Papers
29
Total Citations
475
H-Index
14
About
Tingjun Lei is a prolific researcher specializing in autonomous robotics, multi-robot systems, and intelligent path planning algorithms. His work sits at the intersection of bio-inspired computation, convex optimization, and autonomous navigation, addressing some of the most challenging problems in modern robotics. Lei's most significant contributions center on developing novel frameworks for robot path planning and task allocation. His graph-based approaches combined with bio-inspired algorithms — including ant colony optimization (ACO) and related techniques — have produced measurable improvements in navigational efficiency and solution optimality, earning his papers over 50 and 36 citations respectively. His 2023 convex optimization framework for multi-robot task allocation tackles NP-hard team deployment problems with elegant mathematical rigor, while his multi-UAV collision avoidance framework (37 citations) demonstrates real-world applicability in aerial robotics. Beyond pure planning algorithms, Lei has extended his research into image-based localization, human-autonomy teaming, and informative path planning for practical domains such as precision agriculture and poultry barn monitoring. His tree-search and sampling-based methods further highlight his versatility. With over 300 cumulative citations across a focused body of work, Lei has established himself as an influential voice advancing autonomous robotic intelligence for complex, real-world environments.
Research Focus
Key Achievements
Top Papers
- 1Graph-based robot optimal path planning with bio-inspired algorithms50 citations · 2023
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- 4A Graph-based Ant-like Approach to Optimal Path Planning36 citations · 2020
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- 6Informed sampling space driven robot informative path planning29 citations · 2024
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- 10Variable Speed Robot Navigation by an ACO Approach20 citations · 2019