|MIT Department: Electrical Engineering and Computer Science
Faculty Mentor: Prof. Nicholas Roy
Undergraduate Institution: New Jersey Institute of Technology
Website: LinkedIn, College Spotlight
My name is Jehan Shalabi, a proud American Arab of Palestinian descent from Paterson, New Jersey. I am currently a first-generation rising senior at the New Jersey Institute of Technology majoring in Electrical Engineering. Before transferring to NJIT, I graduated from Passaic County Community College at the age of 18 with an Associate of Science degree in Engineering Science. As a Ronald E. McNair scholar, I conducted research on Drone-Assisted Mobile Networking to study how to use drones to carry small cellular antennas to improve mobile network performance. I plan to pursue my Ph.D. in Electrical Engineering; my research interests range from designing novel electronic systems to developing new navigation strategies for autonomous robotics. My goal is to become an astronaut, a path influenced by my internship at the NASA Goddard Space Flight Center and completing the NASA L’SPACE Mission Concept Academy. Outside of academia, I enjoy playing sports and spending time with family and friends.
Efficient Ground Vehicle Navigation Using Aerial Images
Jehan Shalabi1, Jacopo Banfi2, and Nicholas Roy2
1Department of Electrical & Computer Engineering,
New Jersey Institute of Technology
2Department of Electrical Engineering & Computer Science,
Massachusetts Institute of Technology
One of the most significant challenges in robotic navigation of previously unknown environments is the need to find efficient routes that avoid dead ends. Having robots work as teams to provide additional information about the terrain can help solve this problem. In this project, we consider a small team of two heterogeneous, autonomous robots composed of a ground vehicle and quadrotor. As the quadrotor hovers at high altitudes, it easily acts as an additional long-range sensor for the ground vehicle. Aerial images of the environment can be used by the ground vehicle to determine which regions currently beyond its sensing range are traversable. The ground vehicle then uses this new obstacle map to plan paths less likely to reach dead ends. To differentiate between objects in an environment and create an obstacle map, RGB thresholding is used. This obstacle map is then used to plan a path for the ground vehicle using the A* search algorithm. Using a high-fidelity simulator, we show that the proposed system can be used to plan efficient paths. The results of this research can help improve and expand the use of robots in exploring novel terrains for search and rescue, military surveillance, autonomous package deliveries, agriculture, mapping, and inspection.