## 3 Review of Path-Planning Techniques for Data Harvesting
\begin{table}
\begin{tabular}{p{42.7 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline
**References** & **Platforms** & **Method Category** & **Method** & **Comments** & **Environment** & **Implementation** \\ \hline
[72] & AUV & AI-based method & Deep reinforcement learning & Integrating both acoustic and optical technologies & Python 3.8 & Simulation \\
[73] & Multiple AUVs & AI-based method & Deep reinforcement learning & Data collection with multiple complex tasks & Python & Simulation \\
[74] & USV & AI-based method & Deep reinforcement learning & Stochastic arrived data and the random emergence and movement of dynamic obstacles & Python & Simulation \\
[75] & USV & AI-based method & Deep reinforcement learning & Arrived data and dynamic obstacles are stochastic & Python & Simulation \\
[76] & AUV & AI-based method & Q-learning & Improve energy efficiency of sensor nodes & Not mentioning & Simulation \\
[36] & Multiple AUVs & AI-based method & Q-learning & Balance the energy consumption of a network and extend the network’s lifetime & MATLAB & Simulation \\
[12] & Multiple USVs & AI-based method & Reinforcement learning & In fully unknown environments, and integrating communication channels & MATLAB & Simulation \\
[77] & AUV & AI-based method & Reinforcement learning and small drifts & Dynamic underwater conditions comprising obstacles & MATLAB & Simulation \\
[24] & AUV & Coverage planning & Lawnower path & AUV is responsible for nominating the Cluster Head & Not mentioning & Simulation \\
[78] & AUV & Coverage planning & On-the-way fair data & Improved the overall data freshness at the cost of data & Not mentioning & Simulation \\  & & & freshness & collection delay & & \\
[35] & AUV & Coverage planning & Lawnower pattern & Support the long-range AUVs and the uniform energy path-planting & MATLAB & Simulation \\
[79] & AUV & Coverage planning & Lawnower pattern & Maximum the duration of the cooperative works & MATLAB & Simulation \\  & & & path-planting & Maximize the information value while minimizing & & Experiment \\
[47] & AUV & Coverage planning & Transplant & Maximum the completeness of the data collected & Not mentioning & Simulation \\
[80] & AUV & Exact approach & Iterative local search & Optimative programming & Optimative safety, energy consumption, and cooperation & Not mentioning & Simulation \\
[81] & Multiple AUVs & Exact approach & Concode & Robust to channel variations and interference & C++ & Simulation \\
[89] & AUV & Exact approach & Concode & Concode & Commuulating with multiple nodes at once & C++ & Simulation \\
[83] & AUV & Heuristic method & Greedy algorithm & Consider the energy consumption of an AUV and value of information (Vq) & Not mentioning & Simulation \\
[84] & Multiple AUVs & Heuristic method & A* Energy & Reduce the energy consumption of multi-AUV data & MATLAB & Simulation \\
[85] & AUV & Heuristic method & A* Energy & Acquisition systems & Not mentioning & Simulation \\
[86] & AUV & Heuristic method & A* Minimum expected risk & With predictors of ocean currents & Not mentioning & Simulation \\
[87] & AUV & Heuristic method & Minimum expected risk & Consider uncertainty in the ocean current predictions & \(\backslash\) & Experiment \\
[88] & AUV & Heuristic method & A* & With predictions of ocean currents & Not mentioning & Simulation \\ \hline \end{tabular}
\end{table} 
Table 2: Summary of existing research.