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Nick Mohammad

I am a PhD student in Computer Engineering at the University of Virginia. I work in the Autonomous Mobile Robots Lab (AMR) and Link Lab under Prof. Nicola Bezzo. I received both my B.S. in Computer Engineering and M.S. in Computer Science from the University of Virginia.

My research focuses on agile motion planning of ground and aerial vehicles for use in exploration and inspections of unknown, cluttered environments.

A Robust and Fast Occlusion-based Frontier Method for Autonomous Navigation in Unknown Cluttered Environments

Navigation through unknown, cluttered environments is a fundamental and challenging task for autonomous vehicles as they must deal with a myriad of obstacle configurations typically unknown a priori. Challenges arise because obstacles of unknown shapes and dimensions can create occlusions limiting sensor field of view and leading to uncertainty in motion planning.In this paper we propose to leverage such occlusions to quickly explore and cover unknown cluttered environments. Specifically, this work presents a novel occlusion-aware frontier-based approach that estimates gaps in point cloud data and shadows in the field of view to generate waypoints to navigate. Our scheme also proposes a breadcrumbing technique to save states of interest during exploration that can be exploited in future missions. For the latter aspect we focus primarily on the generation of the minimum number of breadcrumbs that will increase coverage and visibility of an explored environment.

This work has recently been accepted to IROS '22 which is taking place in Kyoto, Japan on October 23-27, 2022. To see our simulation and experiment results, visit our webpage for this paper.


ICRA 2022 Benchmark for Autonomous Robot Navigation (BARN) Challenge

At the International Conference on Robotics and Automation (ICRA) 2022, Rahul Peddi, Lauren Bramblett, and I competed in the BARN Challenge, which tasked competitors to develop a robust navigation strategy to navigate quickly and safely through cluttered unknown environments. The first stage of the competition took place in the simulated BARN dataset with a Clearpath Jackal as the mobile robot. Using a mapless, gap-based navigation scheme, we were able to place in the top three teams and secured a spot at the in person competition being held at ICRA 2022. There, we went up against UT Austin and Temple on real world obstacle courses and placed 2nd. For additional information about the competition and all of the competitors, see the competition website here. After the competition was complete, Clearpath Robotic's wrote an article for their website about the BARN Challenge, which can be found here.




University of Virginia


University of Virginia

B.S. in Computer Engineering

  • Capstone: "Word Puzzle Solving Robot"

M.S. in Computer Science

  • Thesis: "Occlusion-Aware Motion Planning of Autonomous Robots in Cluttered and Unknown Environments"

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