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arXiv · 2405.17127

Motion Primitives Planning For Center-Articulated Vehicles

Jiangpeng Hu·Fan Yang·Fang Nan·Marco Hutter
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Abstract

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.

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Jiangpeng Hu, Fan Yang, Fang Nan, Marco Hutter. 2024-05-27. Motion Primitives Planning For Center-Articulated Vehicles. https://arxiv.org/abs/2405.17127

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