Smooth-optimal Adaptive Trajectory Tracking Using an Uncalibrated Fish-eye Camera

This paper presents a two-stage smooth-optimal trajectory tracking strategy. Different from existing methods, the optimal trajectory tracked point can be directly determined in an uncalibrated fish-eye image. In the first stage, an adaptive trajectory tracking controller is employed to drive the tracking error and the estimated error to an arbitrarily small neighborhood of zero. Afterwards, an online smooth-optimal trajectory tracking planner is proposed, which determines the tracked point that can be used to realize smooth motion control of the mobile robot. The tracked point in the uncalibrated image can be determined by minimizing a utility function that consists of both the velocity change and the sum of cross-track errors. The performance of our planner is compared with other tracked point determining methods in experiments by tracking a circular trajectory and an irregular trajectory. Experimental results show that our method has a good performance in both tracking accuracy and motion smoothness.