Motion Primitives Planning for Center-Articulated Vehicles

Autonomous navigation across unstructured ter- rains, including forests and construction areas, faces unique challenges due to intricate obstacles and the element of the un- known. Lacking pre-existing maps, these scenarios necessitate a motion planning approach that combines agility with effi- ciency. Critically, it must also incorporate the robot’s kinematic constraints to navigate more effectively through complex envi- ronments. 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 vehi- cles. 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 distur- bances, 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.