Unmanned Aerial Vehicles (UAVs) have been implemented for environmental monitoring by using their capabilities of mobile sensing, autonomous navigation, and remote operation. However, in real-world applications, the limitations of on-board resources (e.g., power supply) of UAVs will constrain the coverage of the monitored area and the number of the acquired samples, which will hinder the performance of field estimation and mapping. Therefore, the issue of constrained resources calls for an efficient sampling planner to schedule UAV-based sensing tasks in environmental monitoring. This paper presents a mission planner of coverage sampling and path planning for a UAV-enabled mobile sensor to effectively explore and map an unknown environment that is modeled as a random field. The proposed planner can generate a coverage path with an optimal coverage density for exploratory sampling, and the associated energy cost is subjected to a power supply constraint. The performance of the developed framework is evaluated and compared with the existing state-of-the-art algorithms, using a real-world dataset that is collected from an environmental monitoring program as well as physical field experiments. The experimental results illustrate the reliability and accuracy of the presented coverage sampling planner in a prior survey for environmental exploration and field mapping.
@cite_19: At present, autonomous underwater vehicle (AUV) mine countermeasure (MCM) surveys are normally preplanned by operators using ladder or zig-zag paths. Such surveys are conducted with side-looking sonar sensors whose performance is dependent on environmental, target, sensor, and AUV platform parameters. It is difficult to obtain precise knowledge of all of these parameters to be able to design optimal mission plans offline. This research represents the first known sensor driven online approach to seabed coverage for MCM. A method is presented where paths are planned using a multiobjective optimization. Information theory is combined with a new concept coined branch entropy based on a hexagonal cell decomposition. The result is a planning algorithm that not only produces shorter paths than conventional means, but is also capable of accounting for environmental factors detected in situ. Hardware-in-the-loop simulations and in water trials conducted on the IVER2 AUV show the effectiveness of the proposed method.
@cite_18: Fundamentals of Spatial Statistics.- Fundamentals of Experimental Design.- Exploratory Designs.- Designs for Spatial Trend Estimation.- Design and Dependence.- Multipurpose Designs.
@cite_7: 
For an unknown environment, the underlying environmental model is learned by utilizing observations that are taken across the study area. Coverage sampling that is done to relax unrealistic assumptions is called an exploratory design, which provides sufficient flexibility to explore an unknown field @cite_18 . Such designs have been adopted in spatial mapping without rigid prior assumptions, for initial collection of prior knowledge, exploratory survey for long-term design improvement, or combination with optimum designs for improved estimation performance. In most existing monitoring programs, the exploratory sampling is required to learn the underlying field. Regular grid-based sampling approaches accomplish such objectives as exploratory designs @cite_19 @cite_7 . These approaches distribute sampling sites by decomposing the field into a grid of cells (e.g., squares, triangles, hexagons) where the samples are taken in the grid tessellation. In the absence of prior knowledge, a regular grid ensures overall coverage of the surveillance area. In particular, it provides a simple frame to distribute spatially balanced data samples across the study area for prior survey.