Localization and Motion Planning of Industrial Tractor–Trailers Vehicles
Hongchao Zhao, Wen Chen, Shunbo Zhou, Zheng Fan, Yunhui Liu
Abstract
This brief presents real-time configuration estimation and motion planning for the industrial tractor–trailers vehicle composed of a full-scale car-like tractor and multiple full trailers. For the real-life vehicle, determining the configuration is challenging. With only on-tractor sensors, we solve this problem by fusing information from system dynamics propagation, geometrical constraints among the articulated units, and matching with the prebuilt environment map. The solution is efficiently achieved by formulating and solving a maximum a posterior (MAP) estimation problem in the pose-graph optimization framework. With the complicated tractor–trailers’ structure, small mismatch between actual and planned trajectories is crucial to inherit obstacle-free guarantee from planning to execution. We consider the dynamics and focus on facilitating the reduction of the mismatch by proposing a controller-based smoothing method to perform online motion planning. The given waypoint path is smoothed by forward propagation using a deliberately designed controller to generate the trajectory. The efficiently computed trajectories are also obstacle-free and dynamically feasible. The controller is also applied in execution to precisely reproduce the planned system evolvement. We demonstrate the performance with the real-life industrial tractor–trailers’ vehicle.