## 3 Review of Path-Planning Techniques for Data Harvesting
### AI-Based Methods  
AI-based methods, such as reinforcement learning (RL) [112; 113; 114] and self-organizing maps (SOMs) [115; 116; 117], have demonstrated remarkable strengths that elevate their effectiveness. On the one hand, RL algorithms can learn optimal paths by iteratively interacting with an environment, receiving feedback in the form of rewards, see Figure 10. This capability is particularly advantageous in complex and dynamic environments, where traditional algorithms might struggle to adapt. RL's ability to handle uncertainty and adapt in real time makes it a strong contender for tasks like robotic navigation, where the optimal path can change due to unforeseen obstacles or changing conditions. On the otherhand, SOMs have proven their worth in path planning through their ability to analyze and simplify complex spatial data. SOMs can reduce high-dimensional input, such as maps or sensor data, into a lower-dimensional representation while preserving the topological relationships of the data. This makes them invaluable for tasks like route optimization and exploration, where understanding the underlying structure of the environment is crucial. In summary, AI-based approaches can provide more efficient, adaptable, and contextually aware navigation solutions across many scenarios and applications. RL's capacity to manage uncertainty and adjust in real time has established its popularity in the realm of ocean data collection path planning. In the work of [77], researchers introduced a reinforcement learning technique for AUVs within a hierarchical underwater wireless sensor network (HUWSN). The proposed algorithm dynamically calculates optimal paths toward targets, employing a local search strategy that leverages image acquisition and segmentation. Additionally, to achieve low latency and minimal power usage, authors in [56] put forth an AUV path-planning approach rooted in Q-learning, leading to enhancements in energy efficiency and network longevity. To further optimize sensor node energy consumption, the work in [76] formulated a dynamic routing algorithm based on Q-learning. This algorithm optimizes node routing selection, wherein a novel method based on potential-game theory and optimal rigid graph concepts is introduced to achieve balance between energy consumption and network resilience. In contrast to conventional RL techniques, deep reinforcement learning (DRL) demonstrates a strong capability to effectively manage intricate, high-dimensional state spaces by utilizing neural networks. In the context of marine mooring systems, researchers in [75] have introduced a DRL approach aimed at minimizing weighted data loss and energy consumption during data collection operations. Building upon the foundation laid by the study in [75], a subsequent contribution by authors of [74] introduced a target-oriented double deep Q-learning network (D2 QN)-based algorithm. This algorithm is specifically tailored for collision avoidance and trajectory planning, particularly for USVs. Notably,  
Figure 10: Reinforcement learning in AMV-assisted path planning. the proposed algorithm equips the USVs to effectively navigate through stochastic incoming data and dynamically shifting obstacles. Addressing the intricacies of multi-modal transmission and trajectory planning in complex underwater settings, researchers of [73] have developed a distributed trajectory planning algorithm for multiple AUVs using deep reinforcement learning. Collaborative decision-making among the AUVs considers various factors, including transmission conditions, ocean currents, and submerged obstacles. The primary objective of this approach is to maximize the collection rate while ensuring energy efficiency. Moreover, in the work [72], a synthesis of acoustic and optical technologies was proposed to optimize AUV motion trajectories via a deep reinforcement learning.