180 citations · 692 across the 42 of their papers we have counts for
35 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…
Whisker-based Tactile Flight for Tiny Drones
Chaoxiang Ye, Guido de Croon, Salua Hamaza
Tiny flying robots hold great potential for search-and-rescue, safety inspections, and environmental monitoring, but their small size and limited computational resources constrain…
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…
Cooperative Relative Localization in MAV Swarms with Ultra-wideband Ranging
Changrui Liu, Sven U. Pfeiffer, Guido C. H. E. de Croon
Relative localization (RL) is essential for the successful operation of micro air vehicle (MAV) swarms. Achieving accurate 3-D RL in infrastructure-free and GPS-denied environments…
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…
Evolving Spiking Neural Networks to Mimic PID Control for Autonomous Blimps
Tim Burgers, Stein Stroobants, Guido de Croon
In recent years, Artificial Neural Networks (ANN) have become a standard in robotic control. However, a significant drawback of large-scale ANNs is their increased power consumptio…
AOSoar: Autonomous Orographic Soaring of a Micro Air Vehicle
Sunyou Hwang, Bart D. W. Remes, Guido C. H. E. de Croon
Utilizing wind hovering techniques of soaring birds can save energy expenditure and improve the flight endurance of micro air vehicles (MAVs). Here, we present a novel method for f…
Autonomous Control for Orographic Soaring of Fixed-Wing UAVs
Tom Suys, Sunyou Hwang, Guido C. H. E. de Croon +1
We present a novel controller for fixed-wing UAVs that enables autonomous soaring in an orographic wind field, extending flight endurance. Our method identifies soaring regions and…
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 computing for attitude estimation onboard quadrotors
Stein Stroobants, Julien Dupeyroux, Guido C. H. E. de Croon
Compelling evidence has been given for the high energy efficiency and update rates of neuromorphic processors, with performance beyond what standard Von Neumann architectures can a…
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…
AvoidBench: A high-fidelity vision-based obstacle avoidance benchmarking suite for multi-rotors
Hang Yu, Guido C. H. E de Croon, Christophe De Wagter
Obstacle avoidance is an essential topic in the field of autonomous drone research. When choosing an avoidance algorithm, many different options are available, each with their adva…
CUAHN-VIO: Content-and-Uncertainty-Aware Homography Network for Visual-Inertial Odometry
Yingfu Xu, Guido C. H. E. de Croon
Learning-based visual ego-motion estimation is promising yet not ready for navigating agile mobile robots in the real world. In this article, we propose CUAHN-VIO, a robust and eff…
Adaptive Risk-Tendency: Nano Drone Navigation in Cluttered Environments with Distributional Reinforcement Learning
Cheng Liu, Erik-Jan van Kampen, Guido C. H. E. de Croon
Enabling the capability of assessing risk and making risk-aware decisions is essential to applying reinforcement learning to safety-critical robots like drones. In this paper, we i…
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…
Onboard Ranging-based Relative Localization and Stability for Lightweight Aerial Swarms
Shushuai Li, Feng Shan, Jiangpeng Liu +3
Lightweight aerial swarms have potential applications in scenarios where larger drones fail to operate efficiently. The primary foundation for lightweight aerial swarms is efficien…
Evolved Neuromorphic Control for High Speed Divergence-based Landings of MAVs
J. J. Hagenaars, F. Paredes-Vallés, S. M. Bohté +1
Flying insects are capable of vision-based navigation in cluttered environments, reliably avoiding obstacles through fast and agile maneuvers, while being very efficient in the pro…
Aggressive Online Control of a Quadrotor via Deep Network Representations of Optimality Principles
Shuo Li, Ekin Ozturk, Christophe De Wagter +2
Optimal control holds great potential to improve a variety of robotic applications. The application of optimal control on-board limited platforms has been severely hindered by the…