Vision based Path Planning and Tracking control using Mobile Robot

This paper proposes a novel methodology
for autonomous mobile robot navigation utilizing the
concept of tracking control. Vision-based path planning
and subsequent tracking are performed by utilizing
proposed stable adaptive state feedback fuzzy tracking
controllers designed using the Lyapunov theory and
particle-swarm-optimization (PSO)-based hybrid
approaches. The objective is to design two self-adaptive
fuzzy controllers, for x-direction and y-direction
movements, optimizing both its structures and free
parameters, such that the designed controllers can
guarantee desired stability and, simultaneously, can
provide satisfactory tracking performance for the
vision-based navigation of mobile robot. The design
methodology for the controllers simultaneously utilizes
the global search capability of PSO and Lyapunovtheory-
based local search method, thus providing a high
degree of automation. Two different variants of hybrid
approaches have been employed in this work. The
proposed schemes have been implemented in both
simulation and experimentations with a real robot, and
the results demonstrate the usefulness of the proposed
concept.