Alia Karim
Papers
2
Total Citations
19
H-Index
2
About
Alia Karim’s research centers on multi-robot path planning, with a specific focus on developing efficient algorithms for dynamic and obstacle-rich environments. Her work bridges probabilistic methods with classical search techniques to address the complex challenge of coordinating multiple autonomous agents. Her most cited paper, “Probabilistic Multi Robot Path Planning in Dynamic Environments: A Comparison between A* and DFS” (2013, 12 citations), introduces a two-phase probabilistic roadmap planner that leverages the A* search algorithm to navigate dynamic settings, effectively converting workspace into a searchable configuration space. Building on this, her 2014 paper (7 citations) proposes a decoupled approach combining probabilistic roadmaps, A*, and genetic algorithms to optimize path planning while avoiding static obstacles. Though her citation counts are modest, Karim’s contributions are notable for their practical, comparative methodology—systematically evaluating A* against depth-first search and integrating genetic algorithms for optimization. Her work provides a foundational framework for researchers tackling real-world multi-robot coordination, particularly in logistics, warehouse automation, and search-and-rescue operations. By emphasizing decoupled planning and probabilistic roadmaps, Karim has advanced the field’s understanding of how to balance computational efficiency with path optimality in complex environments.
Research Focus
Key Achievements
Top Papers
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