Tracailer: An Efficient Trajectory Planner for Tractor-Trailer Vehicles in Unstructured Environments

The tractor-trailer vehicle (robot) consists of a drivable tractor and one or more non-drivable trailers connected via hitches. Compared to typical car-like robots, the addition of trailers provides greater transportation capability. However, this also complicates motion planning due to the robot's complex kinematics, high-dimensional state space, and deformable structure. To efficiently plan safe, time-optimal trajectories that adhere to the kinematic constraints of the robot and address the challenges posed by its unique features, this paper introduces a lightweight, compact, and high-order smooth trajectory representation for tractor-trailer robots. Based on it, we design an efficiently solvable spatio-temporal trajectory optimization problem. To deal with deformable structures, which leads to difficulties in collision avoidance, we fully leverage the collision-free regions of the environment, directly applying deformations to trajectories in continuous space. This approach not requires constructing safe regions from the environment using convex approximations through collision-free seed points before each optimization, avoiding the loss of the solution space, thus reducing the dependency of the optimization on initial values. Moreover, a multi-terminal fast path search algorithm is proposed to generate the initial values for optimization. Extensive simulation experiments demonstrate that our approach achieves several-fold improvements in efficiency compared to existing algorithms, while also ensuring lower curvature and trajectory duration. Real-world experiments involving the transportation, loading and unloading of goods in both indoor and outdoor scenarios further validate the effectiveness of our method. The source code is accessible atthis https URL.

@article{xu2025_2502.19832,
  title={ Tracailer: An Efficient Trajectory Planner for Tractor-Trailer Vehicles in Unstructured Environments },
  author={ Long Xu and Kaixin Chai and Boyuan An and Jiaxiang Gan and Qianhao Wang and Yuan Zhou and Xiaoying Li and Junxiao Lin and Zhichao Han and Chao Xu and Yanjun Cao and Fei Gao },
  journal={arXiv preprint arXiv:2502.19832},
  year={ 2025 }
}