Stochastic Nonlinear Model Predictive Mobile Robot Motion Control

• Published in: 2018 Latin American Robotic Symposium, 2018 Brazilian Symposium on Robotics (SBR) and 2018 Workshop on Robotics in Education (WRE) pp. 19 - 25
• Main Authors: Nascimento, Tiago P., Basso, Gabriel F., Dorea, Carlos E.T., Goncalves, Luiz M.G.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-11-2018
• Subjects: Mathematical model; Mobile Robot; Mobile robots; Perception Driven Motion Control; Predictive models; Stochastic NMPC; Stochastic processes; Target tracking; Uncertainty
• DOI: 10.1109/LARS/SBR/WRE.2018.00014

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.