Mohammad Khaneghaei
Papers
3
Total Citations
10
H-Index
2
About
Mohammad Khaneghaei is an emerging researcher whose work sits at the intersection of autonomous systems, unmanned aerial vehicles (UAVs), and intelligent control strategies. His research focuses on advancing UAV autonomy through sophisticated flight planning, vision-based guidance, and multi-agent coordination — areas of growing importance across surveillance, mapping, and delivery applications. Khaneghaei's most recognized contribution, "Software in the Loop Simulation for an Autonomous Multirotor Flight Planning and Landing with ROS and Gazebo" (2023, 6 citations), demonstrates his commitment to practical, simulation-driven development of autonomous landing systems. Building on this foundation, his 2025 work introduces a vision-based integrated guidance and control strategy specifically designed to handle faulty UAV scenarios — a critical safety consideration for real-world deployment. More recently, his comprehensive review of intelligent hybrid optimization algorithms for multi-agent aerial path planning (2026) signals his expanding interest in cooperative UAV systems and swarm intelligence. Though early in his career, Khaneghaei's research reflects a coherent and ambitious trajectory: bridging theoretical optimization with experimentally validated autonomous systems. Students interested in UAV autonomy, robotics middleware like ROS, or multi-agent path planning will find his work an accessible and technically rigorous entry point into these rapidly evolving fields.
Research Focus
Key Achievements
Top Papers
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