Motion Primitives Planning For Center-Articulated Vehicles

TL;DR

This work introduces a novel planning method for center-articulated vehicles (CAV), leveraging motion primitives within a receding horizon planning framework using onboard sensing, and develops a pose-stabilizing controller, tailored to the kinematic specifications of center-articulated vehicles.

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.

Figures (9)

• Figure 1: Experiment of a center-articulated vehicle navigating through a construction site. A, B, and C represent three intermediate moments. The goal is set in the orange dot. The green curve shows the vehicle's path avoiding obstacles marked by red boxes. The bottom images illustrate how obstacles influence the planning choices.
• Figure 2: Egocentric polar coordinate system for a CAV during a steady turn
• Figure 3: Example of the motion primitives for vehicles with steering angles equal -30 degrees, 0 degrees, and 30 degrees. Each color denotes a control group of trajectories that coincide during the initial period and split twice as time progresses.
• Figure 4: An illustration of the two-step collision detection: (a) shows an example of marking the corresponding collision grid for the Trajectory $1$. The collision body for the CAV is designed as 2 circles around the front and rear parts and an extra circle for the arm. (b) shows an online collision detection where Trajectory $3$ is marked as occluded.
• Figure 5: (a) shows the "Unreachable zone" for the CAVs. (b) depicts two potential solutions with bi-directional trajectories.