Pascal Goldschmid
Papers
2
Total Citations
9
H-Index
1
About
Pascal Goldschmid is a rising researcher in autonomous aerial robotics, with a focus on reinforcement learning (RL) for real-world control systems. His work addresses critical challenges in drone and blimp autonomy, particularly in long-duration operations and dynamic environments. Goldschmid’s most-cited paper, “Autonomous Blimp Control using Deep Reinforcement Learning” (2021, 8 citations), pioneers RL-based navigation for energy-efficient, silent, and safe aerial robots—ideal for persistent surveillance or monitoring tasks. More recently, his 2024 study on “Reinforcement learning based autonomous multi-rotor landing on moving platforms” tackles the battery limitation of multi-rotor UAVs by enabling autonomous landing on 2D moving platforms, a key step toward extended flight endurance and data offloading. This work bridges classical control limitations with modern RL, offering a scalable solution for logistics, search-and-rescue, and infrastructure inspection. Though early in his career, Goldschmid’s contributions demonstrate a clear trajectory toward practical, RL-driven autonomy for aerial systems, with potential to reshape how drones and blimps operate in complex, real-world scenarios.
Research Focus
Key Achievements
Top Papers
- 1Autonomous Blimp Control using Deep Reinforcement Learning8 citations · 2021
- 2