Kasra Manavi

Texas A&M University, University of New Mexico

Papers

Total Citations

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About

Kasra Manavi’s research bridges the gap between theoretical robotics and real-world complexity, with a particular focus on motion planning, multi-agent systems, and computational biology. His most-cited work, “Toward realistic pursuit-evasion using a roadmap-based approach” (9 citations), introduces a graph-based framework that integrates multi-agent simulation with roadmap path planning, enabling more realistic group behaviors in pursuit-evasion scenarios. This work addresses a key limitation in traditional approaches by modeling dynamic interactions in complex environments. In “Construction and use of roadmaps that incorporate workspace modeling errors” (8 citations), Manavi tackles the critical challenge of uncertainty in Probabilistic Roadmap Methods (PRMs), proposing techniques to build robust roadmaps that account for workspace modeling errors—a significant step toward reliable high-DOF motion planning. Demonstrating the breadth of his impact, Manavi also applies geometric simulation to immunology in “Influence of model resolution on geometric simulations of antibody aggregation” (7 citations), investigating how computational models can predict allergic responses mediated by IgE antibody aggregation. His work consistently emphasizes practical applicability, from robotics to biomedical simulation, and has laid groundwork for more adaptive, error-tolerant systems.