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About

Yanduo Zhang is a robotics researcher whose work spans robot path planning, simultaneous localization and mapping (SLAM), and bio-inspired control systems. Zhang’s most cited paper, “The Application of Liquid State Machines in Robot Path Planning” (2009, 16 citations), introduced spiking neural networks and the Parallel Delta Rule to solve optimization problems in robot navigation, demonstrating how liquid state machines can effectively guide autonomous movement. In dynamic environments, Zhang advanced semantic SLAM with “PMDS-SLAM: Probability Mesh Enhanced Semantic SLAM in Dynamic Environments” (2020, 8 citations), addressing the critical limitation of static-scene assumptions in traditional vSLAM systems. More recently, Zhang has explored adversarial security in robotics, proposing the RMS-FGSM attack algorithm (2024) to expose vulnerabilities in robot vision models. Additional contributions include CPG-based gait planning for humanoid robots, cable-driven rehabilitation robots for gait training, and cooperative strategies for multi-agent systems using artificial immune systems. Zhang’s work on SLAM loop closure detection and air-ground collaborative path planning further underscores a commitment to robust, real-world robotic perception and navigation. With a career spanning foundational neural network methods to cutting-edge security challenges, Zhang continues to shape how robots perceive, plan, and act in complex environments.