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About

Samaneh Alsadat Saeedinia is a robotics researcher specializing in intelligent navigation and path planning for autonomous mobile robots operating in dynamic, obstacle-rich environments. Her work bridges optimal control theory, reinforcement learning, and geometric optimization to create smooth, collision-free navigation systems. Her most influential paper, "Optimal predictive neuro-navigator design for mobile robot navigation with moving obstacles" (2023, 12 citations), introduces a predictive neural framework for real-time navigation amidst moving obstacles. She further advanced the field with "The synergy of the multi-modal MPC and Q-learning approach for the navigation of a three-wheeled omnidirectional robot" (2022, 10 citations), which synergizes model predictive control with Q-learning to handle dynamic constraints and obstacle avoidance. Her recent contributions include a smooth PSO-IPF navigator (2024) and a comprehensive survey on geometric optimal navigation (2025). With a cumulative citation count approaching 30, Saeedinia’s research is gaining traction for its practical integration of theory and algorithms, offering robust solutions for autonomous systems from self-driving cars to surgical robots.