# EAOA: Energy-Aware Grid-Based 3D-Obstacle Avoidance in Coverage Path Planning for UAVs
## 1 Introduction  
The usage of an Unmanned Aerial Vehicle (UAV) (also known as a drone) has become very fast-growing recently [1]. It pervades many application domains to achieve different missions such as terrain monitoring and smart agriculture [2; 3], photogrammetry [4], harsh and disaster environments management [5], smart policing, wildfire tracking [6], and many more. One of the main active research areas for UAV is the Coverage Path Planning (CPP). It aims to build a path for the drone to explore every location in an Area of Interest (AoI). Often, the main concerns in CPP are the efficiency and completeness of the coverage. The effectiveness of a CPP work is mainly related to the following performance indices such as the path length [7; 8], the mission completion time [9; 10], and the number of turns. These metrics are related with energy consumption [11; 12]. Most of the approaches try to reduce them in order to save energy.  
One of the key challenges in CCP is also to build paths in scenes with obstacles or pop-up threats. Areas could have obstacles and non-flying zones. Obstacles could be static or mobile (such as trees and birds). Pop-up threats are obstacles which are not known a priori by the drone. Different path planning methods rely on two-dimensional or three-dimensional information of the scene to build the trajectory path [13].  
Recent approaches in 3D path planning concentrate more on detecting and avoiding obstacle in a 3D view [13; 14; 15; 16; 17; 18; 19]. They have not given priority to compromise between the energy efficiency and the completeness of the area coverage [20] in the presence of obstacles. In our work, we propose an energy aware 3D obstacle avoidance (EAOA) which plans a full coverage path for an area of interest with the ability to avoid obstacles in a 3D view using a grid-based technique.  
3D trajectory planning shows great benefits, unlike 2D path planning, with graphical limitations. Solutions are not only constrained by UAV size, weight, and flying mechanisms. There should be a compromise between the trajectory length, the total coverage rate for the area, and the energy consumption of the drone. The number of turns a drone makes during the coverage mission is also another issue and needs to be minimized. More turning points means more energy consumption of the drone [21].  
In this paper, we present a grid-based path planning and a 3D obstacle avoidance technique. The planned path relies on a previous paper [14]. It combines the top-view and the frontal-view of the area that includes the obstacle. The algorithm generates the first path offline using the top-view then re-plans it using the front-view of the scene. We deal with static obstacles whose locations are known a priori to the UAV. We aim to avoid the obstacle with the shortest path and least number of turns possible taking into consideration two main factors, energy consumption and completion time.  
The remainder of this paper is organized as follows: Section 2 discusses related works; Section 3 provides details about our work; Section 4 presents the evaluation metrics and results; and Section 5 concludes the paper.