8 papers · 1 filter
SkyDreamer: Interpretable End-to-End Vision-Based Drone Racing with Model-Based Reinforcement Learning
Aderik Verraest, Stavrow Bahnam, Robin Ferede +2
Autonomous drone racing (ADR) systems have recently achieved champion-level performance, yet remain highly specific to drone racing. While end-to-end vision-based methods promise b…
Depth Transfer: Learning to See Like a Simulator for Real-World Drone Navigation
Hang Yu, Christophe De Wagter, Guido C. H. E de Croon
Sim-to-real transfer is a fundamental challenge in robot reinforcement learning. Discrepancies between simulation and reality can significantly impair policy performance, especiall…
Self-Supervised Monocular Visual Drone Model Identification through Improved Occlusion Handling
Stavrow A. Bahnam, Christophe De Wagter, Guido C. H. E. de Croon
Ego-motion estimation is vital for drones when flying in GPS-denied environments. Vision-based methods struggle when flight speed increases and close-by objects lead to difficult v…
One Net to Rule Them All: Domain Randomization in Quadcopter Racing Across Different Platforms
Robin Ferede, Till Blaha, Erin Lucassen +2
In high-speed quadcopter racing, finding a single controller that works well across different platforms remains challenging. This work presents the first neural network controller…
Multi-objective Evolution of Drone Morphology
Elijah H. W. Ang, Christophe De Wagter, Guido C. H. E. de Croon
The design of multicopter drones has remained almost the same since its inception. While conventional designs, such as the quadcopter, work well in many cases, they may not be opti…
Self-supervised Monocular Multi-robot Relative Localization with Efficient Deep Neural Networks
Shushuai Li, Christophe De Wagter, Guido C. H. E. de Croon
Relative localization is an important ability for multiple robots to perform cooperative tasks in GPS-denied environment. This paper presents a novel autonomous positioning framewo…