Seiji Hashimoto

Papers

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About

Seiji Hashimoto is a leading researcher in autonomous mobile robotics, with a focus on path planning, tracking, and intelligent navigation systems. His work bridges deep reinforcement learning and multi-objective optimization to solve critical challenges in real-world robot autonomy. In his highly cited 2024 paper, Hashimoto pioneered a deep reinforcement learning approach for path following, demonstrating how autonomous mobile robots can learn robust tracking behaviors essential for service and logistics applications. Building on this, his 2025 study introduced an enhanced pure pursuit algorithm optimized by NSGA-II, integrated with high-precision GNSS navigation to dramatically improve accuracy in complex environments. With over 40 citations across his most influential works, Hashimoto’s contributions are shaping the next generation of mobile robot control systems. His research is particularly notable for combining theoretical rigor with practical deployment considerations, making his algorithms viable for industrial automation, warehouse logistics, and autonomous transportation. For students and researchers, Hashimoto’s work offers a compelling model of how to advance fundamental robotics techniques while addressing real-world navigation constraints.