Close menu


Sunderland Repository records the research produced by the University of Sunderland including practice-based research and theses.

A survey on inspecting structures using robotic systems

Almadhoun, Randa, Taha, Tarek, Seneviratne, Lakmal, Dias, Jorge and Cai, Guowei (2016) A survey on inspecting structures using robotic systems. International Journal of Advanced Robotic Systems, 13 (6). pp. 1729-8814. ISSN 1729-8814

Item Type: Article


Advancements in robotics and autonomous systems are being deployed nowadays in many application domains such as search and rescue, industrial automation, domestic services and healthcare. These systems are developed to tackle tasks in some of the most challenging, labour intensive and dangerous environments. Inspecting structures (e.g., bridges, buildings, ships, wind turbines and aircrafts) is considered a hard task for humans to perform and of critical importance since missing any details could affect the structure’s performance and integrity. Additionally, structure inspection is time and resource intensive and should be performed as efficiently and accurately as possible. Inspecting various structures has been reported in the literature using different robotic platforms to: inspect difficult to reach areas and detect various types of faults and anomalies. Typically, inspection missions involve performing three main tasks: coverage path planning, shape, model or surface reconstruction and the actual inspection of the structure. Coverage path planning ensures the generation of an optimized path that guarantees the complete coverage of the structure of interest in order to gather highly accurate information to be used for shape/model reconstruction. This article aims to provide an overview of the recent work and breakthroughs in the field of coverage path planning and model reconstruction, with focus on 3D reconstruction, for the purpose of robotic inspection.

Full text not available from this repository.

More Information

Related URLs:
Depositing User: Randa Almadhoun


Item ID: 16464
Identification Number:
ISSN: 1729-8814
Official URL:

Users with ORCIDS

ORCID for Randa Almadhoun: ORCID iD
ORCID for Tarek Taha: ORCID iD
ORCID for Lakmal Seneviratne: ORCID iD
ORCID for Jorge Dias: ORCID iD

Catalogue record

Date Deposited: 20 Nov 2023 10:44
Last Modified: 20 Nov 2023 10:44


Author: Randa Almadhoun ORCID iD
Author: Tarek Taha ORCID iD
Author: Lakmal Seneviratne ORCID iD
Author: Jorge Dias ORCID iD
Author: Guowei Cai

University Divisions

Faculty of Technology


Computing > Artificial Intelligence

Actions (login required)

View Item (Repository Staff Only) View Item (Repository Staff Only)