Abstract
In mobile robotics, constraints may represent mobility issues, vision uncertainties or localization uncertainties. In model predictive control (MPC) theory, constraint satisfaction is typically guaranteed through the use of accurate prediction models or robust control. However, although MPC offers a certain degree of robustness to system uncertainties, its deterministic formulation typically renders it inherently inadequate for systematically dealing with uncertainties. Towards this direction, this paper presents a Stochastic Nonlinear Model Predictive Control (SNMPC) algorithm for active target tracking. Our goal is to use a stochastic nonlinear model predictive controller to penalize the undesired behavior, allowing the robot to converge to the optimal pose in order to observe the target optimally. The paper presents simulations in which the stochastic nonlinear controller provides satisfactory target tracking control.
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