Research on mobile robot path planning and tracking control

Autonomous navigation of a robot is a promising research domain due to its extensive applications in which planning and motion control are the most important and interesting parts. The proposed techniques are classified into two main categories: the first group focuses on the improvement of model free adaptive control (MFAC) to meet the extreme performances of the control system, and the second concentrates on the classic artificial potential field (APF) algorithm to deal with the limitations like falling into local minima and a non-reachable goal problem. This paper proposes a novel exponential feedforward-feedback control strategy based on iterative learning control (ILC) MFAC to the reference trajectory tracking, and then introduces a virtual target with exponential coordinated form to realise local risk collision avoidance for path planning. Compared to some traditional models, our proposed methods have a faster trajectory convergence rate, lower avoidable error, and higher safe performance. The simulation results verify that our work would bring meaningful insights to future intelligent navigation research.