## 3 Review of Path-Planning Techniques for Data Harvesting
### Others  
Apart from the algorithms discussed in the preceding sections, the research community has introduced an array of alternative methods. In addition to some techniques adapted from other domains, such as artificial potential field (APF) and sequential quadratic programming, the majority of these methodologies have been devised by these scholars. In this section, we will undertake a comprehensive review of these methods.  
To facilitate obstacle avoidance for AUVs in data acquisition, a path-planning technique based on fluid mechanics is proposed by the work in [105], where the ocean current characteristics were simplified as a stream function to design the artificial potential field. Furthermore, the APF was extended to the application of AUV flocks by the authors of [51]. They defined the concept of the AUV flock and the united control model for the AUV flock based on the artificial potential field theory. The path-planning method performed more efficiently than the normal-distributed path-planning approach.  
In order to enhance the performance of AUVs during resurfacing in data collection, a series of studies were conducted based on the Euler circuit [106; 133; 134]. An Eulerian circuit is a path in a graph that traverses each edge exactly once and returns to the starting vertex, forming a closed loop. In [134], the authors first established the trajectory planning for  
Figure 12: The greedy search procedures depicted in [83].  
AUVs as an Euler cycle problem. Then in [106; 133], the authors minimized the average data and event reporting delay. Experiments in both the synthetic and real traces are conducted.  
To perform data collection in high-valued regions, the work in [46] proposed a centroid-tracking waypoint-selection algorithm. It determines paths for AUVs to track evolving features of interest in the ocean by considering the output of predictive ocean models. The algorithm uses model predictions to assist in solving the motion planning problem of steering an AUV to high-valued locations. Furthermore, authors of [50] extended the application to tracking a freshwater plume. Also, field trials have been conducted using two gliders at the coast of Newport Beach, Los Angeles, CA.  
Considering the state uncertainty due to the ocean currents, the work in [104] presented an energy-aware feedback planning method for an LRAUV. It uses ocean dynamics from a predictive ocean model to understand the water flow pattern and introduces a goal-constrained belief space to make the feedback plan synthesis computationally tractable. To maximize the information collected, the work in [135] proposed a model predictive control method that incorporates the posterior Cramer-Rao Lower Bound. Similarly, the work in [136] introduced a generalized optimal control for the information collection in mine countermeasures. To minimize the collection delay and facilitate information sharing between AMVs, a state prediction-based data collection (SPDC) algorithm is devised by authors of [103], where path planning is achieved by a heuristic strategy based on the updated access area. To solve the trajectory planning problem for the UAV-USV system (see Figure 13) in data collection, the work in [102] devised a Fermat-point theory in maritime Internet of Things systems. Delaunay triangles based on the deployment of unmanned surface vehicles were constructed and the Fermat point of each Delaunay triangle is calculated as a hovering point of UAVs to receive data from USVs.  
In general, we have compared existing algorithms mentioned in this section, and their advantages and disadvantages are summarized in Table 3.  
Figure 13: UAV-USV system.