Decentralized Formation Control and Obstacles Avoidance Based on Potential Field Method

A new decentralized formation control based on electric field artificial potential is proposed in two dimensions. Making reference to the potential in electric field, the control of agent derived from a weighting of the relative importance of different artificial potential while formation keeping, obstacles avoidance and collision free. The research is based on the model of double integral equation. A strategy of the decentralized formation control based on the artificial potential is given and the proof of stability of this new formation control is presented. The simulation results show that agents can avoid the different shape obstacles effectively

[1]  R. Beard,et al.  Formation feedback control for multiple spacecraft via virtual structures , 2004 .

[2]  Shuzhi Sam Ge,et al.  Multi-robot formations: queues and artificial potential trenches , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

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

[4]  Sunil Kumar Agrawal,et al.  Planning and control of UGV formations in a dynamic environment: A practical framework with experiments , 2005, Robotics Auton. Syst..

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

[6]  Daniel E. Koditschek,et al.  Exact robot navigation using artificial potential functions , 1992, IEEE Trans. Robotics Autom..

[7]  David M. Fratantoni,et al.  Multi-AUV Control and Adaptive Sampling in Monterey Bay , 2006, IEEE Journal of Oceanic Engineering.

[8]  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).

[9]  Naomi Ehrich Leonard,et al.  Vehicle networks for gradient descent in a sampled environment , 2002, Proceedings of the 41st IEEE Conference on Decision and Control, 2002..

[10]  O. Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.