Paper Info
Title
Redundant robot localization system based in wireless sensor network
Abstract
Localization and Mapping of autonomous robots in an harsh and unstable environment such as a steep slope vineyard is a challenging research topic. Dead Reckoning systems can fail due to the harsh conditions of the terrain, and the Global Position System can be affected by noise or even be unavailable. Agriculture is moving towards precision agriculture, with advanced monitoring systems and wireless sensor networks. These systems and wireless sensors are installed in the crop field and can be considered relevant landmarks for robot localization. In this paper the distance accuracy provided by bluetooth based sensors is deeply studied and characterized. It is considered a multi antenna receiver bluetooth system and obtained the transfer functions (from Received Signal Strength Indication (RSSI) to distance estimation) for each set of antenna and sensors. The performance of this technology is compared against Time-of-flight based technologies (Pozyx). The obtained results show that the agricultural wireless sensors can be used as redundant artificial landmarks for localization purposes. Besides, the RSSI characterization allowed to improve the previous results of our Beacon Mapping Procedure (BMP) required for accurate and reliable localization systems.
Year
DOI
Venue
2018
10.1109/ICARSC.2018.8374176
2018 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)
Keywords
Field
DocType
wireless sensor network,bluetooth,precision agriculture,agricultural robotics
Wireless,Received signal strength indication,Computer science,Terrain,Precision agriculture,Real-time computing,Dead reckoning,Robot,Wireless sensor network,Bluetooth
Conference
ISSN
ISBN
Citations 
2573-9360
978-1-5386-5222-0
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Ricardo Reis100.68
Jorge Mendes200.34
Filipe Neves dos Santos32312.24
Raul Morais45613.00
Nuno Ferraz501.01
Luís Santos611014.58
Armando Sousa74614.30