Adaptive Path Following for a Differential Drive Robot with EKF-based Localization

Abstract

For a mobile robot, uncertainties in its operating environment and system setup are common challenges. The robot may need to follow a new path in order to avoid collision with a previously unknown object in the environment. Additionally, the robot does not usually have precise knowledge of its own pose. To compensate for these uncertainties, we develop an adaptive path following scheme for a differential drive robot with localization capabilities. We employ a least-squares adaptive law to estimate an unknown circular path. With measurements to landmarks in the environment, we use an extended Kalman filter to estimate the robot's pose. Furthermore, a cascaded control scheme is used to track the estimated path. Simulation and experimental results are provided to verify the developed adaptive path following scheme and demonstrate its capabilities.