Distributed virtual leader moving formation control using behavior-based MPC

In this paper we present a virtual leader formation control algorithm in which agents collaboratively adapt the formation and its motion in a distributed fashion. The formation is defined in terms of scaling and rotation parameters as well as agent offsets from of a virtual leader. The leader is virtual in that it is purely an artifact of a distributed optimization framework. Adaptation of the formation and control of the virtual leader is achieved through an MPC framework, which allows individual dynamic constraints to be considered in the optimization. By also defining the virtual leader motion in terms of a parameterized controller, distributed parameter optimization techniques are employed to solve the optimization problem at each iteration of the MPC algorithm. This allows agents to respect the underlying network constraints, forming a distributed implementation which scales well to large numbers of agents.

[1]  Jing Wang,et al.  Control approach to distributed optimization , 2010, 2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[2]  Magnus Egerstedt,et al.  Distributed parameterized model predictive control of networked multi-agent systems , 2013, 2013 American Control Conference.

[3]  Ronald C. Arkin,et al.  An Behavior-based Robotics , 1998 .

[4]  Zhong-Ping Jiang,et al.  A nonlinear small-gain approach to distributed formation control of nonholonomic mobile robots , 2013, 2013 American Control Conference.

[5]  Petter Ögren,et al.  Cooperative control of mobile sensor networks:Adaptive gradient climbing in a distributed environment , 2004, IEEE Transactions on Automatic Control.

[6]  Manfred Morari,et al.  Distributed synthesis and control of constrained linear systems , 2012, 2012 American Control Conference (ACC).

[7]  Randal W. Beard,et al.  A control scheme for improving multi-vehicle formation maneuvers , 2001, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148).

[8]  Morgan Quigley,et al.  ROS: an open-source Robot Operating System , 2009, ICRA 2009.

[9]  Francesco Borrelli,et al.  Decentralized receding horizon control for large scale dynamically decoupled systems , 2009, Autom..

[10]  Richard M. Murray,et al.  Recent Research in Cooperative Control of Multivehicle Systems , 2007 .

[11]  R. Olfati-Saber.  Near-identity diffeomorphisms and exponential /spl epsi/-tracking and /spl epsi/-stabilization of first-order nonholonomic SE(2) vehicles , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).

[12]  Zhaodan Kong,et al.  A Survey of Motion Planning Algorithms from the Perspective of Autonomous UAV Guidance , 2010, J. Intell. Robotic Syst..

[13]  Greg Droge.  Behavior-based model predictive control for networked multi-agent systems , 2014 .

[14]  Petter Ögren,et al.  A convergent dynamic window approach to obstacle avoidance , 2005, IEEE Transactions on Robotics.

[15]  Wolfram Burgard,et al.  The dynamic window approach to collision avoidance , 1997, IEEE Robotics Autom. Mag..

[16]  Magnus Egerstedt,et al.  Proportional integral distributed optimization for dynamic network topologies , 2014, 2014 American Control Conference.

[17]  Liu Hsu,et al.  Formation control of dynamic nonholonomic mobile robots with curvature constraints via potential functions , 2013, 2013 American Control Conference.

[18]  Magnus Egerstedt,et al.  Graph Theoretic Methods in Multiagent Networks , 2010, Princeton Series in Applied Mathematics.

[19]  Alonzo Kelly,et al.  Receding Horizon Model-Predictive Control for Mobile Robot Navigation of Intricate Paths , 2009, FSR.

[20]  Melanie Nicole Zeilinger,et al.  Plug and play distributed model predictive control based on distributed invariance and optimization , 2013, 52nd IEEE Conference on Decision and Control.