Accurate navigation in outdoor environments requires integrating multiple sensor sources for reliable localization and trajectory tracking. This study proposes Pure Pursuit with Dynamic Steering Control (PP-DSC), which adaptively adjusts both lookahead distance and velocity based on steering angle. The algorithm was deployed on a four-wheeled steering-type autonomous mobile robot (AMR) using Robot Operating System 2 (ROS 2) Jazzy, with real-time sensor fusion from GNSS-RTK, IMU, and wheel encoders. Experiments were conducted on straight, circular, and figure-eight trajectories at 1.0–5.0 m/s in an open area (64 × 20 m). PP-DSC achieved mean lateral deviations of 0.05, 0.07, and 0.08 m respectively, representing 68–82% improvement over standard PP (means 0.19, 0.40, and 0.27 m). To evaluate cross-domain applicability, the algorithm was extended with a Fire and Explosion Index (F&EI)-based safety factor (Safety-integrated PP-DSC) and tested via simulation in an empty fruit bunch (EFB) biodiesel plant (92 × 65 m). Standard PP outperformed Safety-integrated PP-DSC by 15.6% in this industrial setting due to tight turning radii (5–9 m), though Safety-integrated PP-DSC retained advantages in moderate-curvature sections with 11–17% improvement. The F&EI-based safety integration added less than 1% tracking overhead while providing automatic velocity reduction in hazard zones for Process Safety Management (PSM) compliance. The findings confirm that PP-DSC significantly improves trajectory tracking in open-field environments, while industrial deployment requires geometry-specific algorithm selection.
Promkaew et al. (Fri,) studied this question.