Lett..

[13]  Konstantin Kondak,et al.  Autonomous transportation and deployment with aerial robots for search and rescue missions , 2011, J. Field Robotics.

[14]  Daniel E. Quevedo,et al.  Event-Triggered Quantized Communication-Based Distributed Convex Optimization , 2018, IEEE Transactions on Control of Network Systems.

[15]  George K. I. Mann,et al.  An Optimization Based Approach for Relative Localization and Relative Tracking Control in Multi-Robot Systems , 2017, J. Intell. Robotic Syst..

[16]  R. Bhushan Gopaluni,et al.  Model Predictive Control in Industry: Challenges and Opportunities , 2015 .

[17]  Karl Worthmann,et al.  Distributed and Decentralized Control of Residential Energy Systems Incorporating Battery Storage , 2015, IEEE Transactions on Smart Grid.

[18]  Z. Artstein Stabilization with relaxed controls , 1983 .

[19]  Abraham Sánchez López,et al.  Exploring unknown environments with mobile robots using SRT-Radial , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[20]  Karl Worthmann,et al.  A Distributed NMPC Scheme without Stabilizing Terminal Constraints , 2012 .

[21]  Jürgen Pannek,et al.  A networked unconstrained nonlinear MPC scheme , 2009, 2009 European Control Conference (ECC).

[22]  Melanie Nicole Zeilinger,et al.  Quantization design for distributed optimization with time-varying parameters , 2015, 2015 54th IEEE Conference on Decision and Control (CDC).

[23]  Lars Grne,et al.  Nonlinear Model Predictive Control: Theory and Algorithms , 2011 .

[24]  Karl Worthmann,et al.  Regulation of Differential Drive Robots using Continuous Time MPC without Stabilizing Constraints or Costs , 2015 .

[25]  Walter F. Tichy,et al.  Implementation and evaluation of a revision control system , 1982 .

[26]  Panagiotis D. Christofides,et al.  Distributed model predictive control: A tutorial review and future research directions , 2013, Comput. Chem. Eng..

[27]  Sebastian Sager,et al.  Derivative Based vs. Derivative Free Optimization Methods for Nonlinear Optimum Experimental Design , 2005 .

[28]  Michael Pilato Version Control with Subversion , 2004 .

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

[30]  William B. Dunbar,et al.  Distributed Receding Horizon Control of Vehicle Platoons: Stability and String Stability , 2012, IEEE Transactions on Automatic Control.

[31]  A. Richards,et al.  A decentralized algorithm for robust constrained model predictive control , 2004, Proceedings of the 2004 American Control Conference.

[32]  M. J. D. Powell,et al.  Direct search algorithms for optimization calculations , 1998, Acta Numerica.

[33]  Karl Worthmann,et al.  Quadratic costs do not always work in MPC , 2017, Autom..

[34]  Manuel Fernandez Guasti,et al.  Analytic geometry of some rectilinear figures , 1992 .