Development of Obstacle Detection System Based On the Integration of Different Based Sensor for Small-Sized UAV - Nawawi, Muhammad Amirul; Ramli, Muhammd Faiz (Progress in Aerospace and Aviation Technology, 2023)
Due to the physical size and weight limits of small unmanned aerial vehicles (UAVs), developing a reliable obstacle detection a system that can provide an effective and safe avoidance path is extremely difficult. Prior work has tended to use a vision-based sensor as the primary detecting sensor however, this has resulted in a high reliance on texture appearance and a lack of distance sensing capabilities. Furthermore, due to the inability to detect the free region, vision-based sensor detection systems have difficulty developing a trusted safe avoidance path. However, most wide spectrum range sensors are bulky and expensive, making them unsuitable for small UAVs. This project aims to construct an obstacles detection system with the integration of various based sensors for a small UAV. The potential obstacles are identified by categorizing feature points identified in image frame. The suggested approach was tested in a real-world setting for both of the observed scenarios, which included various obstacles configurations. Two types of scenarios are experimented in this project consists of single frontal obstacles and presence of side obstacles alongside the frontal obstacles. On top of that, the position of the side obstacle is aligned to the frontal obstacle and then will be positioned in the increment of 20cm further from the frontal obstacle in order to analyse the outcome of the proposed algorithm. The proposed detection system had a possibility to be a trustworthy system even after utilising the depth perception technique, however this does not imply that the proposed system is faultless. The results show that the suggested algorithm system detects and distinguishes between the potential obstacles and free region for a single frontal obstacle perfectly. However, there were improvements that should be implemented with the proposed system’s ability of detection for multiple obstacles.