## 4 Discussion
Therefore, the AMV may face stochastic position data from the sensor nodes, which causes a delay in reaching the target. Consequently, designing the AMV's path to accommodate sensor node position uncertainty emerges as a pivotal challenge. 5. **Path planning for long-duration data harvesting with energy supply**: Due to limited energy resources, AMVs are faced with limitations when undertaking tasks over extended distances. Despite the existence of energy-efficient, path-planning methods proposed by many researchers, the energy capacity of AMVs remains insufficient for tasks that cover vast regions. Consequently, introducing energy supply stations during AMV missions has emerged as a viable solution to this challenge. Some scholars have initiated preliminary exploration in this realm. The work in [146] has developed a dynamic routing strategy employing USVs as energy supply stations for UAVs. Similarly, authors of [147] employ USVs to provide energy to AUVs for search tasks. However, the aforementioned work oversimplifies the problem. In reality, the recharging mechanisms for UAVs and AUVs are intricate, involving factors such as the docking of AUVs [148] during motion and the landing of UAVs, see Figure 14. In summary, considering the issue of energy supply station placement in path planning is crucial for enhancing AMV operational efficiency. Nevertheless, the current state of research in this area remains nascent. 6. **Path planning for heterogenous systems**: Leveraging AUV-USV-UAV heterogeneous unmanned systems can significantly enhance data collection efficiency. Specifically, the incorporation of UAVs offers advantages, such as cost-effectiveness, high flexibility, wide coverage, and line-of-sight (LoS) communication links. AUVs excel in navigating intricate underwater terrains, performing bathymetry mapping, conducting environmental monitoring, and executing sonar-based data collection. However, both UAVs and AUVs are constrained by limited battery capacities. USVs are adept at surface operations and serve as mobile workstations, supplying power and communication networks and serving as landing/docking platforms for UAVs and AUVs. This collaborative multi-robot system holds immense potential for mitigating risks and enhancing efficiency in demanding and perilous maritime tasks. Presently, some scholars have embarked on explorations in heterogeneous unmanned systems, yet their focus has been on foundational theoretical research, including collaborative control [149; 150], landing control [151; 152; 153], and task allocation [154; 155]. However, research into the application of heterogeneous unmanned systems in marine engineering remains largely unexplored. * **Field experiments**: In Table 2, it is evident that, in preceding research articles, nearly all investigations relied upon numerical simulation methodologies for validation. It is imperative to acknowledge the substantial distinction between experimental and numerical simulation approaches, recognizing that the substantiation of method feasibility and practical applicability necessitates empirical scrutiny. Nonetheless, the execution of authentic maritime or subaquatic experiments is encumbered by formidable challenges. On the one hand, the procurement of mechanical apparatus and sensors is typically accompanied by a high financial outlay. Moreover, maritime experiments entail a significant investment of both manpower and time, with a customary sea expedition enduring from one week to a month, thereby incurring considerable temporal expenses for a singular experimental iteration. On the other hand, maritime experiments are susceptible to the influences of intricate sea conditions, thereby exerting pronounced impacts on the trajectories of AUVs or USVs, consequently yielding diminished data precision and suboptimal experimental outcomes. Consequently, the formulation of efficacious maritime experimental frameworks for the validation of path-planning algorithms stands out as a formidable challenge for the research community.