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

Multi-Robot Hybrid Coverage Path Planning for 3D Reconstruction of Large Structures

Almadhoun, Randa, Taha, Tarek, Seneviratne, Lakmal and Zweiri, Yahya (2022) Multi-Robot Hybrid Coverage Path Planning for 3D Reconstruction of Large Structures. IEEE Access, 10. pp. 2037-2050. ISSN 2169-3536

Item Type: Article


Coverage Path Planning (CPP) is an essential capability for autonomous robots operating in various critical applications such as firefighting, and inspection. Performing autonomous coverage using a single robot system consumes time and energy. In particular, 3D large structures might contain some complex and occluded areas that shall be scanned rapidly in certain application domains. In this paper, a new Hybrid Coverage Path Planning (HCPP) approach is proposed to explore and cover unknown 3D large structures using a decentralized multi-robot system. The HCPP approach combines a guided Next Best View (NBV) approach with a developed Long Short-Term Memory (LSTM) waypoint prediction approach to decrease the CPP exploration time at each iteration and simultaneously achieve high coverage. The hybrid approach is the new ML paradigm which fosters intelligence by balancing between data efficiency and generality allowing the exchange of some CPP parts with a learned model. The HCPP uses a stateful LSTM network architecture which is trained based on collected paths that cover different 3D structures to predict the next viewpoint. This architecture captures the dynamic dependencies of adjacent viewpoints in the long-term sequences like the coverage paths. The HCPP switches between these methods triggered by either the number of iterations or an entropy threshold. In the decentralized multi-robot system, the proposed HCPP is embedded in each robot where each one of them shares its global 3D map ensuring robustness. The results performed in a realistic Gazebo robotic simulator confirmed the advantage of the proposed HCPP approach by achieving high coverage on different 3D unknown structures in a shorter time compared to conventional NBV.

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More Information

Uncontrolled Keywords: Autonomous exploration, multi-robot, coverage planning, LSTM, path prediction, 3D reconstruction.
Related URLs:
Depositing User: Randa Almadhoun


Item ID: 16457
Identification Number:
ISSN: 2169-3536
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 Yahya Zweiri: ORCID iD

Catalogue record

Date Deposited: 20 Nov 2023 10:21
Last Modified: 20 Nov 2023 10:21


Author: Randa Almadhoun ORCID iD
Author: Tarek Taha ORCID iD
Author: Lakmal Seneviratne ORCID iD
Author: Yahya Zweiri ORCID iD

University Divisions

Faculty of Technology


Computing > Artificial Intelligence

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