###### Abstract  
Seamless integration of both terrestrial and non-terrestrial networks is crucial to providing full-dimensional wireless and ubiquitous coverage, particularly catering to those engaged in marine activities. Compared to terrestrial networks, wireless communications in the marine domain are still not satisfactory for ubiquitous connectivity. Featuring agile maneuverability and strong adaptive capability, autonomous marine vehicles (AMVs) play a pivotal role in enhancing communication coverage by relaying or collecting data. However, path planning for maritime data harvesting is one of the most critical issues to enhance transmission efficiency while ensuring safe sailing for AMVs; yet it has rarely been discussed under this context. This paper provides a comprehensive and holistic overview of path-planning techniques custom-tailored for the purpose of maritime data collection. Specifically, we commence with a general portrayal of fundamental models, including system architectures, problem formulations, objective functions, and associated constraints. Subsequently, we summarize the various algorithms, methodologies, platforms, tools, coding environments, and their practical implementations for addressing these models. Furthermore, we delve into the burgeoning applications of path planning in the realm of maritime data harvesting and illuminate potential avenues for upcoming research endeavors. We believe that future research may focus on developing techniques to adapt more intricate and uncertain scenarios, such as sensor failures, inaccurate state estimations, complete modeling of communication channels, ocean dynamics, and application of heterogeneous systems. p +
Footnote †: journal: Marine Science and Engineering  
They are deemed as promising tools to enhance the efficiency of marine activities for humans, giving rise to the concept of Smart Oceans [5]. Within oceanic networks, the Underwater Wireless Sensor Network (UWSN) stands out as an intelligent network with self-learning and computational abilities. It is part of the networks through which \"things\" in the earth's water bodies (oceans/sea, rivers, lakes, and streams) are digitally connected. It comprises a large number of sensor nodes, including anchored nodes, mobile nodes, and surface sinks, each designed for environmental sensing, data collection, and transmission. However, when considering the unique characteristics of the oceanic environment, Smart Oceans encounter several significant challenges. The underwater sensor nodes situated on the seabed are typically reliant on batteries, and this reliance has a substantial impact on their ability to sustain data collection operations. The energy consumption associated with transmitting data underwater through acoustic waves exceeds the computational overheads, and recharging these batteries is impractical due to the constraints imposed by seawater environments. Hence, there is a compelling need to devise methods to extend the operational life of UWSNs. Furthermore, as evidenced by research outlined in [6], underwater acoustic transmission is limited by prolonged propagation delays and a high probability of packet loss, resulting in diminished communication efficiency. Consequently, there remains unresolved challenges of achieving dependable maritime communication as we venture towards 6G technology in ocean realm. The deployment of connected autonomous marine vehicles (AMVs) is one of the primary drivers for the Smart Ocean due to their strong flexibility and mobility [7; 8], including autonomous underwater vehicles (AUVs) and unmanned surface vehicles (USVs) [9]. On the one hand, AUVs offer a significant advantage in underwater data collection for UWSNs due to their ability to serve as underwater mobile agents. This mobility allows for dynamic data collection in response to changing environmental conditions, enabling more comprehensive and timely data acquisition.