Papers
7
Total Citations
57
H-Index
5
About
Muhammad Usman Arif is a leading researcher in multi-robot systems, with his work centered on the critical challenge of multi-robot task allocation (MRTA). His primary contribution is the development of flexible, evolutionary computing-based frameworks that can handle the complex, diverse scenarios inherent in real-world multi-robot operations. Arif’s most influential work, "An Evolutionary Traveling Salesman Approach for Multi-Robot Task Allocation" (2017, 16 citations), pioneered a novel method for assigning tasks to robot teams. He significantly advanced the field by introducing Rostam, a task allocation framework detailed in his 2021 paper (12 citations), which is specifically designed to efficiently utilize multi-tasking robots—robots capable of performing multiple jobs simultaneously to reduce operational time and energy. His research also tackles the sophisticated problem of time-extended multi-robot coalition formation (9 citations), where robots must form and re-form teams over time to handle a large number of tasks. Through a series of papers (2017-2019), Arif has established a generic, modular framework that unifies solutions across the MRTA taxonomy, demonstrating a sustained impact on how robotic teams are coordinated for efficiency and scalability.
Research Focus
Key Achievements
Top Papers
- 1An Evolutionary Traveling Salesman Approach for Multi-Robot Task Allocation16 citations · 2017
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- 3Robot coalition formation against time-extended multi-robot tasks9 citations · 2021
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- 7On developing a hybrid approach for kick optimization in humanoid robots2 citations · 2014