Challenges and AI-driven solutions in maritime search and rescue planning: A comprehensive literature review

Abstract

Maritime Search and Rescue (MSAR) operations face significant challenges due to high uncertainty, dynamic conditions, and resource constraints. Additionally, rigid organizational structures and hierarchical human-centered communication frameworks, fail to adapt to the challenging conditions of maritime environments. This paper provides a comprehensive review of the integration of Artificial Intelligence (AI) into MSAR operations, highlighting how AI can transform these systems through enhanced decision-making, real-time adaptability, decentralized autonomy, and resource optimization. Through analysis and synthesis, we identified and categorized key challenges in traditional SAR frameworks, such as inherent environmental and structural challenges. We discussed AI-driven solutions that offer efficient, autonomous, resilient, and decentralized coordination. Our thematic and statistical analysis of existing literature reveals significant research gaps, particularly regarding the holistic integration of AI across all SAR stages toward a decentralized fully autonomous paradigm shift. The paper also considers the technological challenges for the integration and adaptation of AI in SAR. By envisioning fully autonomous, AI-driven MSAR operations, this study sets the stage for future research and practical innovations, aiming to improve effectiveness and efficiency in maritime rescue efforts.