2 citations · 5 across the 3 of their papers we have counts for
6 papers
MAVRL: Learn to Fly in Cluttered Environments with Varying Speed
Hang Yu, Christophe De Wagter, Guido C. H. E de Croon
Many existing obstacle avoidance algorithms overlook the crucial balance between safety and agility, especially in environments of varying complexity. In our study, we introduce an…
End-to-end Reinforcement Learning for Time-Optimal Quadcopter Flight
Robin Ferede, Christophe De Wagter, Dario Izzo +1
Aggressive time-optimal control of quadcopters poses a significant challenge in the field of robotics. The state-of-the-art approach leverages reinforcement learning (RL) to train…
Optimality Principles in Spacecraft Neural Guidance and Control
Dario Izzo, Emmanuel Blazquez, Robin Ferede +3
Spacecraft and drones aimed at exploring our solar system are designed to operate in conditions where the smart use of onboard resources is vital to the success or failure of the m…
Guidance & Control Networks for Time-Optimal Quadcopter Flight
Sebastien Origer, Christophe De Wagter, Robin Ferede +2
Reaching fast and autonomous flight requires computationally efficient and robust algorithms. To this end, we train Guidance & Control Networks to approximate optimal control polic…
Neuromorphic Control using Input-Weighted Threshold Adaptation
Stein Stroobants, Christophe De Wagter, Guido C. H. E. de Croon
Neuromorphic processing promises high energy efficiency and rapid response rates, making it an ideal candidate for achieving autonomous flight of resource-constrained robots. It wi…
End-to-end Neural Network Based Quadcopter control
Robin Ferede, Guido C. H. E. de Croon, Christophe De Wagter +1
Developing optimal controllers for aggressive high-speed quadcopter flight poses significant challenges in robotics. Recent trends in the field involve utilizing neural network con…