**5. Conclusions**

This paper presented a survey of coverage path planning according to the decomposition methods, such as no decomposition, exact, and approximate decomposition methods.

Different shapes of the area of interest, such as concave, rectangular, and polygon, are considered in this survey. We focused on simple path planning patterns, such as boustrophedon and spiral, and more complex approaches such as grid-based methods. We also presented multi-robot and multi-UAV CPP strategies that aim to accelerate the coverage area by focusing on optimal routes.

Some authors in more complex missions and areas use multiple UAVs to overcome their endurance limitations. However, this approach demands computational complexity to solve communication issues and coordinate the UAVs. The coordination of the UAVs requires a ground control station, which presents many communication failures in realworld scenarios.

CPP methods with simple path planning, such as boustrophedon [33] and square [36], are preferred over cellular decomposition methods for regular shapes without complexity. These CPP methods need less computational time, but they have limitations when UAVs use them. Exact cellular decomposition CPP methods are preferable in more complex area shapes, such as a polygon or concave. The boustrophedon cellular decomposition [37] is similar but better than trapezoidal decomposition [39,40] when the shape of the area has many vertices. The boustrophedon overcomes the trapezoidal decomposition by reducing the number of cells, which means shorter path planning. The morse-based decomposition [42] has the advantage over the other decomposition approaches in that it can produce different cell shapes such as circular and can be applied in any dimensional space. The contact sensor-based coverage is preferable in a rectilinear environment and for online coverage of the area because the coverage trajectory is updated as the CPP progresses.

Furthermore, we present UAVs' energy-saving CPP algorithms, which enhance the energy efficiency using optimal coverage methods and approaches, such as the sub-area assignment of the area of interest according to the capability of the UAV in a multi-UAV CPP strategy.

Finally, several kinds of research have been performed for UAV energy-aware methods in the literature. However, a remaining issue for further research is the combination of these techniques with machine learning, deep learning, and IoT sensors to develop a new, dynamic CPP method that will maximize energy-saving compared to the proposed energy-efficient CPP methods.

**Author Contributions:** Conceptualization, G.F. and T.L.; methodology, G.F. and T.L.; validation, T.L., V.A. and P.S.; investigation, G.F. and T.L.; resources, V.A. and P.S.; data curation, G.F.; writing— original draft preparation, G.F.; writing—review and editing, G.F. and T.L.; visualization, G.F.; supervision, T.L., V.A. and P.S.; project administration, V.A. and P.S.; funding acquisition, V.A. and P.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.
