Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Connecting the Physical World with Pervasive Networks
IEEE Pervasive Computing
Mobile sensor networks for learning anisotropic Gaussian processes
ACC'09 Proceedings of the 2009 conference on American Control Conference
Guaranteed global performance through local coordinations
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Autonomous manipulation combining task space control with recursive field estimation
AIS'11 Proceedings of the Second international conference on Autonomous and intelligent systems
Brief paper: Decentralized adaptive awareness coverage control for multi-agent networks
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Distributed parametric and nonparametric regression with on-line performance bounds computation
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Hi-index | 22.16 |
This paper presents an algorithm and analysis of distributed learning and cooperative control for a multi-agent system so that a global goal of the overall system can be achieved by locally acting agents. We consider a resource-constrained multi-agent system, in which each agent has limited capabilities in terms of sensing, computation, and communication. The proposed algorithm is executed by each agent independently to estimate an unknown field of interest from noisy measurements and to coordinate multiple agents in a distributed manner to discover peaks of the unknown field. Each mobile agent maintains its own local estimate of the field and updates the estimate using collective measurements from itself and nearby agents. Each agent then moves towards peaks of the field using the gradient of its estimated field while avoiding collision and maintaining communication connectivity. The proposed algorithm is based on a recursive spatial estimation of an unknown field. We show that the closed-loop dynamics of the proposed multi-agent system can be transformed into a form of a stochastic approximation algorithm and prove its convergence using Ljung's ordinary differential equation (ODE) approach. We also present extensive simulation results supporting our theoretical results.