## 1 Introduction
It finds the path by visiting each location exactly once, and provides the shortest path for mobile awareness. [PERSON] [18] proposed a genetic algorithm with path templates to obtain paths. Of course, there are other methods. [PERSON] [19] obtained the coverage path according to the region of interest marked by the wavefront and proposed a task execution time optimization criterion based on the length of the path. There are many CPP methods based on cell decomposition, such as boustrophedon decomposition [20], Morse decomposition [21], lawnower path [22] and its variants [23]. Boustrophedon cell decomposition solves the problem of covered areas with known obstacles by decomposing the target area under the obstacle location and then finding the least costly route through all areas [24]. [PERSON] [25] proposed an improved cell decomposition algorithm to improve the complete coverage efficiency of autonomous environmental monitoring systems. [PERSON] [26] optimized Boustrophedon coverage by using several scanning methods and combining them according to the required transition motion between obstacles and cells, and some extensions of the algorithm also took sensor feedback into account [27]. The reference [28] details how to plan a full coverage path, but most planners do not consider the energy constraints of UAVs, nor do they address or emphasize the limited total travel length. Only recently have researchers begun to consider energy constraints in full-coverage path planning [29; 30; 31]. [PERSON] [32] determined the total length of the voyage according to the energy limit in his work, and constructed a scalar map of monitoring parameters to assess the performance of coverage sampling. [PERSON] [31] treated camera characteristics as image resolution and field of view, avoiding moving over already visited areas, and proposed an energy-aware spiral coverage path planning algorithm designed for optics. [PERSON] [33] proposed a dynamic path model consisting of a chained mass-spring-damper system combined with a Voronoi diagram to generate a potential field in which pre-connected path waypoints are distributed to distribute the path waypoints into a near-optimal configuration. However, it may not be optimal to address the energy shortage in planning only by limiting the total travel length. In [PERSON]'s work [29], two main factors affecting the UAV's energy consumption were identified through experimental measurements: distance and cornering. They found that the average power consumption of the UAV is almost constant during flight, but when entering a turn, it typically decelerates sharply, even to zero to hover steering, leading to longer flight times and increased energy consumption. In this paper, a UAV-based mobile sensing sampling path planning method for spatial sampling of the atmospheric environment is proposed, which integrates spatial coverage sampling design and related path planning. Based on the sampling design of the hexagonal prism, the sampling space is divided into subregions of the same size, and the sampling point is the center of the hexagonal prism. A Genetic Algorithm is used to solve the sampling path. Combined with the energy budget of UAVs, the optimum coverage density can be obtained by adjusting the side length of the hexagonal prism. by improving the genetic algorithm and combining the B-spline curve to optimize the flight path, the number of turns of the path and the speed loss of the UAV during turning are effectively reduced, and the coverage performance and data sampling density of the atmospheric environment detection missions are improved. The rest of the paper is organized as follows. Section 2 details the coverage sampling design and path planning methods. Section 3 presents the simulation and field experiments, and discusses the experimental results. The final section concludes the paper.