DDCNN: A Promising Tool for Simulation-To-Reality UAV Fault Diagnosis

DDCNN: A Promising Tool for Simulation-To-Reality UAV Fault Diagnosis

16 February 2023

Wei Zhang

Papers citing "DDCNN: A Promising Tool for Simulation-To-Reality UAV Fault Diagnosis"

7 / 7 papers shown

Title
Simulation-to-reality UAV Fault Diagnosis with Deep Learning Wei Zhang Junjie Tong F. Liao Yunfeng Zhang 30 4 0 09 Feb 2023
Machine Learning for UAV Propeller Fault Detection based on a Hybrid Data Generation Model J. Tong Weinan Zhang F. Liao C. F. Li Y. Zhang 21 8 0 03 Feb 2023
IPAPRec: A promising tool for learning high-performance mapless navigation skills with deep reinforcement learning Wei Zhang Yunfeng Zhang Ning Liu Kai Ren Pengfei Wang 48 16 0 22 Mar 2021
Learn to Navigate Maplessly with Varied LiDAR Configurations: A Support Point-Based Approach Wei Zhang Ning Liu Yunfeng Zhang 3DPC 59 20 0 20 Oct 2020
Deep Drone Racing: From Simulation to Reality with Domain Randomization Antonio Loquercio Elia Kaufmann René Ranftl Alexey Dosovitskiy V. Koltun Davide Scaramuzza 69 210 0 21 May 2019
Learning agile and dynamic motor skills for legged robots Jemin Hwangbo Joonho Lee Alexey Dosovitskiy Dario Bellicoso Vassilios Tsounis V. Koltun Marco Hutter 108 1,309 0 24 Jan 2019
Virtual-to-real Deep Reinforcement Learning: Continuous Control of Mobile Robots for Mapless Navigation L. Tai Giuseppe Paolo Ming-Yuan Liu 84 711 0 01 Mar 2017

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