# EAOA: Energy-Aware Grid-Based 3D-Obstacle Avoidance in Coverage Path Planning for UAVs
## 2 Related Works  
During the path planning mission, avoiding obstacles or non-flying zones is one of the critical challenges for UAVs. The trajectory needs not only to bypass obstacles but also to be efficient in terms of energy consumption and completion time. Obstacles could be mobile such as pigeon, waving flag, or could be fixed such as street lighting column, a building, etc. Different research works [13; 15; 16] rely on either two-dimensional or three-dimensional methods for obstacle detection and avoidance. In [17], a 3D-SWAP technique for 3D collision avoidance with multiple UAVs is proposed. UAVs rely on local data from sensors and positions of other neighboring UAVs. Their approach is decentralized and requires low computational load. Another approach is presented in [18]. Authors propose a Model Predictive Control (MPC), and it aims to find an optimal path for an (UAV) in a changing environment. The work aims to avoid collision in the presence of non fly zones and pop-up threats as well as fixed obstacles. Authors in [19] propose a Fast Geometric Avoidance algorithm (FGA) for 3D collision avoidance. The algorithm takes into account the avoidance start time based on kinematic considerations, collision probability, and navigational constraints. The efficiency of the work was evaluated with Monte Carlo simulations and scenarios in an aircraft simulator. To adapt a real-time path planning to UAV performances and capability (variations and fluctuations), authors in [22] propose an adaptive path planning for UAVs. In [23], a method to detect changes in image patches is proposed in order to understand variations in the environment and obstacle sizes. In [24], the authors propose an approach for obstacle detection and avoidance. It uses a monocular camera on UAV and motion control measurements to imitate the human behavior in detecting objects in the environment. Authors in [25] developed an algorithm to build a map of the environment and to generate a collision free path for autonomous UAVs. They used an RGB-D camera with sensors and tested their work in an indoor environment in the presence of obstacles. A fixed wing UAV is used in [26] in two phases. They propose a solution for obstacle avoidance by first generating an offline path, then by relying on it to generate an intelligent real-time pop-up threats avoidance path. The work in [27] was inspired by the phenomenon of fluid disturbance to re-plan and tune the trajectory for a 3D collision-free path. The authors use the interfered fluid dynamical system (IFDS) algorithm for UAV. In [28], the noisy environment is not taken into consideration. The later approach relies on sensor readings, to localize the obstacle and understand its motion, in order to determine its velocity and autonomously plan a collision-free path. Another algorithm for autonomous obstacle avoidance is proposed in [29]. It relies on radars and image processing of video frames to detect and avoid obstacles. It aims to reduce the computational load as UAVs are energy- and memory-constrained. In [30], authors use board passive sensors like vision systems on fixed wing drones. UAV flies at lower attitude for aerial surveillance. Determining the position of the obstacle relies on local map of the environment with measurements from sensors aboard the drone. Authors in [31] provides a 2D solution to avoid obstacles using a grid based technique. They propose an algorithm that plans a coverage path for UAVs over irregular shaped areas using a grid-based method with minimum energy consumption. Authors enhanced the algorithm proposed by [PERSON] et al. [25], by substituting the Cost Function with a novel Energy-Cost Function. The novel algorithm comes up with 17% in energy gain compared to the algorithm provided in [25]. They also included two techniques that significantly decrease the computation Time. Authors choose different starting mission points in order to decide about the best path in an offline mode.