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dc.contributor.authorGibbs, Gerard
dc.contributor.authorJia, Huamin
dc.contributor.authorIrfan, Madani
dc.identifier.citationGibbs, G., Jia, H., & Madani, I. (2017). Obstacle detection with ultrasonic sensors and signal analysis metrics. Transportation Research Procedia, 28, 173-182. doi:10.1016/j.trpro.2017.12.183en_US
dc.description.abstractOne of the basic tasks for autonomous flight with aerial vehicles (drones) is the detection of obstacles within its flight environment. As the technology develops and becomes more robust, drones will become part of the toolkit to aid maintenance repair and operation (MRO) and ground personnel at airports. Currently laser technology is the primary means for obstacle detection as it provides high resolution and long range. The high intensity laser beam can result in temporary blindness for pilots when the beam targets the windscreen of aircraft on the ground or on final approach within the vicinity of the airport. An alternative is ultrasonic sensor technology, but this suffers from poor angular resolution. In this paper we present a solution using time-of-flight (TOF) data from ultrasonic sensors. This system uses a single commercial 40 kHz combined transmitter/ receiver which returns the distance to the nearest obstacle in its field of view, +/- 30 degrees given the speed of sound in air at ambient temperature. Two sonar receivers located either side of the transmitter / receiver are mounted on a horizontal rotating shaft. Rotation of this shaft allows for separate sonar observations at regular intervals which cover the field of view of the transmitter / receiver. To reduce the sampling frequency an envelope detector is used prior to the analogue-digital-conversion for each of the sonar channels. A scalar Kalman filter for each channel reduces the effects of signal noise by providing real time filtering (Drongelen, 2017a). Four signal metrics are used to determine the location of the obstacle in the sensors field of view: 1. Maximum (Peak) frequency 2. Cross correlation of raw data and PSD 3. Power Spectral Density 4. Energy Spectral Density Results obtained in an actual indoor environment are presented to support the validity of the proposed algorithm.en_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Ireland*
dc.subjectKalman filteren_US
dc.subjectLomb Algorithmen_US
dc.subjectcross correlationen_US
dc.subjectenvelope detectoren_US
dc.subjectenergy power densityen_US
dc.titleObstacle detection with ultrasonic sensors and signal analysis metricsen_US
dc.subject.departmentDepartment of Aerospace, Mechanical & Electronic Engineering - IT Carlowen_US

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Attribution-NonCommercial-NoDerivs 3.0 Ireland
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Ireland