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Introduction
Global commercial drone market
Vision-based drone navigation
Why we want agile drones
Previous works on agile flight
Vision-based drone control architecture
Key questions to incorporate deep learning into drone control
Our vision-controlled drone: "SWIFT"
Acrobatics and high-speed flight in the wild
Drone racing
Formal definition of the autonomous drone-racing problem
Autonomous drone racing control approaches: MPC, MPCC, RL
Unmodeled effects in autonomous drone racing
State estimation of SWIFT (Nature paper)
How SWIFT learns to race with reinforcement learning (Nature and SciRob papers)
Simulation-to-reality gap in SWIFT
Reinforcement learning vs. optimal control (SciRob paper)
SWIFT vs. human flight behavior
The future of drones: how birds fly and see
Fast sensors: event cameras
Application 1: keeping drones flying when a motor fails
Application 2: Dodging dynamic objects with drones
Application 3: Catching dynamic objects with legged robots
Combining event cameras with RGB cameras
User-conditioned neural network control policies