Integrated Autonomous Underwater Vehicle Path Planning and Collision Avoidance Algorithms

Abstract

This paper addresses the growing need for efficient autonomous underwater vehicle (AUV) path planning, used in environmental monitoring, underwater surveillance, and search and rescue operations. The aim was to develop robust path planning strategies that ensure safe and efficient AUV navigation in complex and unpredictable underwater environments. Our approach combines path planning algorithms with the OMNeT++ network simulator. The procedures for the implementation of these algorithms and AUV motion simulation have been outlined. The algorithms were applied not only to single AUV missions but also to scenarios involving multiple AUVs, allowing exploration of cooperative and coordinated mission planning in diverse underwater settings. The results of the study demonstrate the potential of our approach to address real-world challenges encountered by AUVs. AUV behavior was observed in different simulated mission scenarios and environmental conditions. Our findings shed light on the adaptability of AUVs in the face of unexpected obstacles and dynamic ocean currents. In conclusion, this study contributes to the field of AUV applications, offering a path planning and simulation strategy adaptable to diverse AUV mission requirements. By utilizing simulation models, we illustrate AUVs' ability to autonomously adapt their mission plan and avoid obstacles during missions, improving operational efficiency and safety.