  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.
