Learning-based autonomous uav system for electrical and mechanical (E&m) device inspection

Yurong Feng, Kwaiwa Tse, Shengyang Chen, Chih Yung Wen, Boyang Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)

Abstract

The inspection of electrical and mechanical (E&M) devices using unmanned aerial vehicles (UAVs) has become an increasingly popular choice in the last decade due to their flexibility and mobility. UAVs have the potential to reduce human involvement in visual inspection tasks, which could increase efficiency and reduce risks. This paper presents a UAV system for autonomously performing E&M device inspection. The proposed system relies on learning-based detection for perception, multi-sensor fusion for localization, and path planning for fully autonomous inspection. The perception method utilizes semantic and spatial information generated by a 2-D object detector. The information is then fused with depth measurements for object state estimation. No prior knowledge about the location and category of the target device is needed. The system design is validated by flight experiments using a quadrotor platform. The result shows that the proposed UAV system enables the inspection mission autonomously and ensures a stable and collision-free flight.

Original languageEnglish
Article number1385
Pages (from-to)1-23
Number of pages23
JournalSensors (Switzerland)
Volume21
Issue number4
DOIs
Publication statusPublished - 2 Feb 2021

Keywords

  • Autonomous inspection
  • Deep learning
  • Object detection
  • UAV

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering

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