Autonomous Navigation in Dynamic Environments Using Modified Pure Pursuit, Vector Field Histogram, and LiDAR-SLAM

Autonomous navigation in dynamic and unstructured environments presents several challenges, requiring seamless integration of real-time path planning, obstacle avoidance, and precise localization. This paper introduces a novel hybrid framework that synergizes Modified Pure Pursuit (MPP), Vector Field Histogram (VFH), and LiDAR-based Simultaneous Localization and Mapping (SLAM) to address these challenges. The MPP algorithm improves trajectory tracking through adaptive look-ahead distance tuning and curvature smoothing, minimizing oscillations in high-speed navigation. The VFH module processes LiDAR data to dynamically adjust steering directions, enabling real-time obstacle avoidance without global path replanning. Concurrently, the LiDAR-SLAM pipeline constructs high-resolution occupancy grid maps. The system's computational efficiency meets real-time operational requirements, making it suitable for industrial automation, urban logistics, and emergency response robotics. This work advances autonomous navigation by unifying adaptive control, reactive obstacle avoidance, and robust localization into a cohesive architecture, setting a benchmark for deployable solutions in complex, real-world settings.