Jun Liang

China Academy of Launch Vehicle Technology, Shanghai Jiao Tong University

Papers

Total Citations

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About

Jun Liang is a researcher whose work bridges the critical domains of autonomous robotics and advanced image processing. His most impactful contribution lies in mobile robot path planning, where he authored a comprehensive review that systematically categorizes the representation, models, algorithms, and constraints shaping this field. This work, garnering 10 citations, serves as a foundational resource for researchers tackling the complex challenge of enabling intelligent autonomous robots to navigate and explore dynamic environments. Liang’s analysis synthesizes a vast array of methods, from classical to emerging, providing a structured roadmap for advancing robotic autonomy. In parallel, he explores the frontiers of computational imaging, notably through his work on multiband image fusion using total generalized variation regularization. This approach enhances the quality and detail of fused images from multiple spectral bands, with applications in remote sensing and medical imaging. Though early in its citation impact, this research demonstrates Liang’s versatility and commitment to solving real-world problems through rigorous mathematical modeling. His dual focus on robotic intelligence and image reconstruction marks him as a thoughtful contributor to both autonomous systems and sensor data analysis.