Papers
13
Total Citations
175
H-Index
6
About
Frank Hoeller is a robotics researcher whose work spans human-robot interaction, multi-robot systems, and autonomous exploration for disaster response. His most influential contribution is developing methods for mobile robots to safely accompany walking persons through crowded environments, using motion prediction and probabilistic roadmaps—work that has garnered 45 citations and addresses a critical challenge in human-robot coexistence. Hoeller also made significant advances in multi-robot coordination, creating the ROS Multimaster Extension (35 citations) to simplify deployment of multi-robot systems and the RoSe framework (22 citations) for reliable multicast communication over unreliable networks. His component-based approach to visual person tracking from mobile platforms (32 citations) introduced a biologically inspired cognitive observation model that optimally separates tracked persons from backgrounds. In recent years, Hoeller has focused on autonomous exploration in post-disaster scenarios, developing behavior-tree-based systems for CBRNE hazard detection and 6D SLAM algorithms for robotic mapping. His work on liquid exploration online (LEO) addresses the specific challenges of skid-steer tracked robots in unknown environments. Through contributions to the Eurathlon 2013 competition and collaborations with German military forces, Hoeller has demonstrated the real-world applicability of his research, bridging the gap between laboratory algorithms and field-deployable robotic systems.
Research Focus
Key Achievements
Top Papers
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- 3A Component-Based Approach to Visual Person Tracking from a Mobile Platform32 citations · 2009
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- 8CBRNE hazard detection with an Unmanned vehicle5 citations · 2009
- 9Visual Person Tracking Using a Cognitive Observation Model5 citations · 2009
- 10LEO: Liquid Exploration Online3 citations · 2019