Saeed Mozaffari
Papers
6
Total Citations
79
H-Index
4
About
Saeed Mozaffari is a rising researcher in robotics and autonomous systems, whose work centers on intelligent control, collaborative mapping, and human-robot interaction. His major contributions span deep reinforcement learning for control systems—demonstrated by his highly cited work on DDPG–PPO agents for rotary inverted pendulums (31 citations)—and collaborative SLAM, where he developed feature-based occupancy map-merging techniques that enable multiple robots to efficiently share and integrate environmental data (16 citations). Mozaffari has also tackled challenging perception problems, experimentally analyzing LiDAR navigation in environments with mirror-like objects (16 citations), and advanced human-robot collaboration through learning-from-demonstration systems using wearable sensor gloves (9 citations). His research on optimizing robotic arm trajectories with evolutionary algorithms for energy and time reduction further showcases his commitment to practical, efficient automation. With multiple papers published in 2023 alone, Mozaffari is establishing himself as a versatile contributor to modern robotics, bridging theoretical control methods with real-world deployment challenges.
Research Focus
Key Achievements
Top Papers
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- 2Feature-Based Occupancy Map-Merging for Collaborative SLAM16 citations · 2023
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