A Greedy-Strategy-Based Iterative Optimization Method for Articulated Vehicle Global Trajectory Optimization in Complex Environments.

This article addresses the problem of global trajectory optimization for articulated vehicles operating in complex environments. It formulates the trajectory planning task as an optimal control problem (OCP) and proposes a three-stage greedy-strategy-based planner: (1) the IAA* algorithm identifies a guiding trajectory representing the homotopy class; (2) collision-free tunnels are constructed along this trajectory to simplify collision-avoidance constraints; and (3) a greedy-strategy-based iterative optimization (GSIO) framework progressively reintroduces constraints to escape local optima and converge rapidly to a global optimum. The planner is applicable to any type of articulated vehicle—including standard, nonstandard, and general N-trailer systems—and can optimize any polynomially describable criterion. Simulation and virtual experiments demonstrate that, compared to selected existing algorithms, the proposed method achieves approximately a 40% improvement in optimization performance while maintaining moderate computational time.