Motion Primitives Planning For Center-Articulated Vehicles
在无结构地形中进行自主导航，包括森林和建筑区，由于错综复杂的障碍和未知的元素，面临着独特的挑战。由于缺乏预先存在的地图，这些场景迫使采用运动规划方法，将灵活性和效率相结合。关键的是，它还必须包括机器人的运动约束，以便更有效地通过复杂的环境进行导航。
这项工作介绍了一种新型的规划方法——为中心刚性车辆（CAV）设计的运动规划方法，利用车载感知的运动原型。该方法从离线创建运动原型开始，通过向前仿真反映了中心刚性车辆的显著运动模型。这些原型通过基于启发式的评分函数进行评估，促进选择最合适的实时导航路径。为了增强规划过程，我们开发了一个针对中心刚性车辆运动规格的运动稳定控制器。在实验中，我们的方法在成功率加路径长度（SPL）性能上比现有策略提高了67％。此外，通过与树割草机车辆（SAHA）进行实际世界实验验证了其有效性。
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 augment this planning process, 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.
https://arxiv.org/abs/2405.17127
https://arxiv.org/pdf/2405.17127.pdf