Yuanhang Zhang
Papers
1
Total Citations
2
H-Index
1
About
Yuanhang Zhang is a rising researcher in artificial intelligence and multi-agent systems, with a primary focus on Multi-Agent Combinatorial Path Finding (MCPF). His work addresses the complex challenge of coordinating multiple agents to navigate collision-free paths while visiting intermediate target locations and minimizing total arrival times. Zhang’s 2024 extended abstract, “A Short Summary of Multi-Agent Combinatorial Path Finding with Heterogeneous Task Duration,” introduces novel approaches to handling varying task durations among agents—a critical advancement for real-world applications like warehouse robotics and autonomous vehicle coordination. Though early in his career, with his most-cited paper already garnering 2 citations, Zhang’s contributions are laying important groundwork for scalable, efficient multi-agent coordination. His research bridges theoretical pathfinding algorithms with practical constraints, offering solutions that account for heterogeneous agent capabilities and task requirements. As the field of multi-agent systems continues to grow, Zhang’s work on MCPF with heterogeneous task durations positions him as a promising voice in developing more adaptive and realistic coordination strategies for autonomous systems.
Research Focus
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Top Papers
- 1