Performance Analysis of Dijkstra and A-star Algorithm in Maritime Navigation Pathfinding

Abstract

This study explores various algorithms and methodologies for path planning and decision-making in diverse environments, including maritime distribution and logistics. The research highlights the importance of COLREG rules in designing collision avoidance algorithms for Maritime Autonomous Surface Ships (MASS), emphasizing the need for algorithms that adapt to specific maritime parameters. A Multiple-Criteria Decision-Making (MCDM) approach combined with Dijkstra’s algorithm is presented to optimize route distribution in logistics, taking into account parameters such as cost, distance, congestion, and risk. Experimental path planning methods using A-star and Dijkstra algorithms are discussed for navigating slag disposal sites that emit natural radiation, demonstrating the adaptability of these algorithms in hazardous environments. The study also investigates dynamic path planning algorithms, such as DAA-Star, which incorporate time and risk cost factors to enhance the safety and efficiency of navigation. Integrating various algorithms and considering specific environmental parameters can significantly improve path planning and decision-making processes in maritime and logistics contexts.