Papers
11
Total Citations
254
H-Index
9
About
Suleman Mir is a robotics and artificial intelligence researcher whose work centers on multi-agent systems, autonomous exploration, and bio-inspired optimization algorithms. His research addresses one of the most compelling challenges in modern robotics: enabling teams of autonomous robots to efficiently navigate and map unknown, obstacle-laden environments without human intervention. Mir's most significant contributions lie in developing hybrid optimization frameworks that merge deterministic exploration strategies with stochastic bio-inspired algorithms. His "Coordinated Multi-Robot Exploration Aquila Optimizer" (CME-AO), introduced in 2022, garnered 56 citations and exemplifies his signature approach of adapting nature-inspired metaheuristics — including the Aquila Optimizer and Whale Optimization Algorithm — to complex multi-robot coordination problems. His prolific 2022 output alone accounts for nearly 200 citations across five papers, demonstrating remarkable early-career impact. Notably, Mir has consistently presented his work at prestigious aerospace and robotics venues, with several papers featuring video presentations through the AIAA digital library. His 2023 contributions further refine earlier frameworks by introducing adaptive, variational, and reinforcement-enhanced approaches, reflecting a clear trajectory toward increasingly sophisticated autonomous systems. For students entering robotics research, Mir's body of work offers an excellent foundation in optimization-driven multi-agent exploration methodologies.
Research Focus
Key Achievements
Top Papers
- 1A Centralized Strategy for Multi-Agent Exploration56 citations · 2022
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- 7Efficient Environment Exploration for Multi Agents : A Novel Framework11 citations · 2023
- 8Multi-Agent Variational Approach for Robotics: A Bio-Inspired Perspective10 citations · 2023
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- 10Reinforced Whale Optimizer for Ground Robotics : A Hybrid Framework6 citations · 2023