Mohammad Al-Shaboti

King Fahd University of Petroleum and Minerals

Papers

Total Citations

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About

Mohammad Al-Shaboti’s research focuses on multi-robot systems, task allocation, and multi-objective optimization, with a particular emphasis on dynamic and online decision-making in robotic teams. His most cited work, “Dynamic Multi-Objective Auction-Based (DYMO-Auction) Task Allocation” (2020), addresses the challenging problem of online dynamic multi-robot task allocation (MRTA). He proposed an auction-based approach that efficiently handles multiple conflicting objectives in real-time, overcoming limitations of existing heuristic methods. This contribution has garnered 26 citations, reflecting its relevance to researchers working on scalable and adaptive coordination strategies for autonomous robots. Al-Shaboti’s work bridges theoretical optimization with practical deployment, offering a framework that balances computational efficiency with solution quality. His research is particularly valuable for applications in disaster response, warehouse automation, and environmental monitoring, where robots must adapt to changing conditions. By advancing auction-based mechanisms for dynamic environments, Al-Shaboti has provided a foundation for future work in decentralized multi-robot coordination, making him a notable contributor to the field of robotics and artificial intelligence.