Consensus-based source-seeking with a circular formation of agents

This paper deals with the source-seeking problem in which a group of autonomous vehicles must locate and follow the source of some signal based on measurements of the signal strength at different positions. As recently suggested, the gradient of the signal strength can be approximated by a circular formation of agents via a simple weighted average of the signal measured by the agents. Using this result, we propose a distributed source-seeking algorithm based on a consensus method which is guaranteed to steer the circular formation towards the source location using the estimated gradient direction. The proposed algorithm is provided with two tunable parameters that allow for a tradeoff between speed of convergence, noise filtering and formation stability. The benefit of using consensus-based algorithms resides in a more realist discrete time control of the agents and in asynchronous communication resilient to delays which is particularly relevant for underwater applications. The analytic results are finally complemented with numerical simulations.

[1]  Milos S. Stankovic,et al.  Extremum seeking under stochastic noise and applications to mobile sensors , 2010, Autom..

[2]  Yi Guo,et al.  Distributed source seeking by cooperative robots: All-to-all and limited communications , 2012, 2012 IEEE International Conference on Robotics and Automation.

[3]  Nathan Michael,et al.  Stochastic source seeking in complex environments , 2012, 2012 IEEE International Conference on Robotics and Automation.

[4]  Vijay Kumar,et al.  Robot and sensor networks for first responders , 2004, IEEE Pervasive Computing.

[5]  Fumin Zhang,et al.  Bio-inspired Source Seeking with no Explicit Gradient Estimation , 2012 .

[6]  Lara Bri Consensus-based Source-seeking with a Circular Formation of Agents , 2013 .

[7]  M. Arcak,et al.  Gradient climbing in formation via extremum seeking and passivity-based coordination rules , 2007, IEEE Conference on Decision and Control.

[8]  Derong Liu,et al.  Networked Control Systems: Theory and Applications , 2008 .

[9]  Robert Nowak,et al.  Distributed optimization in sensor networks , 2004, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.

[10]  S. Djouadi,et al.  Dynamic plume tracking using mobile sensors , 2010, Proceedings of the 2010 American Control Conference.

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

[12]  Damiano Varagnolo,et al.  Multidimensional Newton-Raphson consensus for distributed convex optimization , 2012, 2012 American Control Conference (ACC).

[13]  Jay A. Farrell,et al.  Moth-inspired chemical plume tracing on an autonomous underwater vehicle , 2006, IEEE Transactions on Robotics.

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

[15]  Karsten Schmidt,et al.  Networked control systems in motor vehicles , 2008 .

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

[17]  Miroslav Krstic,et al.  Nonholonomic Source Seeking With Tuning of Angular Velocity , 2009, IEEE Transactions on Automatic Control.

[18]  Carlos Canudas-de-Wit,et al.  Source seeking via collaborative measurements by a circular formation of agents , 2010, Proceedings of the 2010 American Control Conference.

[19]  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..

[20]  Zhengyou Zhang,et al.  Maximum Likelihood Sound Source Localization and Beamforming for Directional Microphone Arrays in Distributed Meetings , 2008, IEEE Transactions on Multimedia.

[21]  Carlos Canudas-de-Wit,et al.  Collaborative estimation of gradient direction by a formation of AUVs under communication constraints , 2011, IEEE Conference on Decision and Control and European Control Conference.

[22]  Lino Marques,et al.  Particle swarm-based olfactory guided search , 2006, Auton. Robots.

[23]  Naomi Ehrich Leonard,et al.  Adaptive Sampling Using Feedback Control of an Autonomous Underwater Glider Fleet , 2003 .

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

[25]  Luca Schenato,et al.  A Survey on Distributed Estimation and Control Applications Using Linear Consensus Algorithms , 2010 .