# A Data-Driven Intermittent Online Coverage Path Planning Method for AUV-Based Bathymetric Mapping
## 2 Related Work  
Path planning is a fundamental problem in robotics. An efficient path planning system would greatly improve mission efficiency and results. New algorithms in sampling-based methods [11; 12], networked systems [13; 14], and energy-optimized solution [15] is advancing the field rapidly. Bathymetric mapping studied in this paper could be referred to as a CPP problem which aims to determine a path that passes over all points of an area or volume of interest while avoiding obstacles. CPP problems are widely studied in a variety of applications, such as cleaning robots, painting robots, unmanned aerial vehicles (UAVs) [16], demining robots, lawnmowers [17], mine countermeasure (MCM) surveys with AUVs [18], etc.. The solutions to the CPP problems can be divided into two main groups, area-based and cell-based. In the former group, the workspace can be divided into several areas [19] or be considered as an entire area. In each area, the robot navigates in preset trajectories, mostly lawnmower pattern [16], or spiral pattern [20]. These methods require prior knowledge of the target area and they are able to accomplish covering task in a relatively short period [18]. Area-based approaches have been adopted in various applications. In [19], an area decomposition algorithm is proposed to divide obstacle-exist workspace into obstacle-free areas and obstacle areas. A lawnmower path is then planned to cover individual areas. In [8], a depth-constrained autonomous bathymetric mapping strategy is introduced. The AUV can autonomously divide the target area online according to the depth data, so the lawnmower coverage will operate effectively in each area and transit efficiently between areas. Another adaptive method is presented in [21]. In order to reduce energy consumption of UAVs in sensing tasks, the inter-distance between the parallel transects of lawnmower path are properly adjusted and the returning path is also utilized to cover extra areas. In [22], the target region is segmented by depth features, and lawnmower path are planned in each subregion with different inter-distances. In this way, the coverage overlapping is minimized. A novel informative path planning method handling with spline trajectories is proposed in [23]. The path is generated by a online data-driven optimization algorithm. Its global goal is to collect information effectively rather than complete coverage. In the bathymetric mapping task, the swath width changes with terrain, so it is dilemmatic to determine the inter-distance between parallel transects for lawnmower paths and spiral paths. Small inter-distance will result in a long overall traveling distance and a wide inter-distance will lead to low coverage rate. This is a drawback shared by most area-based methods. In the cell-based approaches, the workspace is divided into small cells. The cell shape can be square, circle, hexagon, et al., depending on different mission requirements [16]. Cell-based methods are usually adaptive, using algorithms such as Genetic Algorithm, Wavefront Algorithm, and objective function based algorithms, which plans the path on-the-fly [16]. In [18], a data-driven and multiobjective CPP method is proposed for mine countermeasure survey of AUVs. The proposed algorithm is composed of information theory and branch entropy and its goal is to determine the desired heading in a hexagonally decomposed workspace. The final path is irregular and shows a better performance than lawnmower path and random track in non-convex workspace. In [24], a topologically organized bioinspired neurodynamics model based on the grid map is constructed to represent the dynamic environment. A gradient-based algorithm is built to search targets in 3-D underwater environments with obstacles. [PERSON] et al. [25] proposed an anytime planning technique for efficient data gathering using point-sampling based on non-uniform data-driven coverage. Their online selective sampling algorithm plans a path for an ASV to perform high density measurements in hotspot regions. [PERSON] et al.