Papers
29
Total Citations
444
H-Index
12
About
Aamir Ahmad is a robotics researcher whose work sits at the intersection of multi-robot systems, cooperative perception, and autonomous aerial vehicles. His research has made significant contributions to cooperative localization and target tracking, where he pioneered least squares minimization frameworks for teams of mobile robots — work that has garnered over 70 citations and established foundational methods in the field. Ahmad has consistently advanced decentralized approaches to multi-robot coordination, developing scalable particle filter-based localization systems and MPC-driven obstacle avoidance strategies that operate effectively in dynamic, real-world environments. A particularly distinctive thread in Ahmad's portfolio is autonomous aerial human motion capture, where his AirPose and AirCapRL systems represent innovative fusions of UAV teams, deep reinforcement learning, and 3D pose estimation for unstructured outdoor settings — addressing longstanding limitations of calibrated, laboratory-bound capture systems. His reinforcement learning contributions extend further to autonomous blimp control, demonstrating breadth across aerial platforms. With formation control driven by cooperative perception, Ahmad has also shaped how robot teams maintain spatial coordination while maximizing observational performance. Collectively accumulating over 330 citations, his body of work reflects a sustained commitment to making multi-robot systems more intelligent, scalable, and deployable in challenging real-world conditions.
Research Focus
Key Achievements
Top Papers
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- 3Formation control driven by cooperative object tracking35 citations · 2014
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- 8Perception-driven multi-robot formation control24 citations · 2013
- 9Deep Residual Reinforcement Learning based Autonomous Blimp Control16 citations · 2022
- 10Towards Optimal Robot Navigation in Domestic Spaces16 citations · 2015