**1. Introduction**

In recent years, due to rapid technological development, UAVs and sensors they can carry have been developed to the extent that they can cover a wide range of applications [1] that cannot be satisfied by other types of robots [2]. Some of the applications are precision agriculture [3,4], search and rescue [5], firefighting [6], law enforcement [7], powerline inspection [8], oil and gas [9], disaster managemen<sup>t</sup> [10], and cell network expansion [11]. However, a fundamental problem is the optimal use of autonomous aircraft in terms of time and space [2].

Recently, CPP algorithms have been developed, considering the parameters required for more efficient data retrieval from remote sensing sensors [12,13]. In addition, algorithms have been developed that use multi-UAV to cover the area, thus reducing the coverage time of the area of interest [14,15]. The way to cover an area with autonomous robots differs depending on the algorithm [2,16,17]. In the literature, CPP algorithms use different methods (e.g., grids, graphs, and neural networks) with calculations performed online or offline for known or unknown areas [18].

The CPP algorithms can be classified into two main categories: offline and online [19]. Offline algorithms need to know the environment and the information included, such as obstacles and the geometry of the area of interest. Of course, in a real-life environment, many dynamic parameters cannot be known in advance. Offline algorithms have prior knowledge of the coverage area environment [20]. They also provide more efficient and convenient route plans and use less central processing unit (CPU) power than online algorithms [21].

The online algorithms are based on real-time environment data retrieved from onboard sensors to cover the area of interest. Online algorithms do not fully understand the coverage area environment, and the coverage path is executed in real-time by the UAV after processing the data using the sensors it carries. The benefits of online algorithms are

**Citation:** Fevgas, G.; Lagkas, T.; Argyriou, V.; Sarigiannidis, P. Coverage Path Planning Methods Focusing on Energy Efficient and Cooperative Strategies for Unmanned Aerial Vehicles. *Sensors* **2022**, *22*, 1235. https://doi.org/ 10.3390/s22031235

Academic Editors: Zihuai Lin and Wei Xiang

Received: 31 December 2021 Accepted: 2 February 2022 Published: 6 February 2022

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the design of the in-flight route to complete the mission regardless of unforeseen situations and the unnecessary prior detailed knowledge of the coverage area [20,22].

Furthermore, there are two categories of problems in area coverage: single coverage and repeat coverage. The goal of single coverage is to cover the entire area of interest and, at the same time, minimize the time and distance traveled by the coverage route [23]. On the other hand, repetitive coverage aims to repeatedly cover all points of interest in the area, maximize the frequency of visits to points of interest, and minimize time and total coverage [24].

This paper aims to present the CPP methods and approaches used by UAVs, focusing on energy-saving CPP methods, such as using the direction of the wind in the cover area [25]. The CPP problem is the optimal motion of the robot in a specific area that includes obstacles to cover this area with minimum overlapping and the shortest path. In the case of a UAV in a three-dimensional area, the shortest path is related to the sensor's footprint. Of course, as the altitude of flight is higher, the footprint is more extensive, which means the shortest path. On the other hand, the higher the flight altitude of the UAV, the bigger the ground sample distance (GSD) and the lower the image quality. GSD is the distance between pixel centers measured in the ground. However, there are a lot of other limitations, such as no-flight zones, that must be computed during path planning to avoid obstacles [26].

Many surveys present studies related to UAV trajectory planning in an environment with obstacles [27], UAV autonomous guidance [28], and in specific applications, such as remote sensing with UAVs in precision agriculture [29]. A survey on CPP methods for mobile robots was presented by Choset, who classified the approaches in two classes [19]. The robots follow simple rules, but the success of area coverage is not guaranteed to be classified as a heuristic approach. On the other hand, the complete methods using cellular decomposition guarantee coverage. Moreover, the author mentions the flight time, which can be minimized by using multiple robots and reducing the number of turns.

The most recent surveys regarding the CPP methods for robotics or UAVs are presented in Table 1. Cabreira et al. [30] present a survey of the decomposition methods, UAV and Multi-UAV CPP methods, and energy-saving algorithms. Galceran and Carreras [20] present a survey of the decomposition methods and ground multi-robot strategies. Additionally, Almandhoun et al. [31] present Multi-UAV CPP methods in their survey. Chen et al. [32] present a survey of CPP methods using UAV or Multi-UAV. The existing surveys of CPP methods considering unmanned ground vehicles (UGV) and the surveys of CPP methods using UAVs extend the UGV's CPP methods. However, many factors, such as the sensors' weight, the flight endurance, direction, and intensity of the wind, must be considered when using UAVs on CPP methods developed for UGVs.

**Table 1.** Related surveys.


Table 1 compares the present work to already existing surveys of CPP methods for robotics or UAVs. The present paper is focused not only on surveying the CPP methods for UAVs, but also on: (a) examining all the decomposition methods, (b) reviewing the multi-robot strategies, (c) the multi-UAV's and standalone UAV's CPP methods, (d) UAVs' energy-saving CPP algorithms, and (e) the comparison of the energy-saving CPP methods. Our approach proves to be the most complete regarding the variables considered for the survey comparison.

The key contributions of this work can be summarized as follows:


This paper is organized considering the CPP methods, multi-UAV strategies, and energy-saving algorithms. Section 2 focuses on a detailed analysis of the systematic review research methodology. Section 3 reviews all decomposition algorithms, multi-robot CPP strategies, multi-UAV CPP methods, and presents UAVs' energy-saving CPP algorithms and a comparative table. Finally, directions for future research on energy-saving CPP algorithms are given in Section 4.

Our review considers the research gap concerning the differences between UGV CPP methods and the UAV CPP methods. Furthermore, our review presents the limitations of the UAVs considering environmental conditions, such as the intensity and direction of the wind. A detailed discussion about the main aspects of multi-robot and multi-UAV CPP methods is also provided. Our review focuses on approaches related to UAV energysaving algorithms and a discussion of the combination of these algorithms considered for future research.

This paper aims to inform the reader of the coverage path planning approaches in different shapes of the area of interest, including rectangular, concave, and polygons, according to the decomposition method employed. Furthermore, we explore the limitations of the CPP methods between UGVs and UAVs, the latest multi-robot and multi-UAV CPP strategies, and the energy-efficient algorithms for UAVs. Finally, our review considers the performance metrics and the limitations of these methods.
