Papers
1
Total Citations
3
H-Index
1
About
Renaud Poncelet is a robotics researcher whose work centers on motion planning in highly dynamic and unpredictable environments. His key contributions lie in developing frameworks that enable robots to navigate safely and efficiently among moving obstacles, a critical challenge for autonomous systems in real-world settings. His most notable work, "RIS: A Framework for Motion Planning Among Highly Dynamic Obstacles" (2018), introduces the concept of reachable interaction sets—a novel method for integrating a robot's future interaction zones directly into its planning algorithms. This approach allows robots to anticipate and adapt to rapidly changing surroundings, bridging the gap between traditional quasi-static path planning and the demands of truly dynamic scenarios. While his citation count of 3 reflects the specialized and emerging nature of his research area, the work is foundational for advancing autonomous navigation in crowded or unpredictable spaces, such as urban environments or disaster zones. Poncelet’s framework offers a promising path toward more responsive and intelligent robotic systems, making his contributions valuable for students and researchers tackling motion planning in complex, real-time applications.
Research Focus
Key Achievements
Top Papers
- 1RIS: A Framework for Motion Planning Among Highly Dynamic Obstacles3 citations · 2018