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About

Jiahao Wan is a robotics researcher whose work focuses on the intersection of intelligent control, path planning, and dynamic obstacle avoidance for mobile and industrial robots. His key research areas include robot dynamics modeling, stiffness optimization, and autonomous navigation in challenging environments. Wan’s most cited paper, "A Path-Planning Method for Wall Surface Inspection Robot Based on Improved Genetic Algorithm" (2022, 17 citations), addresses the critical problem of GPS-denied environments, proposing a genetic algorithm-based solution to enhance positioning accuracy and safety during wall inspections. He further advanced robot control with his work on "Robot hybrid inverse dynamics model compensation method based on the BLL residual prediction algorithm" (2024, 6 citations), which improves motion precision by compensating for unmodeled dynamics residuals. Wan has also contributed to aeronautical manufacturing with "Stiffness performance optimization method of drilling robot based on QPSO algorithm" (2024, 2 citations), optimizing robot stiffness for high-quality processing tasks. His most recent work, "TAP—time-aligned prediction: an improved dynamic obstacle avoidance method for mobile robots" (2025, 1 citation), introduces a novel time-aligned prediction approach for safer navigation. With a growing citation record and a focus on practical, real-world robotics challenges, Wan’s research is steadily gaining recognition for its impact on autonomous systems and industrial automation.