## 3 Review of Path-Planning Techniques for Data Harvesting
### Heuristic and Direct Search  
Heuristic and direct search approaches, such as A*, dynamic programming (DP), and greedy search, have drawn substantial attention in classical robot path planning. The appeal of their simplicity and efficiency has captivated numerous researchers, leading to their widespread exploration.  
Due to its ease of implementation, A*-related algorithms have attracted interest among researchers. In the context of enhancing oceanic observation using autonomous underwater vehicles (AUVs), work in [85; 86] introduced an A* planner that utilizes a locally generated controllability map derived from ocean current predictions. This approach computes a path between predetermined waypoints that maximizes the probability of successful execution. Expanding upon the foundation laid by [85], subsequent research in [49] refined the concept by incorporating considerations of uncertainty associated with ocean currents. They devised a minimum expected risk planner designed to prevent collision risks through sea trials. Furthermore, to enhance the energy efficiency of data acquisition systems employing multiple AUVs, a study documented in [84] introduced the AE (A*-Energy) algorithm. This algorithm was developed for the purpose of path planning for multiple AUVs. Its implementation led to a reduction in total distance traveled and turning angles executed by the AUV system, consequently resulting in improved energy efficiency.  
Several traditional optimization algorithms, such as dynamic programming, greedy algorithms, and local search techniques, have been applied to data collection. In addressing challenges associated with AUV yaw change and sensor node movement arising from time-varying ocean currents, a local path-planning algorithm based on greedy algorithmic approach (depicted in Figure 12) was formulated by researchers of [83]. This algorithm effectively resolves issues related to sensor nodes' movement while simultaneously optimizing the VoI-energy ratio. Drawing on the interval programming concepts in [132], a multiple-objective path planning strategy for data acquisition tasks was introduced by the authors of [81]. This approach optimizes three distinct objective functions: AUV safety, energy consumption, and vehicle coordination. It is important to note that, however, the communication channel model was omitted from consideration in this study. Moreover, some researchers have directed their efforts towards addressing data collection challenges through the utilization of commercial software. To maximize gathered information while minimizing travel time, a novel form of the TSP was proposed and termed the Communication-Constrained Data Collection Problem (CC-DCP) by authors in [82]. This complex problem was solved using the Concorde software ([[https://www.math.uwaterloo.ca/tsp/concorde/index.html](https://www.math.uwaterloo.ca/tsp/concorde/index.html)]([https://www.math.uwaterloo.ca/tsp/concorde/index.html](https://www.math.uwaterloo.ca/tsp/concorde/index.html))). Building upon the work in [82], researchers of [48] extended this line of research by incorporating data from AUV deployments and incorporating more advanced acoustic communication models. In addressing scenarios where event-generated data dynamically occur, a scheme known as the on-demand path-planning scheme (OD-PPS) was developed by researchers of [91]. This scheme employs local search techniques to maximize the completeness of collected data. It is worth noting that in this approach, the modeling does not account for external oceanic environmental factors.