Papers
11
Total Citations
140
H-Index
7
About
Sergi Molina is a robotics researcher whose work spans autonomous mobile robots, human-aware navigation, and agricultural robotics. His research is particularly distinguished by its focus on spatiotemporal modeling — developing methods that allow robots to understand, predict, and adapt to the dynamic patterns of people and environments over extended time periods. Among his most influential contributions is his work on rhythmic flow pattern modeling (2018, 32 citations), which introduced probabilistic, time-dependent maps capable of capturing long-term pedestrian behavior. This line of inquiry extends through his pedestrian flow models for service robots (2019) and his robotic exploration methods for learning human motion patterns (2021), collectively establishing him as a leading voice in human-centric robot navigation. His research on safe industrial robot navigation through the ILIAD Safety Stack further demonstrates the real-world applicability of his methods in intralogistics environments. More recently, Molina has made significant strides in agricultural robotics, contributing the Bacchus Long-Term dataset (2023, 24 citations) and advancing adaptive localization and grape harvesting systems. These efforts reflect a broader commitment to deploying robust, intelligent robots in complex, ever-changing field conditions — work that bridges foundational modeling research with practical, high-impact autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1Modelling and Predicting Rhythmic Flow Patterns in Dynamic Environments32 citations · 2018
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- 3Robotic Exploration for Learning Human Motion Patterns18 citations · 2021
- 4Time-varying Pedestrian Flow Models for Service Robots17 citations · 2019
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- 9Modelling and Predicting Rhythmic Flow Patterns in Dynamic Environments6 citations · 2018
- 10