Our Mission
Unmanned Aerial Vehicles (UAVs) Safety
My research in the Drone lab demonstrates a dedication to advancing Unmanned Aerial Vehicles (UAVs) Safety technology through theoretical innovation, practical implementation, and interdisciplinary collaboration. During the past 5 years, I have published 9 peer-reviewed papers, including
five journal and four conference papers. These papers have received more than 500 citations. I currently advise four master’s students and co-advise two PhD students from SIUe. Also, seven students have graduated from the Drone lab (3 with master’s and 4 with bachelor’s degrees), working at companies like
Caterpillar, Cooper Machine, Thyssenkrupp Presta and P&G.
Comparing YOLOv3, YOLOv4 and YOLOv5 for autonomous landing spot detection in faulty UAVs
In-flight system failure is one of the major safety concerns in the operation of unmanned aerial vehicles (UAVs) in urban environments. To address this concern, a safety framework consisting of following three main tasks can be utilized: (1) Monitoring health of the UAV and detecting failures, (2) Finding potential safe landing spots in case a critical failure is detected in step 1, and (3) Steering the UAV to a safe landing spot found in step 2. In this paper, we specifically look at the second task, where we investigate the feasibility of utilizing object detection methods to spot safe landing spots in case the UAV suffers an in-flight failure.
Comparing YOLOv3, YOLOv4 and YOLOv5 for autonomous landing spot detection in faulty UAVs
In-flight system failure is one of the major safety concerns in the operation of unmanned aerial vehicles (UAVs) in urban environments. To address this concern, a safety framework consisting of following three main tasks can be utilized: (1) Monitoring health of the UAV and detecting failures, (2) Finding potential safe landing spots in case a critical failure is detected in step 1, and (3) Steering the UAV to a safe landing spot found in step 2. In this paper, we specifically look at the second task, where we investigate the feasibility of utilizing object detection methods to spot safe landing spots in case the UAV suffers an in-flight failure.
Comparing YOLOv3, YOLOv4 and YOLOv5 for autonomous landing spot detection in faulty UAVs
In-flight system failure is one of the major safety concerns in the operation of unmanned aerial vehicles (UAVs) in urban environments. To address this concern, a safety framework consisting of following three main tasks can be utilized: (1) Monitoring health of the UAV and detecting failures, (2) Finding potential safe landing spots in case a critical failure is detected in step 1, and (3) Steering the UAV to a safe landing spot found in step 2. In this paper, we specifically look at the second task, where we investigate the feasibility of utilizing object detection methods to spot safe landing spots in case the UAV suffers an in-flight failure.
Publications | See All
Geometric Integral Attitude Control on SO(3
This article proposes a novel integral geometric control attitude tracking scheme, utilizing a
coordinate-free representation of attitude on the Lie group of rigid body rotations, SO(3). This scheme
exhibits almost global asymptotic stability in tracking a reference attitude profile.
Geometric Integral Attitude Control on SO(3
Geometric Integral Attitude Control on SO(3
This article proposes a novel integral geometric control attitude tracking scheme, utilizing a
coordinate-free representation of attitude on the Lie group of rigid body rotations, SO(3). This scheme
exhibits almost global asymptotic stability in tracking a reference attitude profile.