Ying-Ze Mu

Inner Mongolia University of Technology, Mongolian University of Science and Technology

Papers

Total Citations

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About

Ying-Ze Mu is a researcher focused on advancing autonomous navigation and path planning for mobile robots and automated guided vehicles (AGVs). Their work centers on three core challenges: efficient path optimization, robust simultaneous localization and mapping (SLAM), and vision-based navigation. Mu’s most cited paper, "Path planning of mobile robots based on an improved A* algorithm" (2020, 19 citations), enhances the classic A* search by integrating Dijkstra’s algorithm and varied heuristic functions to generate optimal paths in static environments, directly improving AGV navigation efficiency. A second key contribution, "An Improved Particle Filter SLAM Algorithm for AGVs" (2020, 6 citations), tackles the computational bottlenecks of traditional particle filters, proposing an IPF-SLAM method that reduces computational expense and boosts real-time positioning accuracy. Additionally, Mu’s work on "Research on Navigation and Path Planning of Mobile Robot Based on Vision Sensor" (2020, 4 citations) employs an improved ORB-SLAM2 algorithm to enable autonomous mapping and obstacle avoidance in unknown environments. With a cumulative impact of nearly 30 citations across these foundational papers, Mu’s research provides practical, scalable solutions for industrial robotics and autonomous systems, bridging algorithmic theory with real-world deployment.