Leong, Wai Lun (National University of Singapore), Huang, Sunan (National Universtiy of Singapore), Wang, Pengfei (National University of Singapore), Ma, Zhengtian (National University of Singapore), Yang, Hong (National University of Singapore), Sun, Jingxuan (National University of Singapore), Zhou, Yu (National University of Singapore), Abdul Hamid, Mohamed Redhwan (National University of Singapore), Srigrarom, Sutthiphong (National University of Singapore), Teo, Rodney (National University of Singapore)

Vision-Based Sense and Avoid with Monocular Vision and Real-Time Object Detection for UAVs

Scheduled for presentation during the Regular Session "See-and-avoid Systems" (FrC2), Friday, June 18, 2021, 15:00−15:20, Kozani

2021 International Conference on Unmanned Aircraft Systems (ICUAS), June 15-18, 2021, Athens, Greece

This information is tentative and subject to change. Compiled on April 25, 2024

Abstract

The use of unmanned aerial vehicles (UAVs) or drones have become ubiquitous in the recent years. Collision avoidance is a critical component of path planning, allowing multi-agent networks of cooperative UAVs to work together towards common objectives while avoiding each other. We implemented, integrated and evaluated the effectiveness of using a low cost, wide angle monocular camera with real-time computer vision algorithms to detect and track other UAVs in local airspace and perform collision avoidance in the event of a communications degradation or the presence of non-cooperative adversaries, through experimental flight tests where the UAVs were set on collision courses.