A Smart Landing Platform With Data-Driven Analytic Procedures for UAV Preflight Safety Diagnosis

Due to the limitation imposed by hardware and form factor considerations, multirotor unmanned aircraft (drones) are unable to conduct preflight physical checks on their own capacity. Critical safety checks involve detecting various anomalies such as imbalanced payload, damaged propellers, mulfunctio...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Zhenyu Zhou, Yanchao Liu
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Anomaly detection nonlinear least squares statistical inference unmanned aircraft Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/ba6814d949fe4c1688cb73040ef00faa
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:ba6814d949fe4c1688cb73040ef00faa
record_format	dspace
spelling	oai:doaj.org-article:ba6814d949fe4c1688cb73040ef00faa2021-11-25T00:00:51ZA Smart Landing Platform With Data-Driven Analytic Procedures for UAV Preflight Safety Diagnosis2169-353610.1109/ACCESS.2021.3128866https://doaj.org/article/ba6814d949fe4c1688cb73040ef00faa2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9617601/https://doaj.org/toc/2169-3536Due to the limitation imposed by hardware and form factor considerations, multirotor unmanned aircraft (drones) are unable to conduct preflight physical checks on their own capacity. Critical safety checks involve detecting various anomalies such as imbalanced payload, damaged propellers, mulfunctioning motors and poorly calibrated compass, etc. Human efforts are currently required for performing such tasks, which impedes large-scale deployments of drones and increases the operational costs. In this work, we propose a weight-measuring landing platform along with a set of statistical inference algorithms aimed at performing safety checks for any multicopter aircraft that lands on the platform. We develop a nonconvex nonlinear least squares model for estimating the center of gravity and orientation of the aircraft, and derive a recursive formula for calculating the optimal solution. In numeric experiments, our analytical solution method has been able to find the global solution orders-of-magnitude faster than a global optimization solver. We have conducted real-system tests on a quadcopter drone deliberately configured to carry misplaced payload, and to use damaged propellers. Experiment results show that the platform is able to detect and profile these common safety issues with high accuracy.Zhenyu ZhouYanchao LiuIEEEarticleAnomaly detectionnonlinear least squaresstatistical inferenceunmanned aircraftElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 154876-154891 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Anomaly detection nonlinear least squares statistical inference unmanned aircraft Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Anomaly detection nonlinear least squares statistical inference unmanned aircraft Electrical engineering. Electronics. Nuclear engineering TK1-9971 Zhenyu Zhou Yanchao Liu A Smart Landing Platform With Data-Driven Analytic Procedures for UAV Preflight Safety Diagnosis
description	Due to the limitation imposed by hardware and form factor considerations, multirotor unmanned aircraft (drones) are unable to conduct preflight physical checks on their own capacity. Critical safety checks involve detecting various anomalies such as imbalanced payload, damaged propellers, mulfunctioning motors and poorly calibrated compass, etc. Human efforts are currently required for performing such tasks, which impedes large-scale deployments of drones and increases the operational costs. In this work, we propose a weight-measuring landing platform along with a set of statistical inference algorithms aimed at performing safety checks for any multicopter aircraft that lands on the platform. We develop a nonconvex nonlinear least squares model for estimating the center of gravity and orientation of the aircraft, and derive a recursive formula for calculating the optimal solution. In numeric experiments, our analytical solution method has been able to find the global solution orders-of-magnitude faster than a global optimization solver. We have conducted real-system tests on a quadcopter drone deliberately configured to carry misplaced payload, and to use damaged propellers. Experiment results show that the platform is able to detect and profile these common safety issues with high accuracy.
format	article
author	Zhenyu Zhou Yanchao Liu
author_facet	Zhenyu Zhou Yanchao Liu
author_sort	Zhenyu Zhou
title	A Smart Landing Platform With Data-Driven Analytic Procedures for UAV Preflight Safety Diagnosis
title_short	A Smart Landing Platform With Data-Driven Analytic Procedures for UAV Preflight Safety Diagnosis
title_full	A Smart Landing Platform With Data-Driven Analytic Procedures for UAV Preflight Safety Diagnosis
title_fullStr	A Smart Landing Platform With Data-Driven Analytic Procedures for UAV Preflight Safety Diagnosis
title_full_unstemmed	A Smart Landing Platform With Data-Driven Analytic Procedures for UAV Preflight Safety Diagnosis
title_sort	smart landing platform with data-driven analytic procedures for uav preflight safety diagnosis
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/ba6814d949fe4c1688cb73040ef00faa
work_keys_str_mv	AT zhenyuzhou asmartlandingplatformwithdatadrivenanalyticproceduresforuavpreflightsafetydiagnosis AT yanchaoliu asmartlandingplatformwithdatadrivenanalyticproceduresforuavpreflightsafetydiagnosis AT zhenyuzhou smartlandingplatformwithdatadrivenanalyticproceduresforuavpreflightsafetydiagnosis AT yanchaoliu smartlandingplatformwithdatadrivenanalyticproceduresforuavpreflightsafetydiagnosis
_version_	1718414716149170176

A Smart Landing Platform With Data-Driven Analytic Procedures for UAV Preflight Safety Diagnosis

Ejemplares similares