Papers
21
Total Citations
560
H-Index
10
About
Imran Mir is a robotics and autonomous systems researcher whose work sits at the intersection of bio-inspired optimization, multi-robot coordination, and autonomous path planning. His research has made significant contributions to solving complex navigation and exploration challenges in obstacle-cluttered environments, leveraging nature-inspired meta-heuristic algorithms to push the boundaries of what autonomous robotic systems can achieve. Mir's most influential work, a 2020 study on PSO–GWO hybrid optimization for autonomous robot path planning, has garnered 141 citations, establishing him as a notable voice in multi-objective trajectory optimization. Building on this foundation, he has developed a compelling body of research around multi-robot space exploration, introducing frameworks such as the Frequency Modified Whale Optimization Algorithm and the Coordinated Multi-Robot Exploration Aquila Optimizer, collectively accumulating hundreds of citations. These systems address real-world challenges of task allocation, sensor-driven mapping, and collision-free mobility in complex terrains. His more recent contributions extend toward parallel computing strategies and adaptive optimization frameworks, reflecting an ambition to scale solutions for increasingly demanding environments. With over 500 cumulative citations and a consistent publication record across leading venues including AIAA, Mir's work offers valuable, practically oriented insights for researchers working in autonomous robotics, swarm intelligence, and intelligent systems design.
Research Focus
Key Achievements
Top Papers
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- 3Multi-Robot Space Exploration: An Augmented Arithmetic Approach67 citations · 2021
- 4A Centralized Strategy for Multi-Agent Exploration56 citations · 2022
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- 10Efficient Environment Exploration for Multi Agents : A Novel Framework11 citations · 2023