Papers
13
Total Citations
330
H-Index
8
About
Zhuozhu Jian is a robotics researcher whose work sits at the intersection of autonomous navigation, safety-critical control, and human-robot interaction. His research has made notable contributions to mobile robot autonomy across challenging real-world environments, from structured indoor spaces to rugged outdoor terrain. Jian's most influential work introduces a Dynamic Control Barrier Function-based Model Predictive Control framework for obstacle avoidance, leveraging LiDAR point clouds and DBSCAN clustering to enable safe navigation around both static and dynamic obstacles — a contribution that has garnered 117 citations and established him as a leading voice in safety-aware robot motion planning. His PUTN framework (75 citations) addresses the demanding problem of autonomous ground robot navigation over uneven, unstructured terrain through plane-fitting techniques. Beyond autonomous systems, Jian has demonstrated a thoughtful human-centered perspective, developing a comfort-aware quadruped guidance robot for the visually impaired (45 citations) that prioritizes user experience alongside obstacle avoidance. His broader portfolio spans vegetated terrain navigation, dynamic object removal for map construction, articulated tracked robot trajectory optimization, and multi-robot cooperative exploration. With over 300 cumulative citations and growing, Jian represents an emerging force in field robotics research.
Research Focus
Key Achievements
Top Papers
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- 2PUTN: A Plane-fitting based Uneven Terrain Navigation Framework75 citations · 2022
- 3Quadruped Guidance Robot for the Visually Impaired: A Comfort-Based Approach45 citations · 2023
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- 10PUTN: A Plane-fitting based Uneven Terrain Navigation Framework3 citations · 2022