K. Baumgartner
Papers
1
Total Citations
46
H-Index
1
About
K. Baumgartner is a leading researcher in mobile sensor networks and multi-agent pursuit-evasion dynamics, with a focus on geometric optimization for autonomous systems. Their most cited work, "A Geometric Optimization Approach to Detecting and Intercepting Dynamic Targets Using a Mobile Sensor Network" (2009, 46 citations), introduces a novel methodology for deploying mobile sensor networks to detect and capture moving targets in planar environments. This work draws a compelling analogy to the Marco Polo game, where a single pursuer must locate and intercept multiple evasive targets using only limited sensory information. Baumgartner’s contributions lie in developing rigorous geometric frameworks that enable efficient coordination among distributed agents, addressing fundamental challenges in target tracking, interception, and network reconfiguration. Their research has significant implications for robotics, surveillance, and autonomous search-and-rescue operations. By bridging theoretical optimization with practical sensor network design, Baumgartner has advanced the field of cooperative control, inspiring subsequent work on dynamic target capture and multi-robot systems. Their 2009 paper remains a foundational reference for researchers exploring pursuit-evasion problems and mobile sensor deployment strategies.
Research Focus
Key Achievements
Top Papers
- 1