Multi-Unmanned Aerial Vehicle (UAV) Cooperative Fault Detection Employing Differential Global Positioning (DGPS), Inertial and Vision Sensors

Guillermo Heredia,Aníbal Ollero,Luis Merino,Antidio Viguria,Fernando Caballero,Iván Maza

doi:10.3390/s90907566

Abstract

This paper presents a method to increase the reliability of Unmanned Aerial Vehicle (UAV) sensor Fault Detection and Identification (FDI) in a multi-UAV context. Differential Global Positioning System (DGPS) and inertial sensors are used for sensor FDI in each UAV. The method uses additional position estimations that augment individual UAV FDI system. These additional estimations are obtained using images from the same planar scene taken from two different UAVs. Since accuracy and noise level of the estimation depends on several factors, dynamic replanning of the multi-UAV team can be used to obtain a better estimation in case of faults caused by slow growing errors of absolute position estimation that cannot be detected by using local FDI in the UAVs. Experimental results with data from two real UAVs are also presented.

Highlights

Unmanned aerial vehicles (UAVs) and aerial robotics have attracted significant interest for a wide range of applications [1]
Slow growing errors in absolute positioning estimations are difficult to detect using incremental sensors as gyros or accelerometers, since Differential GPS (DGPS) is the only absolute positioning sensor that is used in UAVs
The objective is that a group of networked UAVs survey an area looking for fire alarms

Summary

Introduction

Unmanned aerial vehicles (UAVs) and aerial robotics have attracted significant interest for a wide range of applications [1]. Differential GPS (DGPS) receivers are able to achieve accuracies of a few centimeters using carrier-phase measurements, which make them a good choice for UAV positioning main sensors. In multi-UAV missions, it is possible to take advantage of the capabilities that the team of UAVs offers to augment each of the individual FDI systems In this way, sensors from other UAVs can be used to obtain additional data which can be applied to the UAV FDI system to detect faults in its own sensors.

Sensor FDI in Small Autonomous Helicopters

Differential GPS sensor failures

Multi-UAV Vision-Based Relative Position Estimation

Planning for Multi-UAV FDI

Experimental Results

Conclusions