Papers
2
Total Citations
12
H-Index
2
About
Olivier Pauplin is a researcher whose work lies at the intersection of evolutionary computation and mobile robotics, with a primary focus on real-time obstacle detection and avoidance. His major contributions center on the innovative application of the "Parisian approach"—a form of artificial evolution that decomposes a robot’s environmental representation into a large number of simple, co-evolving primitives. This method allows for efficient stereo image analysis, enabling robots to navigate complex environments dynamically. Pauplin’s most cited work, "Evolutionary Optimisation for Obstacle Detection and Avoidance in Mobile Robotics" (2005), has garnered 10 citations, demonstrating its foundational role in the field. A related paper, "Applying Evolutionary Optimisation to Robot Obstacle Avoidance" (2005), further refines these techniques, though with fewer citations. While his citation counts are modest, Pauplin’s research is notable for pioneering the use of evolutionary algorithms to solve practical, real-time challenges in autonomous navigation—a testament to his forward-thinking approach in an era when such methods were still emerging. His work remains a valuable reference for those exploring bio-inspired solutions in robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2Applying Evolutionary Optimisation to Robot Obstacle Avoidance2 citations · 2005