Seongmo An

Seoul National University of Science and Technology

Papers

Total Citations

H-Index

About

Seongmo An is a researcher focused on advancing the computational efficiency of autonomous mobile robots (AMRs), with key contributions in hardware acceleration and path planning. His work addresses critical challenges in power-constrained robotic systems, particularly for LiDAR-based perception and navigation. An’s most cited paper, “Grid-Based DBSCAN Clustering Accelerator for LiDAR’s Point Cloud” (2024, 9 citations), introduces a novel grid-based approach to accelerate density-based clustering, significantly reducing the computational burden on low-power cores in battery-operated autonomous robots. This work highlights his expertise in designing energy-efficient hardware accelerators for real-time object detection. Additionally, his 2025 paper, “An Accelerated Block Searching Approach in A* for Autonomous Mobile Robots,” proposes a memory-efficient path-planning algorithm that enhances A* search speed for large-scale maps, addressing critical resource constraints in AMRs. Though early in his career, An’s research demonstrates a clear impact on enabling practical, low-power autonomy, with his clustering accelerator offering a promising solution for embedded systems. His work bridges algorithm design and hardware implementation, positioning him as an emerging contributor to efficient robotic perception and navigation.