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This paper extends Model Predictive Contouring Control (MPCC) for trajectory planning of articulated commercial vehicles like tractor-semitrailers, addressing challenges such as vehicle size, non-holonomic constraints, and jackknifing during reversing. The key innovation is a scenario-dependent weighting of vehicle anchor points (tractor vs. semitrailer) combined with explicit road boundary constraints for all axles. Simulations and initial prototype tests demonstrate successful navigation in forward and reverse directions within a logistic scenario, highlighting the practical applicability and parameter tuning insights for controlling vehicle behavior.
Finally, a trajectory planner handles the reversing jackknife of tractor-trailers by prioritizing different parts of the vehicle based on the driving scenario.
This paper presents a trajectory planning method for articulated commercial vehicles, specifically tractorsemitrailers, based on Model Predictive Contouring Control (MPCC). Although MPCC has proven effective for passenger cars, it is generally ill-suited for tractor-semitrailers. These vehicles are significantly larger, the semitrailer follows a different path than the tractor, and reversing maneuvers are unstable and prone to jackknifing. Furthermore, practical driving scenarios often require scenario-dependent prioritization of different vehicle 'anchor points', e.g., prioritizing the semitrailer position during docking or the tractor position when parking to charge. Therefore, we extend MPCC to enable scenario-dependent weighting of these anchor points and incorporate explicit roadboundary constraints for the front and rear tractor axles and the semitrailer axle, thereby ensuring that all considered wheels remain within the drivable area. The simulation results demonstrate the successful navigation of a representative logistic scenario in both forward and reverse direction. Furthermore, the influence of the optimization parameters on the trajectories is analyzed, providing insights into controlling the vehicle behavior. Finally, first tests using a full-scale prototype vehicle show the practical applicability of the approach.