ROSI: A Robotic System for Harsh Outdoor Industrial Inspection - System Design and Applications

Belt Conveyors are essential for transporting dry bulk material in different industries. Such structures require permanent inspections, traditionally executed by human operators based on cognition. To improve working conditions and process standardization, we propose a novel procedure to inspect con...

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Veröffentlicht in:	Journal of intelligent & robotic systems Jg. 103; H. 2; S. 30
Hauptverfasser:	Rocha, Filipe, Garcia, Gabriel, Pereira, Raphael F. S., Faria, Henrique D., Silva, Thales H., Andrade, Ricardo H. R., Barbosa, Evelyn S., Almeida, André, Cruz, Emanuel, Andrade, Wagner, Serrantola, Wenderson G., Moura, Luiz, Azpúrua, Héctor, Franca, André, Pessin, Gustavo, Freitas, Gustavo M., Costa, Ramon R., Lizarralde, Fernando
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Dordrecht Springer Netherlands 01.10.2021 Springer Springer Nature B.V
Schlagworte:	Algorithms Artificial Intelligence Belt conveyors Cognition Contact force Control Conveying machinery Data mining Decoupling Electrical Engineering Energy conservation Energy consumption Engineering Fault detection Field tests Hybrid systems Innovations Inspection Locomotion Machine learning Mechanical Engineering Mechatronics Mineral industry Mining industry Regular Paper Robot arms Robot control Robot dynamics Robotics Robots Sensors Standardization Systems design Topical collection on ICAR 2019 Special Issue Assisted operation control Belt conveyor inspection Mobile manipulator design Service robot Machine learning for industrial inspection
ISSN:	0921-0296, 1573-0409
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Abstract	Belt Conveyors are essential for transporting dry bulk material in different industries. Such structures require permanent inspections, traditionally executed by human operators based on cognition. To improve working conditions and process standardization, we propose a novel procedure to inspect conveyor structures with a ground robot composed by a mobile platform, a robotic arm, and a sensor-set. Based on field experience, we introduce ROSI, a new robotic device designed for long-term operations in harsh outdoor environments. The mobile robot has a hybrid locomotion system, using wheels to reduce energy consumption while covering long distances, and also flippers with tracks to improve mobility during obstacle negotiation. A mechanical passive switch allows decoupling tracks’ traction, reducing components wear and energy consumption without raising mechanical complexity. Aiming the robot-assisted operation, control strategies help to (i) command both the mobile platform and a robotic manipulator considering the system whole-body model, (ii) adjust the contact force for touching the conveyor structure during vibration inspection, and (iii) climb stairs while automatically adjusting the flippers. Machine Learning algorithms detect conveyors’ dirt build-ups, roller failures, and bearing faults by processing visual, thermal and sound data as inspection functionalities. The algorithms training and validation use a dataset collected from running conveyors at Vale, presenting detection accuracy superior to 90%. Field test results in a mining site demonstrate the robot capabilities to stand for the harsh operating conditions while executing all the required inspection tasks, stating ROSI as a disruptive solution for Belt Conveyor inspections and other general industrial operations.
AbstractList	Belt Conveyors are essential for transporting dry bulk material in different industries. Such structures require permanent inspections, traditionally executed by human operators based on cognition. To improve working conditions and process standardization, we propose a novel procedure to inspect conveyor structures with a ground robot composed by a mobile platform, a robotic arm, and a sensor-set. Based on field experience, we introduce ROSI, a new robotic device designed for long-term operations in harsh outdoor environments. The mobile robot has a hybrid locomotion system, using wheels to reduce energy consumption while covering long distances, and also flippers with tracks to improve mobility during obstacle negotiation. A mechanical passive switch allows decoupling tracks’ traction, reducing components wear and energy consumption without raising mechanical complexity. Aiming the robot-assisted operation, control strategies help to (i) command both the mobile platform and a robotic manipulator considering the system whole-body model, (ii) adjust the contact force for touching the conveyor structure during vibration inspection, and (iii) climb stairs while automatically adjusting the flippers. Machine Learning algorithms detect conveyors’ dirt build-ups, roller failures, and bearing faults by processing visual, thermal and sound data as inspection functionalities. The algorithms training and validation use a dataset collected from running conveyors at Vale, presenting detection accuracy superior to 90%. Field test results in a mining site demonstrate the robot capabilities to stand for the harsh operating conditions while executing all the required inspection tasks, stating ROSI as a disruptive solution for Belt Conveyor inspections and other general industrial operations. Belt Conveyors are essential for transporting dry bulk material in different industries. Such structures require permanent inspections, traditionally executed by human operators based on cognition. To improve working conditions and process standardization, we propose a novel procedure to inspect conveyor structures with a ground robot composed by a mobile platform, a robotic arm, and a sensor-set. Based on field experience, we introduce ROSI, a new robotic device designed for long-term operations in harsh outdoor environments. The mobile robot has a hybrid locomotion system, using wheels to reduce energy consumption while covering long distances, and also flippers with tracks to improve mobility during obstacle negotiation. A mechanical passive switch allows decoupling tracks' traction, reducing components wear and energy consumption without raising mechanical complexity. Aiming the robot-assisted operation, control strategies help to (i) command both the mobile platform and a robotic manipulator considering the system whole-body model, (ii) adjust the contact force for touching the conveyor structure during vibration inspection, and (iii) climb stairs while automatically adjusting the flippers. Machine Learning algorithms detect conveyors' dirt build-ups, roller failures, and bearing faults by processing visual, thermal and sound data as inspection functionalities. The algorithms training and validation use a dataset collected from running conveyors at Vale, presenting detection accuracy superior to 90%. Field test results in a mining site demonstrate the robot capabilities to stand for the harsh operating conditions while executing all the required inspection tasks, stating ROSI as a disruptive solution for Belt Conveyor inspections and other general industrial operations. Keywords Mobile manipulator design * Assisted operation control * Machine learning for industrial inspection * Belt conveyor inspection * Service robot
ArticleNumber	30
Audience	Academic
Author	Franca, André Almeida, André Silva, Thales H. Azpúrua, Héctor Andrade, Ricardo H. R. Andrade, Wagner Pereira, Raphael F. S. Serrantola, Wenderson G. Freitas, Gustavo M. Cruz, Emanuel Rocha, Filipe Barbosa, Evelyn S. Lizarralde, Fernando Costa, Ramon R. Pessin, Gustavo Moura, Luiz Garcia, Gabriel Faria, Henrique D.
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Cites_doi	10.1007/978-3-319-57870-5 10.1007/978-1-84628-642-1 10.1016/S0301-679X(99)00077-8 10.1115/1.3143764 10.1109/ICAR46387.2019.8981561 10.1109/IROS.2017.8206485 10.1007/s10514-012-9321-0 10.5220/0006369101900200 10.1109/ICMLA.2019.00116 10.1109/MRA.2018.2877189 10.1145/358669.358692 10.1364/OE.25.008315 10.3390/app11052299 10.1109/ICMECH.2019.8722900 10.1080/10402004.2010.533817 10.1145/3174243.3174266 10.1109/CVPR.2016.90 10.3390/s20205762 10.3390/s20082243 10.3390/app10144984 10.1016/j.ifacol.2015.08.033 10.1109/EESMS.2011.6067043 10.1109/IROS.2007.4399139 10.1109/URAI.2016.7734069
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Keywords	Assisted operation control Belt conveyor inspection Mobile manipulator design Service robot Machine learning for industrial inspection
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SubjectTerms	Algorithms Artificial Intelligence Belt conveyors Cognition Contact force Control Conveying machinery Data mining Decoupling Electrical Engineering Energy conservation Energy consumption Engineering Fault detection Field tests Hybrid systems Innovations Inspection Locomotion Machine learning Mechanical Engineering Mechatronics Mineral industry Mining industry Regular Paper Robot arms Robot control Robot dynamics Robotics Robots Sensors Standardization Systems design Topical collection on ICAR 2019 Special Issue
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Title	ROSI: A Robotic System for Harsh Outdoor Industrial Inspection - System Design and Applications
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