Papers
10
Total Citations
247
H-Index
7
About
Muhammad Fahad is a leading researcher in human-robot interaction and autonomous multi-robot systems, with a focus on developing socially-aware navigation and environmental monitoring technologies. His most impactful work centers on using deep reinforcement learning and inverse reinforcement learning to enable robots to regulate pedestrian flows in crowded spaces, achieving 80 citations for his seminal 2018 paper on robot-assisted pedestrian regulation. Fahad has made significant contributions to dynamic plume tracking, where he pioneered cooperative control strategies for autonomous surface vessels to monitor pollutant propagation in marine environments, earning 50 citations for his 2019 work. His research extends to human indoor localization using robot-assisted smartphone and Kinect sensor fusion, as well as learning human navigation behavior from measured trajectories to improve robot social compliance. With over 250 total citations across his publications, Fahad's work has advanced both theoretical frameworks and practical implementations in crowd safety, environmental monitoring, and human-aware robot navigation. His field experiments with unmanned surface vessels for ocean plume characterization demonstrate his commitment to real-world validation of robotic systems.
Research Focus
Key Achievements
Top Papers
- 1Robot-Assisted Pedestrian Regulation Based on Deep Reinforcement Learning80 citations · 2018
- 2Dynamic Plume Tracking by Cooperative Robots50 citations · 2019
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- 4Robotic simulation of dynamic plume tracking by Unmanned Surface Vessels24 citations · 2015
- 5Robot-assisted smartphone localization for human indoor tracking12 citations · 2018
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- 9Evaluation of ocean plume characteristics using unmanned surface vessels5 citations · 2017
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