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3D Coverage Path Planning for Efficient Construction Progress Monitoring

Becker, Katrin ; Oehler, Martin ; Stryk, Oskar von (2022)
3D Coverage Path Planning for Efficient Construction Progress Monitoring.
25th IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR 2021). Sevilla, Spain (08.-10.11.2022)
doi: 10.1109/SSRR56537.2022.10018726
Konferenzveröffentlichung, Bibliographie

Kurzbeschreibung (Abstract)

On construction sites, progress must be monitored continuously to ensure that the current state corresponds to the planned state in order to increase efficiency, safety and detect construction defects at an early stage. Autonomous mobile robots can document the state of construction with high data quality and consistency. However, finding a path that fully covers the construction site is a challenging task as it can be large, slowly changing over time, and contain dynamic objects. Existing approaches are either exploration approaches that require a long time to explore the entire building, object scanning approaches that are not suitable for large and complex buildings, or planning approaches that only consider 2D coverage. In this paper, we present a novel approach for planning an efficient 3D path for progress monitoring on large construction sites with multiple levels. By making use of an existing 3D model we ensure that all surfaces of the building are covered by the sensor payload such as a 360-degree camera or a lidar. This enables the consistent and reliable monitoring of construction site progress with an autonomous ground robot. We demonstrate the effectiveness of the proposed planner on an artificial and a real building model, showing that much shorter paths and better coverage are achieved than with a traditional exploration planner.

Typ des Eintrags: Konferenzveröffentlichung
Erschienen: 2022
Autor(en): Becker, Katrin ; Oehler, Martin ; Stryk, Oskar von
Art des Eintrags: Bibliographie
Titel: 3D Coverage Path Planning for Efficient Construction Progress Monitoring
Sprache: Englisch
Publikationsjahr: 11 November 2022
Verlag: IEEE
Buchtitel: SSRR 2022: IEEE International Symposium on Safety, Security, and Rescue Robotics
Veranstaltungstitel: 25th IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR 2021)
Veranstaltungsort: Sevilla, Spain
Veranstaltungsdatum: 08.-10.11.2022
DOI: 10.1109/SSRR56537.2022.10018726
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Kurzbeschreibung (Abstract):

On construction sites, progress must be monitored continuously to ensure that the current state corresponds to the planned state in order to increase efficiency, safety and detect construction defects at an early stage. Autonomous mobile robots can document the state of construction with high data quality and consistency. However, finding a path that fully covers the construction site is a challenging task as it can be large, slowly changing over time, and contain dynamic objects. Existing approaches are either exploration approaches that require a long time to explore the entire building, object scanning approaches that are not suitable for large and complex buildings, or planning approaches that only consider 2D coverage. In this paper, we present a novel approach for planning an efficient 3D path for progress monitoring on large construction sites with multiple levels. By making use of an existing 3D model we ensure that all surfaces of the building are covered by the sensor payload such as a 360-degree camera or a lidar. This enables the consistent and reliable monitoring of construction site progress with an autonomous ground robot. We demonstrate the effectiveness of the proposed planner on an artificial and a real building model, showing that much shorter paths and better coverage are achieved than with a traditional exploration planner.

Freie Schlagworte: emergenCITY, emergenCITY_CPS
Fachbereich(e)/-gebiet(e): 20 Fachbereich Informatik
20 Fachbereich Informatik > Simulation, Systemoptimierung und Robotik
LOEWE
LOEWE > LOEWE-Zentren
LOEWE > LOEWE-Zentren > emergenCITY
Hinterlegungsdatum: 27 Feb 2023 13:20
Letzte Änderung: 12 Jan 2024 08:38
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