Robot Differential Drive Navigation Through Probabilistic Roadmap and Pure Pursuit

摘要

This research addresses autonomous navigation for differential drive robots by integrating probabilistic roadmap (PRM) and pure pursuit algorithms. The proposed method innovatively tackles path planning challenges in complex environments by developing a novel framework that combines forward kinematics, binary occupancy mapping, and adaptive path generation. The approach demonstrates superior navigation performance through efficient path planning in constrained spaces, validated by a case study in Riyadh, Saudi Arabia. By using PRM’s probabilistic path generation and pure pursuit’s real-time control, the method outperforms traditional navigation techniques in generating feasible trajectories through intricate environments. Key contributions include a comprehensive framework for robot navigation that offers enhanced adaptability and robust path planning. While being limited to static environments, the research provides a foundational approach for developing more resilient autonomous robotic systems, setting the stage for future navigation algorithm advancements.