Multi-agent deployment in the plane using stochastic extremum seeking

We consider the problem of deployment of a group of N autonomous fully actuated vehicles (agents) in a non-cooperative manner in a planar signal field using the recently introduced method of stochastic extremum seeking. The spatial distribution of the signal is unknown to the vehicles but known to be convex. The vehicles are not able to sense their own positions but are capable of sensing the distance between their neighbors and themselves. Each vehicle employs a stochastic extremum seeking control law whose goal is to minimize the value of the measured signal, namely to be as close as possible to the bottom of the signal field, as well as to simultaneously minimizing a function of the distances between neighboring agents. Such a seemingly conflicting and mutually competitive nature of the agents' control laws produces a Nash equilibrium that depends on the agents' control parameters and the unknown signal distribution. We prove local exponential convergence, both almost surely and in probability, to a small neighborhood near the Nash equilibrium. The theoretical results are illustrated with simulations.

[1]  Warren E. Dixon,et al.  An extremum seeking method for non-isometric neuromuscular electrical stimulation , 2007, 2007 IEEE International Conference on Systems, Man and Cybernetics.

[2]  Gene H. Golub,et al.  Matrix computations (3rd ed.) , 1996 .

[3]  Milos S. Stankovic,et al.  Distributed seeking of Nash equilibria in mobile sensor networks , 2010, 49th IEEE Conference on Decision and Control (CDC).

[4]  Jorge Cortés,et al.  Coverage control by multi-robot networks with limited-range anisotropic sensory , 2009, Int. J. Control.

[5]  M.L. Walker,et al.  Extremum-Seeking Finite-Time Optimal Control of Plasma Current Profile at the DIII-D Tokamak , 2007, 2007 American Control Conference.

[6]  Sonia Martínez,et al.  Coverage control for mobile sensing networks , 2002, IEEE Transactions on Robotics and Automation.

[7]  H. Berg,et al.  Chemotaxis in Escherichia coli analysed by Three-dimensional Tracking , 1972, Nature.

[8]  Karl Johan Åström,et al.  Optimotaxis: A Stochastic Multi-agent Optimization Procedure with Point Measurements , 2008, HSCC.

[9]  Miroslav Krstic,et al.  Source seeking with non-holonomic unicycle without position measurement and with tuning of forward velocity , 2007, Syst. Control. Lett..

[10]  Gene H. Golub,et al.  Matrix computations , 1983 .

[11]  Miroslav Krstic,et al.  GPS denied source seeking for underactuated autonomous vehicles in 3D , 2008, 2008 IEEE International Conference on Robotics and Automation.

[12]  Sonia Martínez,et al.  Distributed coverage games for mobile visual sensors (I): Reaching the set of Nash equilibria , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[13]  Miroslav Krstic,et al.  Extremum seeking for limit cycle minimization , 2000, IEEE Trans. Autom. Control..

[14]  Miroslav Krstic,et al.  Real-Time Optimization by Extremum-Seeking Control: Ariyur/Extremum Seeking , 2004 .

[15]  A.R. Teel,et al.  Robust source-seeking hybrid controllers for nonholonomic vehicles , 2008, 2008 American Control Conference.

[16]  Milos S. Stankovic,et al.  Stochastic extremum seeking with applications to mobile sensor networks , 2009, 2009 American Control Conference.

[17]  Miroslav Krstic,et al.  Extremum seeking for moderately unstable systems and for autonomous vehicle target tracking without position measurements , 2006 .

[18]  J. R. Baxter,et al.  Energy and the law of the interated logarithm. , 1976 .

[19]  N Ghods,et al.  Multi-agent deployment around a source in one dimension by extremum seeking , 2010, Proceedings of the 2010 American Control Conference.

[20]  Miroslav Krstic,et al.  Stochastic source seeking for nonholonomic unicycle , 2010, Autom..

[21]  Miroslav Krstic,et al.  Stochastic averaging in continuous time and its applications to extremum seeking , 2010, Proceedings of the 2010 American Control Conference.

[22]  Kartik B. Ariyur,et al.  Real-Time Optimization by Extremum-Seeking Control , 2003 .

[23]  D. Popovic,et al.  Extremum seeking methods for optimization of variable cam timing engine operation , 2006, IEEE Transactions on Control Systems Technology.

[24]  Miroslav Krstic,et al.  Source seeking with a nonholonomic unicycle without position measurements and with tuning of angular velocity part I: Stability analysis , 2007, 2007 46th IEEE Conference on Decision and Control.

[25]  Howard C. Berg,et al.  E. coli in Motion , 2003 .

[26]  Miroslav Krstic,et al.  Experimental application of extremum seeking on an axial-flow compressor , 2000, IEEE Trans. Control. Syst. Technol..