Haisheng Li

Beijing Technology and Business University, University of Electronic Science and Technology of China

Papers

Total Citations

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About

Haisheng Li is a rising researcher at the forefront of optimization theory, robotics, and data-driven navigation. His work spans three key areas: time-variant constrained quadratic programming, cable-driven parallel robots, and inertial navigation systems. Li’s major contribution includes the development of the activated variable parameter gradient-based neural network (AVPGNN), a novel model that efficiently solves time-varying constrained quadratic programming problems with enhanced convergence and robustness. In robotics, he advanced the workspace analysis and optimal design of translational cable-driven parallel robots (CDPRs) with passive springs, addressing the complexity and cost of redundant cable systems for high-speed applications. His most cited paper, “An activated variable parameter gradient‐based neural network for time‐variant constrained quadratic programming and its applications,” has garnered 27 citations, while his CDPR work has 26 citations, reflecting significant impact in optimization and robotics communities. Most recently, Li introduced VANE-IN, a velocity auto-encoder for inertial navigation, achieving 3 citations in 2024. This work pushes boundaries in data-driven inertial navigation for mobile computing, augmented reality, and robotics. With a growing portfolio and innovative solutions, Li is establishing himself as a versatile contributor to applied mathematics, mechanical design, and sensor-based localization.