Paul Tan
Papers
1
Total Citations
4
H-Index
1
About
Paul Tan is a robotics researcher whose work centers on multi-robot coordination, perception, and control in challenging, real-world environments. His key contributions address the critical problem of maintaining reliable formation control when sensor data is unreliable. In his most cited work, "Probabilistic trajectory estimation based leader following for multi-robot systems" (2016), Tan tackles the multi-robot leader-following problem under frequent vision detection dropouts—a common issue in structured environments where a target robot may move out of view. By developing a probabilistic trajectory estimation framework, he enables follower robots to robustly infer the leader’s path even during sensor outages, significantly enhancing system resilience. This research has garnered attention in the field, with 4 citations, and lays important groundwork for deploying multi-robot teams in logistics, warehouse automation, and search-and-rescue operations. Tan’s work exemplifies a practical, estimation-driven approach to overcoming perception limitations, making him a notable contributor to the advancement of autonomous multi-agent systems.
Research Focus
Key Achievements
Top Papers
- 1