**4. Discussion**

The CPP problem using UAVs in areas of interest with different shapes and environmental conditions has been studied by several authors. Standard-shaped areas of interest, such as polygons and rectangles, do not require decomposition and can be covered by boustrophedon and spiral patterns. Generally, no decomposition methods, such as backand-forth, require low computational cost to find the path trajectory. The main issue of these patterns is not considering that the UAVs are directly aerodynamically affected by the environmental conditions, which means the actual trajectory of the flight in most cases is not close to that planned.

In more complex and irregular areas of interest, a cellular decomposition method may be applied to split the area of interest into subregions. The subregions can be covered by different CPP methods to obtain the optimum path to minimize the total path and the total coverage flight time. Multi-UAV cooperative strategies are also being studied using the decomposition method according to the capabilities of the UAVs.

When the vehicle used for the proposed CPP algorithms is a UAV, there is the limitation of the motion constraints, such as the feasible trajectory of fixed-wing UAVs. However, the CPP methods plan the coverage path according to a performance metric. These approaches do not consider the UAVs' environmental factors and aerodynamic and flight limitations.

A further study is necessary for the area of CPP methods using UAVs. The coverage algorithms should consider the constraints of the aerial vehicles, such as the actual path trajectory rather than that planned. Moreover, the environmental factors in the area of interest that affect the path, the time, and the actual flight path should also be considered. According to all these mutable factors, an offline CPP method will not achieve optimal path planning, but an online CPP method considering all these factors and re-planning the trajectory will achieve the optimal coverage path within minimum time.

In recent years, many new CPP algorithms have been developed for energy-efficiency and awareness. The approach using a glider UAV for soaring limits early knowledge of the field's wind conditions. Otherwise, the method is less effective in a situation where the knowledge of wind conditions is limited [25]. In approaches where engine-driven UAVs are used, there are some methods or combinations for energy saving. A method for power saving in non-complex areas is reducing the number of turns in the UAV's trajectory to minimize the total path and the acceleration's power consumption after every turn, and eventually the total coverage time of the area of interest [20,76]. In approaches for energy saving, considering the direction and intensity of the wind was validated as the UAV's path should be vertical in the wind direction, and the turning maneuvers against the wind direction [77,78]. This approach can be combined with the previous method for greater energy saving.

Two more approaches that can be used in combination with the previous methods for further energy saving include minimizing the UAV's turns according to the GSD [18]. A spiral CPP algorithm uses wider angle turns to maintain a constant speed [67] or an algorithm for a conventional trajectory that modifies the turns for smoother motion [70] to minimize the deceleration and acceleration before and after the turning point. Another energy-aware algorithm computes the take-off weight, flight speed, and air friction to generate an energy-optimal path [81].

In convex areas, there are approaches using multiple UAVs to divide into sub-areas and assign each sub-area according to the UAV's capability, such as motion, sensors onboard, and total endurance flight time [83,84].

The proposed energy-efficient UAV CPP methods aim to minimize the total flight time and the coverage path length to save energy. However, the performance metrics are based on the path trajectory without considering other constraints, such as UAV aerodynamics and environmental conditions. For example, in a convex area, a CPP method with a performance metric for minimum path trajectory may produce very sharp turns. Meanwhile, it is infeasible for a fixed-wing UAV to obtain the planned trajectory due to its aerodynamics constraints. Another variable affecting the UAV's actual trajectory is the wind's direction and intensity. The UAV will consume more energy than a more extensive path length with smoother turns considering all these limitations.

A further study is necessary to combine all of the above constraints to develop new energy-efficient UAV CPP methods that consider variables, such as the vehicle kinematics and environmental conditions offline and online. A research direction to develop UAV CPP methods to maximize energy-saving should combine machine learning or deep learning and IoT onboard sensors in order to develop a CPP approach that will plan offline and adapt online the coverage path trajectory according to the main performance metrics, such as UAV kinematics constraints, and the information retrieved from onboard sensors such as wind conditions.
