Kaidong Yang
Papers
1
Total Citations
10
H-Index
1
About
Kaidong Yang is a rising researcher in intelligent robotics and optimization, whose work centers on solving complex path planning problems for time-critical rescue operations. His most-cited study, "An Improved Iterated Greedy Algorithm for Solving Rescue Robot Path Planning Problem with Limited Survival Time" (2024), addresses a critical gap in post-disaster robotics by modeling rescue missions as a variant of the Traveling Salesman Problem (TSP) with survival time constraints. This innovative approach has already garnered 10 citations, reflecting its immediate relevance to both robotics and operations research communities. Yang’s major contribution lies in developing an enhanced iterated greedy algorithm that balances computational efficiency with optimality, enabling mobile robots to navigate hazardous environments and reach victims faster when every second counts. His work bridges theoretical optimization and practical deployment, offering a scalable framework for autonomous emergency response systems. As an early-career scholar, Yang’s research holds promise for advancing disaster robotics, with potential applications in search-and-rescue missions, autonomous navigation, and humanitarian technology. His growing citation record signals a researcher poised to make lasting impacts in intelligent systems and life-saving robotics.
Research Focus
Key Achievements
Top Papers
- 1