Shengming Zhang

Papers

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About

Shengming Zhang is a robotics researcher whose work focuses on enabling mobile robots to navigate and interact intelligently in human-centered environments. His primary research areas include human-robot interaction, target tracking, and autonomous navigation in structured indoor spaces. Zhang’s most influential contribution, "Laser-Based Intersection-Aware Human Following With a Mobile Robot in Indoor Environments" (2018), addresses the critical challenge of maintaining visual contact with a moving person at corridor intersections—a scenario where full occlusion by walls can cause the robot to lose its target. This work, with 37 citations, proposes a laser-based solution that anticipates human motion patterns to prevent permanent target loss, significantly improving the reliability of human-following robots in real-world settings. In his more recent work, "Moving Target Tracking with a Mobile Robot based on Modified Social Force Model" (2021), Zhang extends his research to crowded environments, applying an improved social force model to help robots navigate while tracking a target without disrupting nearby pedestrians. Though newer, this work demonstrates his commitment to developing socially-aware robots that can coexist and collaborate with humans. Zhang’s research is particularly valuable for advancing service and assistive robotics in hospitals, airports, and offices.