Obstacle Awareness System of An Indoor UAV with Multi-Sensor Fusion Algorithm

Abstract

A Flying Ad-hoc network (FANET) emerges recently due to its flexibility in terms of flying tracks and movements. As one type of Unmanned Aerial Vehicles (UAVs), a drone can be considered as the low-cost platform to implement the FANET. In a particular case, the flying tracks and movements of a drone can encounter inevitable obstacles such as building construction and any random objects. Thus, this paper focused on the obstacle issue in drone’s movements and proposed the feasibility of Sensor Fusion algorithm to distinguish the obstacle in the indoor environment. Under two conditions: single and multiple obstacles scenarios, the autonomous drone implementing Kalman Filter in Sensor Fusion experienced the real time response linearly as the distance increases.