Hybrid path planning framework to integrate improved A*-DWA algorithms for enhancing path safety and efficiency

Abstract

As the deployment of Unmanned Surface Vehicles (USVs) expands across various sectors, the importance of safe and efficient path planning systems is increasingly underscored, particularly in fields such as maritime safety and national security where high levels of navigational efficiency and safety are demanded. While previous path planning research often focused on either safety or efficiency, this study proposes an improved path planning framework that considers both aspects simultaneously, overcoming the limitations of traditional Global Path Planning (GPP) and Local Path Planning (LPP) to enhance route safety, efficiency capabilities. The proposed framework comprises GPP with Safety Areas (GPP-SA) and LPP with Goals & Obstacles (LPP-GO). The proposed structure ensures that the USV can navigate safely and efficiently in a variety of environments and reach its destination easily. The framework has been evaluated through various experiments, including scenario-based validation and statistical verification, demonstrating its superiority by integrating and improving upon existing methods. The findings of this research explore the feasibility of applying these advancements in real maritime environments, making a significant contribution to the realization of safe and efficient path planning in unmanned maritime and ship navigation systems.