Decentralized near-to-near approach for vehicle platooning based on memorization and heuristic search

This paper deals with vehicle platooning, where a convoy aims at following closely and safely its leader´s path without collision nor lateral deviation. In this paper, we propose a platooning algorithm based on a near-to-near decentralized approach. Each vehicle estimates and memorizes on-line the path of its predecessor as a set of points. After choosing a suitable position to aim for, the follower estimates on-line the predecessor´s path curvature around the selected target. Then, based on a heuristic search, it computes an angular velocity using the estimated curvature. The optimization criteria used in this work allows the robot to follow its predecessor´s path without oscillation while reducing the lateral and angular errors.