Asymptotic optimal-based trajectory planning approach for tractor-trailer vehicles via safe travel corridor and risk field constraints

Trajectory planning for tractor-trailer vehicles (TTVs) in unstructured environments poses significant challenges due to their complex constraints. This paper proposes a real-time trajectory planning strategy that efficiently generates feasible optimal trajectories using a reconfigurable TTV kinematic model. Specifically, to resolve the structural variability of TTVs, hitch vectors are formulated as modular connection units and integrated into a non-standard N-trailer kinematic model, establishing a reconfigurable framework with configurable trailer-hitch compatibility. Then, the trajectory planning task is formulated as an optimal control problem through systematic definition of constraint sets and objective functions. A significant advancement is the dual-mechanism obstacle avoidance framework: Dynamic collision risks are quantified via a risk-field model, while static safety constraints are enforced through an adaptive safe travel corridor, collectively ensuring comprehensive motion safety across operational phases. To efficiently solve the resulting nonlinear optimization problem, a homotopy-based strategy with constraint softening is developed, implementing a two-stage framework that first generates initial trajectories through single-vehicle state extension, then refines them via alternating strong/soft constraint optimization. Extensive simulations in four representative complex scenarios demonstrate the method’s superiority over three benchmark algorithms, particularly in computational efficiency and success rate, validating its practical applicability in real-world environments. Specifically, the proposed method is feasible in all testing cases, and saves up to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mrow> <mml:mn>21</mml:mn> <mml:mo>.</mml:mo> <mml:mn>33</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mrow> <mml:mn>78</mml:mn> <mml:mo>.</mml:mo> <mml:mn>78</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> solving time compared with other two methods in the narrow passage scenario, respectively, bringing the great improvement of planning efficiency.