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<p>Tra jectory planning for tractor-trailer vehicles (TTVs) presents significant challenges due to their complex characteristics, such as underactuated structures and nonholonomic constraints. This paper proposes a novel time-optimal trajectory planning method based on differential flatness specifically for TTVs. First, a new kinodynamic model for the TTV is established, and its differential flatness property is demonstrated. Building on this foundation, an optimization problem is designed to find time-optimal and kinodynamically feasible trajectories. Finally, a modified model predictive control strategy that incorporates a preview yaw rate reference is employed to track the planned trajectories. The effectiveness of the proposed methodology is verified through various simulations conducted in Matlab/Simulink and Trucksim, and compared across three rigorous criteria: computational efficiency, verification of time optimality, and trajectory quality. Simulation results indicate that our method achieves a 56% reduction in computation time compared to traditional planning methods, guarantees time optimality, and enables better driving curvatures for TTVs than sigmoid-based and hyperbolic-based trajectories.</p>

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A novel time-optimal flatness-based trajectory planning for tractor-trailer vehicles

  • 期刊论文

  • 发表日期：

    2026年5月16日

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  • Huu Viet Nguyen,
  • Dao Nguyen Giap Tran,
  • Thi Mai Hoang,
  • Ngoc Duy Le,
  • Tung Lam Nguyen

摘要

Tra jectory planning for tractor-trailer vehicles (TTVs) presents significant challenges due to their complex characteristics, such as underactuated structures and nonholonomic constraints. This paper proposes a novel time-optimal trajectory planning method based on differential flatness specifically for TTVs. First, a new kinodynamic model for the TTV is established, and its differential flatness property is demonstrated. Building on this foundation, an optimization problem is designed to find time-optimal and kinodynamically feasible trajectories. Finally, a modified model predictive control strategy that incorporates a preview yaw rate reference is employed to track the planned trajectories. The effectiveness of the proposed methodology is verified through various simulations conducted in Matlab/Simulink and Trucksim, and compared across three rigorous criteria: computational efficiency, verification of time optimality, and trajectory quality. Simulation results indicate that our method achieves a 56% reduction in computation time compared to traditional planning methods, guarantees time optimality, and enables better driving curvatures for TTVs than sigmoid-based and hyperbolic-based trajectories.