Decentralized Near-to-Near Approach for Vehicle Platooning based on Memorization and Heuristic Search
Résumé
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.