Hao Xun

Papers

Total Citations

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About

Hao Xun is a researcher focused on intelligent robotics and autonomous navigation, with particular expertise in mobile robot path planning and scalable image processing for surveillance applications. His most impactful work introduces a self-adaptive dynamic window approach (DWA) for local path planning, addressing critical limitations of the traditional DWA algorithm—including unreachable target points and suboptimal routing in dense obstacle environments. This 2021 paper has garnered 20 citations, reflecting its practical significance in improving real-time navigation performance for mobile robots. Xun also contributed to the development of RineGAN, a scalable image processing architecture designed for large-scale surveillance systems that integrate multiple camera-equipped robots, overcoming the narrow field-of-view limitations of single-robot setups. While this work has received 4 citations, it addresses a growing need in smart building and industrial security. Xun’s research bridges fundamental algorithmic improvements with real-world deployment challenges, making his contributions valuable for both roboticists developing autonomous systems and engineers working on multi-agent surveillance networks. His work demonstrates a commitment to enhancing robot autonomy and perception in complex, obstacle-rich environments.