# A Data-Driven Intermittent Online Coverage Path Planning Method for AUV-Based Bathymetric Mapping
## 5 Discussion  
According to test results, the proposed CPP method has some major advantages over the traditional lawnmower paths and spiral paths, as summarized below:  
1. The characteristics of multibeam sonar are taken into consideration to get high quality bathymetric map. By selecting proper weights, the proposed method is able to obtain a map with higher confidence than lawnmower paths or spiral paths in every stage of a mission. 2. The data-driven approach can autonomously adapt to various terrains, different depth of the vehicle, and different dimensions of target area. 3. The proposed algorithm can guarantee high coverage rate as path planning won't stop until the preset coverage goal is achieved. 4. The proposed method has higher coverage efficiency than lawnmower path during the early stage, and has the potential to achieve higher overall coverage efficiency by choosing proper weights. 5. Low coverage rate map obtained by the proposed method is more suitable for interpolation than that obtained by lawnmower surveys or spiral surveys. 6. The parameters of the proposed method can be adjusted under different mission goals. For example, if the mission time is adequate for a full coverage mission, \(W_{e}\) can be set to 0, so that high coverage efficiency can be achieved. Otherwise, non-zero \(W_{e}\) is a better choice for proper interpolation. Figure 12: Path planning results of proposed method in different terrains. Both the trials applied the setup of run #2. The left terrain is generated by the same function mentioned in the previous subsection and the depth of the vehicle stays unchanged. The right terrain is generated by bathymetric data of Narragansett Bay, and the depth of the vehicle is set at 0 for this trial. In the proposed model, we use the minimum swath width observed to predict the future swath width. This is a conservative choice to minimize the possibility of generating mapping void during the early stage. It can be replaced by the average swath width observed if fast coverage at the early stage is required. In principle, the proposed algorithm also works with convex irregular workspace. We just need to add two steps at the beginning. (1) Find the maximum bounding box. (2) Assign the cells outside the workspace but inside the bounding box to be explored ones. Then everything else works the same. For non-convex workspace, we could adopt area decomposition to divide the workspace into several convex domains. Then our CPP method can be applied in each domain. However, the overall algorithm efficiency may decrease due to the additional cell decomposition process. We plan to have further research in non-convex workspace and compare the algorithm performance with some existing solutions [18]. We set a constant vehicle depth in our approach, which is a practical and commonly-adopted setup in AUV-based bathymetric survey. However, in the real situation, the depth may change due to imperfect controller tuning or due to the user's command. In both cases, the altitude change will result in significant change in swath width, which leads to inaccurate estimate when predicting the potential coverage in the CPP algorithm. In order to handle the above situation, the CPP algorithm could be modified to construct a mathematical equation between depth and swath width. Then use the equation to estimate the swath width for a future transect at a different depth. Here we provide a preliminary evaluation about the energy performance on selected physical systems. In a 500 m \(\times\) 700 m target region, the mission time is in the order of 2.5 to 3 h. In our project, we plan to use a micro-AUV with 244 watt-hour energy (Alkaline battery). Without any payload, the AUV could last 30 h. Therefore, the AUV is estimated to have a power consumption of about 8 watts. We plan to integrate a bathymetric sonar and a backseat driver computer with a power consumption of 15 watts and 10 watts, respectively.