This article presents an overviewof the SmartSite research project that adopts machine learning, decision theory and distributed artificial intelligence to design and test amulti-agent system(MAS) for asphalt road construction. SmartSite putsmajor emphasis on sensing and communication technologies that integrate real-time automated information exchange in the supply chain of road construction. As part of the larger SmartSite project, this article introduces a novel real-time path planning system for compactors and presents the results of several simulation and field realistic experiments conducted to evaluate the system in a sophisticated simulation and harsh construction environment, respectively. The systemoperates based on Belief-Desire-Intention (BDI) software agents and real-time sensory inputs. The newly developed integrated and information rich process benefits asphalt compactor operators, as they are now capable to control their machinery and react to changing environmental, material-related and process-related disturbances or changes. This improves the quality of the delivery and laying of asphalt material, prevents compactors from over-compacting certain road segments, increases the road’s pavement longevity during the operational life cycle phase; refocuses the work tasks of the site managers, and reduces the construction budget and schedule. The system’s ability tomaneuver an asphalt roller during realword operation also makes it an important step towards a fully automated asphalt compactor
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