A bondgraph approach to formation control using relative state measurements

In this paper we apply port-Hamiltonian theory with the bondgraph modelling approach to the problem of formation control using partial measurements of relative positions. We present a control design that drives a group of vehicles to a desired formation without requiring inter-vehicle communications or global position and velocity measurements to be available. Our generic approach is applicable to any form of relative measurement between vehicles, but we specifically consider the important cases of relative bearings and relative distances. In the case of bearings, our theory closely relates to the field of image-based visual servo (IBVS) control. We present simulation results to support the developed theory.

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

[2]  Hiroyuki Kawai,et al.  Passivity-Based Dynamic Visual Feedback Control for Three-Dimensional Target Tracking: Stability and $L_{2}$-Gain Performance Analysis , 2007, IEEE Transactions on Control Systems Technology.

[3]  Antonio Franchi,et al.  Modeling and Control of UAV Bearing Formations with Bilateral High-level Steering , 2012, Int. J. Robotics Res..

[4]  Antonio Franchi,et al.  Bilateral Teleoperation of Groups of Mobile Robots With Time-Varying Topology , 2012, IEEE Transactions on Robotics.

[5]  P. Olver Nonlinear Systems , 2013 .

[6]  Wolfgang Borutzky Bond Graph Modelling And Simulation Of Mechatronic Systems An Introduction Into The Methodology , 2006 .

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

[8]  Antonio Franchi,et al.  Bilateral teleoperation of a group of UAVs with communication delays and switching topology , 2012, 2012 IEEE International Conference on Robotics and Automation.

[9]  Randal W. Beard,et al.  A decentralized scheme for spacecraft formation flying via the virtual structure approach , 2003, Proceedings of the 2003 American Control Conference, 2003..

[10]  Masayuki Fujita,et al.  Passivity-Based Pose Synchronization in Three Dimensions , 2012, IEEE Transactions on Automatic Control.

[11]  François Chaumette,et al.  Visual servo control. II. Advanced approaches [Tutorial] , 2007, IEEE Robotics & Automation Magazine.

[12]  Tucker R. Balch,et al.  Behavior-based formation control for multirobot teams , 1998, IEEE Trans. Robotics Autom..

[13]  A.J. Calise,et al.  Approaches to vision-based formation control , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[14]  George J. Pappas,et al.  Flocking in Fixed and Switching Networks , 2007, IEEE Transactions on Automatic Control.

[15]  Vijay Kumar,et al.  Planning and control for cooperative manipulation and transportation with aerial robots , 2011, Int. J. Robotics Res..

[16]  François Chaumette,et al.  Visual servo control. I. Basic approaches , 2006, IEEE Robotics & Automation Magazine.

[17]  Naomi Ehrich Leonard,et al.  Virtual leaders, artificial potentials and coordinated control of groups , 2001, Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228).

[18]  Vijay Kumar,et al.  Trajectory design and control for aggressive formation flight with quadrotors , 2012, Auton. Robots.

[19]  Xiaoming Hu,et al.  Formation constrained multi-agent control , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[20]  Robert E. Mahony,et al.  A port-Hamiltonian approach to image-based visual servo control for dynamic systems , 2012, Int. J. Robotics Res..

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