UAV

iHunter: Empowering Autonomous UAVs for Robust, Real-Time Aerial Terget Tracking and Trajectory Prediction

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B9 L2 R2325

This talk introduces iHunter, an integrated framework for UAVs to reliably detect, track, and predict the trajectories of moving targets by fusing multi-modal perception with state estimation, employing kinematic predictors and a GRU-based neural network trained on synthetic data, and demonstrating improved tracking stability and predictive performance for aerial surveillance and target interception.

Mohammed Alwagait

Mohammed is a PhD Student in the Electrical and Computer Engineering program (ECE) at KAUST. He focuses on Reinforcement Learning Based Energy Saving Path Planning and Control for UAVs.

Sim-to-Real Transfer for Autonomous Navigation

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B2 L5 R5220

This work investigates the problem of transfer from simulation to the real world in the context of autonomous navigation. To this end, we first present a photo-realistic training and evaluation simulator Sim4CV which enables several applications across various fields of computer vision. Built on top of the Unreal Engine, the simulator features cars and unmanned aerial vehicles (UAVs) with a realistic physics simulation and diverse urban and suburban 3D environments. We demonstrate the versatility of the simulator with two case studies: autonomous UAV-based tracking of moving objects and autonomous driving using supervised learning.

Data assimilation and optimal control in the context of UAV-based flash flood monitoring

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B5 L5 R5209

Flash floods are one of the most common natural disasters worldwide, causing thousands of casualties every year. The emergence of Unmanned Aerial Vehicles (UAVs) gives the possibility to monitor these events over large geographical areas. In this talk, we focus on the problem of trajectory planning for a swarm of unmanned aerial vehicles sensing flooding conditions.

Civil drones traffic management: Wireless communication for safe UAV

Prof. Sofie Pollin, KU Leuven

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KAUST

UAV

In this webinar, we describe the common terminology for these two communities. First, the traffic management system architecture, requirements, terminology, and services are discussed. A quick overview of existing technologies that can be useful for aerial deconfliction. Next, the focus will shift to wireless technologies used for the tactical (while flying) deconfliction: ADS-B, p2p LoRa, WiFi, FLARM.

Computer vision: Teaching computers how to see the world

1 min read ·
Ghanem works in the Visual Computing Center (VCC) and is interested in computer vision, image processing, and machine learning.

Robots learn by checking in on team members

1 min read ·
Innovative drone designs and software enables a team of drones to work together in a coordinated approach.

KAUST Ph.D. student Matthias Mueller wins award for drone research

1 min read ·

UAV

KAUST Ph.D. student Matthias Mueller, who works in the Image and Video Understanding Lab under the supervision of KAUST Associate Professor Bernard Ghanem, won the Best Paper/Presentation Award at the 2nd International Workshop on Computer Vision for Unmanned Aerial Vehicles (UAVs). UAVs are commonly known as drones. The workshop was held in September 2018 in Germany in the post-proceedings of the 15th European Conference on Computer Vision workshops. Müller's paper entitled "Teaching UAVs to Race: End to End Regression of Agile Controls in Simulation" outlines the research of Müller and his

ISL PhD Student Osama Bushnaq does Internship at UBC

1 min read ·

UAV IoT

ISL PhD Student Osama Bushnaq did an internship at the University of British Coloumbia in Canada. His internship was focused on UAV-IoT. Every year, an average of 2.5 million hectares (ha) is burnt only in Canadian forests, which costs around 500 million to 1 billion Canadian dollars per year. The current forest fire detection methods such as satellite imaging and infrared cameras are not reliable. In order to detect forest fires before getting out of control, a new fire detection method based on Unmanned Aerial Vehicle Internet of Things (UAV-IoT) network was studied during this internship. A

Mohammed Aldosari

Ph.D. Student, Mechanical Engineering
Education 2021.08 - Present Ph.D. Student in Mechanical Engineering, KAUST, KSA 2018.08 - 2020.06 M.S. in Mechanical Engineering, KAUST, KSA 2011.08 - 2016.05 B.Sc. in Aerospace Engineering, KFUPM, KSA Professional Profile 2022.01 - 2022.05 Teaching Assistant, CEMSE, KAUST, KSA 2017.07 - Present Researcher, Center of Excellence for Aeronautics & Astronautics, King Abdulaziz City for Science and Technology, KSA 2017.04 - 2017.07 Maintenance Planning Engineer, SaudiGulf Airlines, KSA Selected Publications M.N.Aldosari, K.K.Yalamanchi, X. Gao, S.M. Sarathy, "Predicting entropy and heat capacity

Kuat Telegenov

An experienced Robotics Research Engineer and Laboratory Manager.

Hajar El Hammouti

Postdoctoral Research Fellow, Electrical and Computer Engineering
Publications dblp Scopus LinkedIn Education Profile Ph.D. in Telecommunications and Computer Science, National Institute of Posts and Telecommunications (INPT), Rabat, Morocco, Jan. 2013- May. 2017. 3rd year of engineering studies at ENSEEIHT, Toulouse, France, 2011-2012. Engineering studies at INPT, Rabat, Morocco, 2009 - 2011. Awards and Distinctions Best Paper Award, UNet 2018. Best Student Paper Award, WINCOM 2015. Best Student Paper Award, NETYS 2015.

Bernard Ghanem

Professor Ghanem's research focuses on computer vision and machine learning, emphasizing large-scale video understanding, vision for automated navigation and theoretical foundations.