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Volume 22
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10.3390/s22093414
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Article

ROADS—Rover for Bituminous Pavement Distress Survey: An Unmanned Ground Vehicle (UGV) Prototype for Pavement Distress Evaluation

by
Alessandro Mei
1,*,
Emiliano Zampetti
1,
Paola Di Mascio
2,
Giuliano Fontinovo
1,
Paolo Papa
1 and
Antonio D’Andrea
2
1
Institute of Atmospheric Pollution Research, National Research Council of Italy, 00015 Monterotondo, Italy
2
Department of Civil, Constructional and Environmental Engineeering, Sapienza University of Rome, 00184 Rome, Italy
*
Author to whom correspondence should be addressed.
Sensors 2022, 22(9), 3414; https://doi.org/10.3390/s22093414
Submission received: 3 March 2022 / Revised: 19 April 2022 / Accepted: 25 April 2022 / Published: 29 April 2022
(This article belongs to the Special Issue Sensing Technologies and Applications in Infrared and Visible Imaging)
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Abstract

Maintenance has a major impact on the financial plan of road managers. To ameliorate road conditions and reduce safety constraints, distress evaluation methods should be efficient and should avoid being time consuming. That is why road cadastral catalogs should be updated periodically, and interventions should be provided for specific management plans. This paper focuses on the setting of an Unmanned Ground Vehicle (UGV) for road pavement distress monitoring, and the Rover for bituminOus pAvement Distress Survey (ROADS) prototype is presented in this paper. ROADS has a multisensory platform fixed on it that is able to collect different parameters. Navigation and environment sensors support a two-image acquisition system which is composed of a high-resolution digital camera and a multispectral imaging sensor. The Pavement Condition Index (PCI) and the Image Distress Quantity (IDQ) are, respectively, calculated by field activities and image computation. The model used to calculate the IROADS index from PCI had an accuracy of 74.2%. Such results show that the retrieval of PCI from image-based approach is achievable and values can be categorized as “Good”/“Preventive Maintenance”, “Fair”/“Rehabilitation”, “Poor”/“Reconstruction”, which are ranges of the custom PCI ranting scale and represents a typical repair strategy.
Keywords: unmanned ground vehicle; imaging; road distress; pavement condition index; multispectral; image distress quantity; repair strategy unmanned ground vehicle; imaging; road distress; pavement condition index; multispectral; image distress quantity; repair strategy
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MDPI and ACS Style

Mei, A.; Zampetti, E.; Di Mascio, P.; Fontinovo, G.; Papa, P.; D’Andrea, A. ROADS—Rover for Bituminous Pavement Distress Survey: An Unmanned Ground Vehicle (UGV) Prototype for Pavement Distress Evaluation. Sensors 2022, 22, 3414. https://doi.org/10.3390/s22093414

AMA Style

Mei A, Zampetti E, Di Mascio P, Fontinovo G, Papa P, D’Andrea A. ROADS—Rover for Bituminous Pavement Distress Survey: An Unmanned Ground Vehicle (UGV) Prototype for Pavement Distress Evaluation. Sensors. 2022; 22(9):3414. https://doi.org/10.3390/s22093414

Chicago/Turabian Style

Mei, Alessandro, Emiliano Zampetti, Paola Di Mascio, Giuliano Fontinovo, Paolo Papa, and Antonio D’Andrea. 2022. "ROADS—Rover for Bituminous Pavement Distress Survey: An Unmanned Ground Vehicle (UGV) Prototype for Pavement Distress Evaluation" Sensors 22, no. 9: 3414. https://doi.org/10.3390/s22093414

APA Style

Mei, A., Zampetti, E., Di Mascio, P., Fontinovo, G., Papa, P., & D’Andrea, A. (2022). ROADS—Rover for Bituminous Pavement Distress Survey: An Unmanned Ground Vehicle (UGV) Prototype for Pavement Distress Evaluation. Sensors, 22(9), 3414. https://doi.org/10.3390/s22093414

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