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.

翻译：在包括森林和施工区域在内的非结构化地形中进行自主导航，由于存在复杂的障碍物和未知环境因素，面临着独特的挑战。由于缺乏预先存在的地图，这些场景需要一种兼具敏捷性与高效性的运动规划方法。至关重要的是，该方法还必须结合机器人的运动学约束，以便在复杂环境中更有效地导航。本文针对中心铰接式车辆（CAV）提出了一种新颖的规划方法，该方法利用车载感知，在滚动时域规划框架内运用运动基元。该方法首先通过反映中心铰接式车辆独特运动学模型的前向仿真，离线创建运动基元。这些基元通过基于启发式的评分函数进行评估，从而为实时导航选择最合适的路径。为了应对干扰，我们开发了一种姿态稳定控制器，该控制器专为中心铰接式车辆的运动学特性而设计。在实验中，我们的方法在SPL（由路径长度加权的成功率）性能上比现有策略提高了$67\%$。此外，其有效性通过使用树木采伐车——SAHA进行的真实世界实验得到了验证。