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Distributed model predictive coverage control for decoupled mobile robots

Published online by Cambridge University Press:  24 November 2015

F. Mohseni ,
A. Doustmohammadi  and
M. B. Menhaj
F. Mohseni*
Affiliation:
Amirkabir University of Technology, Tehran Polytechnic, Tehran 15875-4413, Iran
A. Doustmohammadi
Affiliation:
Amirkabir University of Technology, Tehran Polytechnic, Tehran 15875-4413, Iran
M. B. Menhaj
Affiliation:
Amirkabir University of Technology, Tehran Polytechnic, Tehran 15875-4413, Iran
*
*Corresponding author. E-mail: [email protected], [email protected], [email protected]
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Summary

A distributed coverage control scheme based on the state space model predictive control, which is known as receding horizon control (RHC) for decoupled systems, is presented. An optimal control problem is formulated for a set of decoupled robotic systems where a cost function couples the dynamical behavior of the robots. The coupling is described through a connected graph using a Voronoi diagram, where each robot is a node and the cost and constraints of the optimization problem associated with each robot are a function of its state and of the states of its neighbors. The complexity of the problem is addressed by breaking a centralized receding horizon controller into distinct RHC controllers of smaller sizes. Each RHC controller is associated with a different node and it computes the local control inputs based only on the position of the robot and that of its neighbors. The stability of the distributed scheme is analyzed and its properties compared with the linear quadratic regulator (LQR) design which has been proposed in the literature. Moreover, the proposed coverage algorithm is also applied to deploy a group of mobile robots in a desired formation pattern. The simulation results are used to illustrate the good performance of the proposed coverage control scheme.

Keywords

Coverage controlDistributed controlOptimizationReceding horizon controlStability
Type
Articles
Information
Robotica , Volume 35 , Issue 4 , April 2017 , pp. 922 - 941
Copyright
Copyright © Cambridge University Press 2015 

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