Master's Thesis Abstract

The multi-agent robotic system has been proved to be more superior in undertaking
functionalities, arduous or even impossible when performed by single agents. The
increased efficiency in multi-agent systems is achieved by the execution of the task
in a cooperative manner. But to achieve cooperation in multi-agent systems, a good
localization system is an important prerequisite. Currently, most of the multi-agent
systems rely on the use of the GPS to provide global positioning information which
suffers great deterioration in performance in indoor applications, and also all-to-all
communication between the agents will be required which is not efficient especially
when the number of agents is large. In this regard, a real-time localization scheme
is introduced which makes use of the robot's onboard sensors and computational
capabilities to determine the states of other agents in the multi-agent system. This
algorithm also takes advantage of the swarming behavior of the robots in the
estimation of the states. This localization algorithm was found to produce more
accurate agent state estimates as compared to a similar localization algorithm that
does not take into account the swarming behavior of the agents in simulations and
the real experiment involving two Unmanned Aerial Vehicles.

Areas of Expertise and Research Interests

  • Automatic system Control
  • Robotics
  • Multi-Robot Systems
  • Localization
  • Machine Learning 
  • Artificial Intelligence

Education Profile

  • MS in Electrical Engineering, King Abdullah University of Science and Technology, Saudi Arabia(2019)
  • BS in Mechatronics Engineering, University of Turkish Aeronautical Association, Turkey (2017)

Selected Publications

Rajab,F.O (July,2019) Swarm Localization and Control via On-board Sensing and Computation,10.25781/KAUST-NSP83
Omar Rajab, F.-H., Guler, S., & Shamma, J. S. (2020). Peer-to-Peer Localization via On-board Sensing for Aerial Flocking. 2020 17th International Conference on Ubiquitous Robots (UR). doi:10.1109/ur49135.2020.9144914