# Coverage Path Planning with Semantic Segmentation for UAV in PV Plants
## 1 Introduction
Whereas the orthophotography technique is a vertical photograph that shows images of objects in true planimetric position [29]. Thus, these two techniques were integrated with previous works to achieve an advanced tool that allows monitoring and taking actions in the operation and maintenance (O and M) of PV plants [7]. In short, this tool has been used to detect defects and dust or dirt in PV modules [28]. Apart from this, some approaches have been developed for the detection of defects in PV modules using artificial vision techniques, machine learning, deep learning, and the integration therewith of the previous approaches [7; 24; 30; 31; 32; 33]. Additionally, these approaches to planning the UAV's flight path were configured from a ground control station (GCS) program. Irrespective of these approaches, an important aspect is that the UAV should automatically follow the path to cover important points in PV plants. Many research efforts have been made to calculate the paths of and solve the waypoint planning problem for UAV inspection of solar PV plant applications [34; 35; 36; 37], but none of them propose the coverage path planning (CPP) as a method to complete this task. The CPP, given a region of interest (RoI) in a 2D environment, consists of calculating the path that passes through each one of the points that make up the desired environment and must be found considering the limitations of movement [38; 39]. CPP is classified as a classical NP-hard problem in the field of computational complexity. These problems were initially analyzed for indoor environments with mobile robots. However, with the development of GPS, CPPs began to be used for missions with UAVs. Due to the environment in which the task is performed and the obstacles present, precise localization in the environment is an arduous task, which makes the CPP a difficult problem [40]. Additionally, it is classified as a motion planning subtopic in robotics, and has two approaches, heuristic and complete. In heuristic approaches, the robots follow a set of rules defining their behavior, but do not present a guarantee for successful full coverage. These guarantee using the cellular decomposition of the area, which involves space discretization into cells to simplify the coverage in each sub-region, unlike complete methods, which cannot afford such processing. Another important issue mentioned by the authors of reference [39] is the flight time to fully cover the area, which can be reduced using multiple robots and by reducing the number of turning maneuvers. Finally, the available RoI information is important; several approaches accept previous knowledge of the robot's respecting the search RoI (offline), while sensor-based approaches collect such information in real-time along the coverage (online) [41]. In the literature, CPP approaches are needed in several application areas, such as floor cleaning [42], agriculture [43; 44], wildfire tracking [45], bush trimming [46], power line inspection [47], photogrammetry [39], visual inspection [48], and many more. Additionally, many surveys regarding CPP present several approaches and techniques for performing missions with, mostly, land vehicles [41; 49; 50]. The research interest in aerial robots (indoor and outdoor) has surely motivated the research of CPP [51]. This can be implemented in many UAVs platforms, such as fixed-wing, rotary-wing, and hybrid UAV (VTOL) [39]. Rotary-wing UAVs are inexpensive and have good maneuverability, and their small payload capacity limits the weight of on-board sensors and flight time. Hence, they are more suitable for CPP missions on a small scale. Additionally, the increasing usage of UAVs in applications with complicated missions has led to CPP methods being a very active research area for single and multiple UAVs, especially recently [52; 53; 54; 39], As evidenced in a previous work [43], the classic taxonomy of coverage paths in UAVs are classified into no decomposition, exact cellular decomposition, and approximate cellular decomposition.