## 4 Discussion  
Based on the preceding observations, in this section, we will explore the challenges and opportunities associated with the path-planning problem for maritime data collection assisted by AMVs. The following points are highlighted:  
1. **Vessel dynamics and unknown ocean currents**: When planning paths for AMVs in maritime data collection, it is essential to consider the vessel dynamics and the influence of unpredictable ocean currents. On the one hand, our previous works [137] have shown that the trajectory design must ensure that the AMVs move in a feasible manner, as infeasible paths would require inaccessible forces/torques, such as propulsive force and steering torque, which are limited. For instance, a sharp turning is infeasible for a USV, as steering maneuvers can lead to significant sideslip, deviating from the original path. Furthermore, navigating the vehicle along a predetermined path encounters complexity arising from the ever-changing nature of ocean currents and their inherent unpredictability. The vehicle's trajectory is susceptible to divergence caused by motion-induced disturbances, while underwater environments further compound matters with sensor-induced inaccuracies in state estimation. Successfully addressing this challenge would result in more precise and effective data collection even in the face of varying environmental conditions. 2. **Path planning under more complex communication channels**: Prior research has engaged in simplifications when constructing models for underwater communication, and in certain cases, some studies have even omitted these communication models [90]. However, communication within the actual marine environment presents greater intricacies [138; 139; 140], including factors such as Doppler frequency, multipath effects, latency, and data transmission rates, all of which are influenced by the environment. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **Methods** & & **Highlights** & **Drawbacks** \\ \hline Meta-heuristic methods & GA, PSO, ACO, SA, etc. &  
Developing sophisticated path-planning algorithms that optimize communication paths while factoring in variables like signal strength and interference patterns is crucial. Potential future avenues involve enhancing the control model, particularly in scenarios considering the instability of underwater communication and significant delays inherent in acoustic-based communication. 3. **Considering onboard sensor failure and uncertainty in state estimation**: Previous research commonly operated under the presumption of having complete knowledge of the environment or flawless sensing capabilities. However, these assumptions are often impractical in the vast majority of scenarios, particularly in underwater settings [129; 141]. Even when advanced techniques like terrain-relative navigation (TRN) or simultaneous localization and mapping (SLAM) are employed to enhance localization, achieving perfect knowledge or sensing remains a challenging proposition. However, due to the interference posed by unknown ocean currents, the inaccuracy in onboard sensor data and state observations can lead to errors in path planning. Additionally, satellite-based position accuracy might not suffice, particularly in unforeseen circumstances demanding high precision. Some propose the adoption of re-planning methods, e.g., [142], while others suggest the integration of UAVs for aiding in localization, e.g., [6; 143]. Addressing these challenges holds significant implications for real-world maritime data collection missions. 4. **Navigating AMVs with uncertain sensor node positions**: Previous studies have assumed accurate knowledge of sensor node locations. However, in dynamic underwater environments, anchored nodes can shift from their initial positions due to water currents [144; 145]. This movement within a defined range can impede data collection when an AMV follows a predetermined strategy to reach a specific location.