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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Vydáno v:Journal of intelligent & robotic systems Ročník 103; číslo 2; s. 30
Hlavní autoři: 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
Médium: Journal Article
Jazyk:angličtina
Vydáno: Dordrecht Springer Netherlands 01.10.2021
Springer
Springer Nature B.V
Témata:
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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Shrnutí: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.
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ISSN:0921-0296
1573-0409
DOI:10.1007/s10846-021-01459-2