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ADMM-based Adaptive Sampling Strategy for Nonholonomic Mobile Robotic Sensor Networks by Le, Viet-Anh; Nguyen, Linh; Nghiem, Truong X. is a scholarly article available to read on EtoBox.

What is ADMM-based Adaptive Sampling Strategy for Nonholonomic Mobile Robotic Sensor Networks about?

This paper discusses the adaptive sampling problem in a nonholonomic mobile robotic sensor network for efficiently monitoring a spatial field. It is proposed to employ Gaussian process to model a spatial phenomenon and predict it at unmeasured positions, which enables the sampling optimization problem to be formulated by the use of the log determinant of a predicted covariance matrix at next sampling locations. The control, movement and nonholonomic dynamics constraints of the mobile sensors are also considered in the adaptive sampling optimization problem. In order to tackle the nonlinearity and nonconvexity of the objective function in the optimization problem we first exploit the linearized alternating direction method of multipliers (L-ADMM) method that can effectively simplify the objective function, though it is computationally expensive since a nonconvex problem needs to be solved exactly in each iteration. We then propose a novel approach called the successive convexified ADMM (SC-ADMM) that sequentially convexify the nonlinear dynamic constraints so that the original optimization problem can be split into convex subproblems. It is noted that both the L-ADMM algorithm and o

Author

Le, Viet-Anh; Nguyen, Linh; Nghiem, Truong X.

Published

2021

Language

EN