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About

Ruizhe Yang is a forward-thinking researcher whose work sits at the intersection of robotics, logistics automation, and safety-critical systems engineering. Yang’s most cited contribution, “Path Planning of Rail-Mounted Logistics Robots Based on the Improved Dijkstra Algorithm” (2023), has garnered 51 citations and addresses the pressing need for efficient material distribution in modern factories. By refining a classic algorithm, Yang has provided a practical solution for optimizing the routes of rail-mounted robots in large-scale production environments, directly supporting the ongoing digital transformation of manufacturing. Beyond logistics, Yang has made notable strides in the assurance of safety-critical systems. The paper “ACCESS: Assurance Case Centric Engineering of Safety–critical Systems” (2024, 10 citations) introduces a model-based approach to creating and evaluating assurance cases—a crucial step toward more rigorous and automated verification of system properties like safety and security. This work is particularly relevant as industries increasingly rely on complex, autonomous systems. Yang’s research also ventures into the emerging field of soft robotics. The study “Liquid metal droplet motion transferred from an alkaline solution by a robot arm” (2022, 8 citations) explores the manipulation of gallium-based liquid metals, offering a novel method for enabling autonomous soft robots to operate outside of restrictive liquid environments. This interdisciplinary approach highlights Yang’s ability to bridge fundamental materials science with practical robotic applications, marking a researcher whose work is both technically rigorous and industrially impactful.