Peihan Li
Papers
1
Total Citations
5
H-Index
1
About
Peihan Li is a rising researcher in multi-robot systems and autonomous decision-making, with a focus on the intersection of sensing, control, and optimization. Her most cited work, "Assignment Algorithms for Multi-Robot Multi-Target Tracking with Sufficient and Limited Sensing Capability" (2023, 5 citations), addresses a critical challenge in robotics: how to optimally assign robots with varying sensing abilities to track multiple targets while minimizing uncertainty in target states. Li’s key contribution lies in developing efficient assignment algorithms that balance tracking quality against real-world constraints, such as limited sensor range or communication bandwidth. This work is foundational for applications like search-and-rescue, environmental monitoring, and autonomous surveillance, where robot teams must operate under tight resource limitations. Though early in her career, Li’s research has already garnered attention for its practical relevance, bridging theoretical optimization with deployable multi-robot coordination. Her achievements include advancing the understanding of how sensing capability directly impacts team performance, offering a framework that can scale to larger robot networks. For students and researchers, Li’s work exemplifies how algorithmic thinking can solve tangible problems in robotics, making her a promising voice in the field of distributed autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1