Hao Lang

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About

Hao Lang is a researcher specializing in multi-robot systems, with a core focus on formation control and localization in GPS-denied environments. His work addresses fundamental challenges in enabling teams of robots to coordinate using only limited sensory data. Lang’s major contributions center on bearing-only leader-follower formation control, where he has rigorously analyzed the nonlinear observability properties of these systems. His 2019 study on the topic, cited 5 times, demonstrated that a leader robot system becomes completely observable when it can detect two distinct landmarks, a critical insight for reliable decentralized control. Complementing this, his 2015 work on adaptive particle filters for indoor robot localization (8 citations) provides a robust framework for robots to estimate their position in cluttered, feature-rich spaces. By bridging theoretical observability analysis with practical filtering algorithms, Lang has advanced the reliability of autonomous robot teams. His research is particularly impactful for applications in search-and-rescue, warehouse automation, and environmental monitoring, where precise coordination without external infrastructure is essential.