Kossar Jeddisaravi

University of Science and Technology of Mazandaran, Universidade Federal de Minas Gerais

Papers

Total Citations

198

H-Index

About

Kossar Jeddisaravi is a leading researcher in intelligent robotics, whose work sits at the critical intersection of multi-robot coordination, motion planning, and autonomous navigation. Her primary contributions have reshaped how robotic teams handle complex, real-world tasks. Her most influential work, "Multi-robot exploration in task allocation problem" (2021, 78 citations), provides a foundational framework for efficiently distributing exploration duties among multiple agents. She has also pioneered multi-objective approaches to robot motion planning, notably in "Multi-objective approach for robot motion planning in search tasks" (2016, 50 citations) and "Multi-objective mobile robot path planning based on A* search" (2016, 39 citations), where she demonstrated how to optimize for competing goals like path length, safety, and energy—using a compelling Mars Rover scenario. Further extending this work, she addressed the challenges of "Multi-objective multi-robot deployment in a dynamic environment" (2016, 26 citations). Demonstrating versatility, her more recent work, "Line Following Autonomous Driving Robot using Deep Learning" (2020), applies modern deep neural networks to classical control problems. With over 198 total citations, Jeddisaravi’s research provides essential algorithms for the next generation of autonomous systems operating in unpredictable, multi-objective environments.