Motion Primitives Planning For Center-Articulated Vehicles
<|reference_start|>Motion Primitives Planning For Center-Articulated Vehicles: Autonomous navigation across unstructured terrains, including forests and construction areas, faces unique challenges due to intricate obstacles and the element of the unknown. Lacking pre-existing maps, these scenarios necessitate a motion planning approach that combines agility with efficiency. Critically, it must also incorporate the robot's kinematic constraints to navigate more effectively through complex environments. This work introduces a novel planning method for center-articulated vehicles (CAV), leveraging motion primitives within a receding horizon planning framework using onboard sensing. The approach commences with the offline creation of motion primitives, generated through forward simulations that reflect the distinct kinematic model of center-articulated vehicles. These primitives undergo evaluation through a heuristic-based scoring function, facilitating the selection of the most suitable path for real-time navigation. To account for disturbances, we develop a pose-stabilizing controller, tailored to the kinematic specifications of center-articulated vehicles. During experiments, our method demonstrates a $67\%$ improvement in SPL (Success Rate weighted by Path Length) performance over existing strategies. Furthermore, its efficacy was validated through real-world experiments conducted with a tree harvester vehicle - SAHA.<|reference_end|>
@article{hu2024motion,
    title={Motion Primitives Planning For Center-Articulated Vehicles},
    author={Jiangpeng Hu, Fan Yang, Fang Nan, and Marco Hutter},
    journal={arXiv preprint arXiv:2405.17127},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.17127},
    primaryClass={cs.RO}
}