Jae Bum Jung
Papers
1
Total Citations
6
H-Index
1
About
Jae Bum Jung is a robotics researcher whose work centers on multi-robot coordination, motion planning, and optimal control under physical constraints. His most cited paper, "Pareto optimal multi-robot coordination with acceleration constraints" (2008, 6 citations), introduces a novel framework for programming collision-free motions among robots sharing a common environment. Rather than imposing a single global objective, Jung employs Pareto optimality to respect each robot’s individual goal, while accounting for realistic acceleration limits and roadmap-based configuration spaces. This approach offers a principled way to balance efficiency and safety in decentralized multi-agent systems. Although his citation count is modest, Jung’s contribution is notable for addressing a fundamental tension in robotics: how to achieve optimal coordination without sacrificing each agent’s autonomy. His work is particularly relevant for applications in automated warehouses, drone swarms, and autonomous vehicle fleets, where robots must navigate shared spaces without centralized control. Jung’s research continues to influence the design of scalable, constraint-aware coordination algorithms.
Research Focus
Key Achievements
Top Papers
- 1Pareto optimal multi-robot coordination with acceleration constraints6 citations · 2008