Papers
3
Total Citations
34
H-Index
2
About
Liang Ren is a researcher focused on advancing multi-robot systems, with key contributions in task allocation, collision avoidance, and collaborative perception. His most cited work (2017, 25 citations) introduces a hierarchical coalition framework for large-scale robotic teams, optimizing task allocation under resource constraints through a bottom-up resource vector update process—a scalable solution for complex coordination. Ren also addresses the critical challenge of collision avoidance in dense, unknown environments with a laser-based approach (2018, 7 citations), enabling safe robot aggregation in tight spaces. Further, he tackles vision-based target recognition and tracking (2019, 2 citations) by proposing a two-level adaptive method that leverages multi-robot collaborative perception to overcome limitations of single-view systems, such as susceptibility to poor illumination and occlusion. These contributions demonstrate Ren’s impact on enhancing autonomy and efficiency in multi-robot systems, with applications ranging from industrial automation to search-and-rescue. His work is particularly notable for its practical, hierarchical solutions to real-world constraints, making him a valuable voice in the field of distributed robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3