Scalable Parametric-Identification Procedure for Kinematics of Automated N-Trailer Vehicles

Abstract

Kinematic description of a nonholonomic articulated N-Trailer vehicle includes two kinds of parameters: trailer lengths and hitching offsets. In the case of picking up of various trailers by an automated tractor in logistic hubs or transshipment terminals, the kinematic parameters of trailers can be uncertain or even unknown to a control system of the automated tractor. Since an accurate kinematic model is usually required to keep effective functionality of automated vehicles, it seems justified to provide a model-learning capability to the automated or intelligent tractors of N-Trailer vehicles. In this paper, we propose a scalable (with respect to any finite number of trailers) parametric identification procedure applicable to any type of N-Trailer kinematics with non-steerable trailers' wheels. The key idea results from a reformulation of joint-angles kinematics in an iterative form of linear regression models with only two parameters. The proposed estimation algorithm assumes availability of measurements of articulation angles and characteristic velocities of the tractor. Numerical results obtained for the 5-Trailer nonholonomic kinematics and for a high-fidelity TruckSim vehicle model equipped with three trailers illustrate effectiveness of the proposed data-based modeling approach and large-sample statistical properties of the applied estimation procedure.