# OADC: An Obstacle-Avoidance Data Collection Scheme Using Multiple Unmanned Aerial Vehicles
## 2 Related Works
[21] focused on minimizing the task-termination time of a UAV while keeping in mind the sensor's communication link limits and avoiding obstacles in the system. However, this work did not analyze the UAV data collection trajectories, supporting a large and widely distributed ground sensor. Although the above studies considered UAV-aided data collection and obstacle avoidance, they did not consider the UAV path as a Hamiltonian cycle. On the other hand, in the proposed OADC scheme, after finishing a data collection tour, the UAV battery was fully recharged from the sink, which is a Hamiltonian tour that prepares the UAV for the next tour. UAV obstacle avoidance without considering data collection: Several studies in different fields from WSN that used UAVs considered an obstacle in the path of UAVs in their environment. For instance, one study [22] considered an algorithm called rapidly exploring random trees (RRTs) for planning the path with the kinematic constraints of UAVs. They focused on finding the optimal routes for the UAV from starting points to destination points while encountering consistent and moving obstacles. [PERSON] et al. [23] focused on UAV route planning to avoid obstacles from the source to the goal position. The authors developed a method where a graph with elliptic tangents is used that ensures obstacle avoidance. The study presented in [24] proposed collision-idle optimal or quasi-optimal paths for UAVs in a 3D urban area, considering the obstacles and focusing on time complexity reduction. In [25], the authors focused on various task allocation and trajectory planning procedures for multiple UAVs while avoiding obstacles and arriving at the targets. The work in [26] focused on finding the flying paths for UAVs with obstacle avoidance using an enhanced RRT algorithm. In their proposed approach, all the UAVs reached their destinations at the same time. Furthermore, the authors of [27] designed the UAV path for two modes: with and without obstacles. A modified Grossberg neural network (GNN)-based algorithm was developed to detect where there are obstacles and avoid them. The other mode depends on the optimal formulation. However, their considered scenario was much different from the OADC scheme, and they considered the UAV path without any cycle. In contrast, in the proposed work, each UAV trajectory is a TSP tour where data are delivered to the base station (BS) within a given flight time in an obstacle-contained environment. In each TSP tour, the sub-tour between every two sensors is the shortest path between them while avoiding the obstacles. Furthermore, this paper considers multiple UAVs to ensure fast data delivery to the BS because using only one UAV to collect data from a large area can be time-consuming, and the UAV needs more energy to collect more data. Moreover, to the best of the authors' knowledge, no previous study examined multiple UAVs that collect data from various groups of sensors while avoiding numerous obstacles, whereas the UAV sensor visiting order (UAV trajectory or path) is a Hamiltonian cycle.