Papers
6
Total Citations
129
H-Index
5
About
Alex Goldhoorn’s research sits at the intersection of autonomous robotics, human-robot interaction, and real-world navigation, with a particular focus on enabling robots to find, track, and follow people in complex urban environments. His most influential work, “Searching and tracking people with cooperative mobile robots” (2017, 46 citations), demonstrates how multiple robots can collaborate to locate individuals in cluttered settings. Goldhoorn is perhaps best known for developing the Continuous Real-time POMCP method and its extension, the Adaptive Highest Belief Continuous Real-time POMCP follower, which allow humanoid service robots to find and follow people in real time—a breakthrough detailed in his 2014 study (29 citations). He also contributed to robot homing and navigation, notably through the “Combining Invariant Features and the ALV Homing Method” (2011, 26 citations) and the “Average Landmark Vector Method” (2007, 7 citations). His work on human-robot hide-and-seek (2013) adds a playful yet rigorous dimension to understanding robot perception and decision-making. With over 100 total citations, Goldhoorn’s research has advanced the practical deployment of service robots in dynamic, human-centered spaces.
Research Focus
Key Achievements
Top Papers
- 1Searching and tracking people with cooperative mobile robots46 citations · 2017
- 2
- 3
- 4
- 5Using the Average Landmark Vector Method for Robot Homing7 citations · 2007
- 6