7 papers
E2E-Fly: An Integrated Training-to-Deployment System for End-to-End Quadrotor Autonomy
Fangyu Sun, Fanxing Li, Linzuo Zhang +5
Training and transferring learning-based policies for quadrotors from simulation to reality remains challenging due to inefficient visual rendering, physical modeling inaccuracies,…
Vector Field Augmented Differentiable Policy Learning for Vision-Based Drone Racing
Yang Su, Feng Yu, Yu Hu +4
Autonomous drone racing in complex environments requires agile, high-speed flight while maintaining reliable obstacle avoidance. Differentiable-physics-based policy learning has re…
AI-IO: An Aerodynamics-Inspired Real-Time Inertial Odometry for Quadrotors
Jiahao Cui, Feng Yu, Linzuo Zhang +2
Inertial Odometry (IO) has gained attention in quadrotor applications due to its sole reliance on inertial measurement units (IMUs), attributed to its lightweight design, low cost,…
Curriculum Reinforcement Learning for Quadrotor Racing with Random Obstacles
Fangyu Sun, Fanxing Li, Yu Hu +4
Autonomous drone racing has attracted increasing interest as a research topic for exploring the limits of agile flight. However, existing studies primarily focus on obstacle-free r…
Mastering Diverse, Unknown, and Cluttered Tracks for Robust Vision-Based Drone Racing
Feng Yu, Yu Hu, Yang Su +3
Most reinforcement learning(RL)-based methods for drone racing target fixed, obstacle-free tracks, leaving the generalization to unknown, cluttered environments largely unaddressed…
Mapless Collision-Free Flight via MPC using Dual KD-Trees in Cluttered Environments
Linzuo Zhang, Yu Hu, Yang Deng +2
Collision-free flight in cluttered environments is a critical capability for autonomous quadrotors. Traditional methods often rely on detailed 3D map construction, trajectory gener…