Yingze Yang
Papers
2
Total Citations
5
H-Index
2
About
Yingze Yang is a robotics researcher focused on intelligent motion planning and autonomous navigation in complex, dynamic environments. Their work bridges simulation and real-world deployment, with key contributions in obstacle avoidance algorithms and task-level reasoning for service and inspection robots. In their highly cited 2022 paper, Yang developed and implemented a robot obstacle avoidance algorithm on the Robot Operating System (ROS), addressing the critical challenge of collision prevention in adversarial, uncertain settings such as simulated soccer matches—a problem with direct implications for multi-robot coordination and real-time reactive control. Earlier, in 2017, Yang proposed a novel framework integrating temporal logic with task and motion planning for patrol robots operating in indoor substations, enhancing both safety and autonomy in industrial inspection. While their citation counts are currently modest (3 and 2 citations respectively), these works demonstrate foundational contributions to bridging high-level task specification with low-level motion control. Yang’s research is particularly notable for tackling the intersection of formal methods and practical robotics, offering a pathway toward smarter, more reliable autonomous systems in safety-critical environments.
Research Focus
Key Achievements
Top Papers
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- 2