## 3 Review of Path-Planning Techniques for Data Harvesting
### Coverage Planning Techniques  
Coverage path planning refers to a strategic approach used in various domains, including robotics and autonomous systems, to efficiently navigate and explore an area while ensuring that every point within the region is visited or observed. The primary goal of coverage path planning is to devise a path or trajectory for a vehicle or agent that maximizes the coverage of a specified area, minimizing redundancy and ensuring systematic exploration. Particularly, coverage path planning is a critical aspect of marine robotics, encompassing a wide range of applications like bathymetry mapping [122; 123], underwater surveys [124; 125], search and rescue missions [126; 127], and mine-countermeasures [128; 129]. By intelligently navigating through designated areas, AMVs can efficiently collect data, perform critical tasks, and contribute to a safer and more informed maritime environment.  
In the field of oceanic data collection, the research community has extensively investigated coverage path planning. To enhance the freshness of data in wireless sensor networks, the work in [78] introduced an innovative approach called the on-the-way fair data freshness (OFDF) path, utilizing lawn shower patterns (refer to Figure 11). In comparison to traditional lawnmowers and shortest path traversal algorithms, this scheme improved overall data freshness. Building upon the foundation of [78], work in [24] extended the model to incorporate factors, such as residual energy and communication protocols. In this context, the AUV not only gathers data but also undertakes the responsibility of designing the Cluster Head (CH) and establishing a wakeup-sleep schedule for the clusters. Further contributing to this, work in [79] also devised a lawn shower pattern to facilitate prolonged cooperation with sensor nodes. Their efforts culminated in an AUV-based data-gathering protocol that optimizes cooperative work duration. Drawing inspiration from the work in [79], a subsequent study [35] enhanced the feasibility of this concept in practical scenarios by introducing the idea of cluster reconstruction by AUVs to ensure uniform energy consumption. Another method presented in [47] introduced algorithms designed to formulate persistent monitoring missions for underwater vehicles. The coverage planning methodology not only maximizes information value along the path but also minimizes deviations from the predetermined route.  
However, it can be observed from the literature review that coverage path planning is not the mainstream approach for oceanic data collection. This is because the distribution pattern of sensor nodes is different from the approximate cellular decomposition pattern described in [24; 78]. In reality, the distribution of sensor nodes is more dispersed and disorderly, rendering it mostly unsuitable for conducting data collection using the back-and-forth path (BFP) method. Additionally, there are some issues with coverage path planning  
Figure 11: The lawn shower path depicted in [24], which resembles the approximate cellular decomposition in coverage planning.  
in data collection tasks. Foremost among these is the challenge that BFP poses to the energy consumption of AMVs. Due to the influence of ocean currents, AMVs consume much more energy during turning maneuvers than during straight-line travel, as demonstrated in [35]. However, the BFP path includes a substantial amount of turning motions [130], which hinders the ability of AMVs to perform long-distance missions. Secondary to the foregoing, it is pertinent to acknowledge the substantial challenges confronting AMVs when executing turns in underwater environments. Given the complexity of the ocean environment, AMVs struggle to execute precise turning maneuvers [131], often causing deviations from the original path and thus affecting the efficiency of data collection.