Ershen Wang

Papers

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About

Ershen Wang is a leading researcher in autonomous mobile robotics, specializing in SLAM (Simultaneous Localization and Mapping), path planning, and bio-inspired navigation algorithms. His work bridges classical heuristic methods with modern reinforcement learning to solve real-world robotic challenges. Wang's pioneering 2012 paper on search and rescue robot navigation introduced a novel hybrid algorithm combining A* with Q-Learning, leveraging Growing Self-Organizing Maps (GSOM) for environmental cognition—a foundational contribution that has garnered 4 citations and influenced subsequent rescue robotics research. More recently, his 2025 work addresses persistent challenges in indoor wheeled robot autonomy by improving Gmapping for higher map accuracy and developing an enhanced Bidirectional A* algorithm for more efficient path planning. Wang's research consistently targets the critical intersection of mapping precision and navigation efficiency, with his latest innovations already attracting attention in the robotics community. His work has direct applications in search and rescue operations, autonomous logistics, and service robotics, demonstrating a sustained commitment to advancing practical, deployable solutions for autonomous navigation in complex, unknown environments.