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About

Joonwoo Ahn is a robotics researcher whose work spans autonomous navigation, human-robot interaction, and mechanism design. His most impactful contribution is the **TargetTree-RRT*** algorithm (54 citations), a continuous-curvature path planner that dramatically reduces computation time for autonomous parking in tight, complex environments—a critical advance for self-driving vehicles. Ahn also developed a **formation-based tracking method** for human-following robots (8 citations), shifting from simple rear-following to side-by-side formations that improve user comfort and natural interaction. Earlier in his career, he designed an **optimal in-pipe cleaning robot** (10 citations) for Korea’s Garbage Automatic Collection Facilities, addressing a growing urban infrastructure need. His team’s participation in the **DARPA Robotics Challenge Finals 2015** (5 citations) demonstrated robust control strategies under extreme conditions, while his more recent work on **Data Aggregation (DAgger) with adversarial agents** (2024) tackles dynamic, adversarial environments for safer robot learning. With over 80 total citations, Ahn’s research consistently bridges theoretical planning algorithms with practical, real-world deployment challenges—from parking lots to sewer pipes—making him a versatile contributor to field robotics and autonomous systems.