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Optimising data extraction from one dimensional distance sensors

Optimising data extraction from one dimensional distance sensors

Seals, Richard and Eissa, Hazem (2016) Optimising data extraction from one dimensional distance sensors. In: The First Medway Engineering Conference on Systems: Efficiency, Sustainability and Modelling, 6th June 2016, University of Greenwich. (Submitted)

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Abstract

To be effective a mobile robot needs to be aware of the local environment and this information is obtained using a variety of sensors. A one dimensional type of sensor is the distance sensor, such as ultrasonic, laser or infrared rangefinders which provide the distance to the nearest object in whatever direction they are pointing. This type of distance sensor only provides one dimensional data as the orientation of the mobile robot itself cannot be relied upon to provide a second dimension. But by utilising a variety of different techniques it is possible to increase the dimensional content of the distance data obtain. One approach is to cause the mobile robot to weave left and right as it is moving forward thereby effectively creating a scanned volume to the front of the sensor. Alternatively an actuator can be used to accurately rotate the distance sensor through known angles which provides two dimensional distance data about the environment. Another approach is to implement multiple distance sensors to create one dimensional or even two dimensional arrays. All these techniques can be combined together to further increase the dimensional content of the distance data. Once the data has been collected then knowledge about the environment is extracted to determine which are static objects and which are moving.

Item Type: Conference or Conference Paper (Lecture)
Uncontrolled Keywords: distance sensors, mobile robots, signal analysis
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Department of Engineering Science
Last Modified: 11 Aug 2017 10:51
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/14551

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