## 3 Review of Path-Planning Techniques for Data Harvesting
### AI-Based Methods
This methodology is used to select optimal communication strategies, while an innovative angle steering algorithm is also introduced to enhance AUV navigation under diverse communication scenarios, leading to reduced energy consumption. Lately, the self-organizing map has gained significant attention among researchers due to its capacity to reveal the underlying topological arrangement of environments. Given the heterogeneity of task types, the work in [118] introduced an enhanced version of the self-organizing map. It used a fitness function to assign multi-type tasks to specific USVs. Additionally, for specific tasks that require sequential execution, like sea sweeping tasks, the method of tasks treatment list (TTL) is presented. Incorporating energy consumption and path-smoothing techniques, a hierarchical two-layer framework was proposed by the authors of [119]. This framework addressed cooperative path planning for multi-USV systems. In this framework, the self-organizing map served as the first layer to prevent collisions and efficiently access multiple targets in an optimal sequence. Addressing task urgency and uncertain ocean environments, a double-layered bio-inspired self-organizing map (DLBSOM) algorithm was introduced by the study mentioned in [120]. This algorithm facilitates collaborative search within a 3D environment, where multiple AUVs balance factors such as task completion time, distance, energy consumption, system stability, and security to achieve optimal task planning. To enhance cooperative operational capabilities in formation control and cluster search tasks, the authors of [121] proposed the hybrid bio-inspired self-organizing map (HBSOM) algorithm. This algorithm improves the speed, stability, and accuracy of multi-AUVs. Though SOMs are powerful unsupervised learning techniques used for clustering and dimensionality reduction, they also come with certain drawbacks and challenges. First, handling high-dimensional problems with SOMs can be difficult due to the \"curse of dimensionality\". The computational complexity of SOM training can be an issue when dealing with large problem scales. Moreover, convergence of SOM training is not always guaranteed, and the stopping criteria for training can be difficult to determine. Stopping training too early might result in an underdeveloped map, while training for too long could lead to overfitting. While the past decade has witnessed promising outcomes from AI-based approaches, it is imperative to acknowledge that these approaches continue to come with challenges that demand resolution: (1) RL algorithms often require a significant amount of data (samples) to learn effective policies. In oceanic environments, data collection can be time-consuming and costly, making it challenging to gather enough data to train RL agents effectively. (2) Ensuring that policies generalize effectively is quite a challenging work. AMVs need to generalize their learning from one situation to another. However, oceanic environments can vary widely, and what works well in one area might not work in another due to differences in underwater topography, currents, and other factors. (3) Oceanic environments are complex and dynamic, with various factors such as currents, weather conditions, and underwater obstacles affecting the path-planning process. Designing AI-based methods that can handle these complexities and make informed decisions could be challenging as well. (4) Transferring knowledge from simulations to real-world oceanic environments can be challenging due to the domain gap between the two. Ensuring that AMVs can effectively learn from simulations and apply that knowledge in the real world is a significant challenge.