A framework for real-time pro-active safety assistance for mobile crane lifting operations

Abstract Despite many safety considerations addressed in lift pre-planning, the ability to provide real-time safety assistance to crane operators and to mitigate human errors during the lifting operation is missing. This research developed a framework for real-time pro-active safety assistance for mobile crane lifting operations. First, crane poses are reconstructed in real-time based on the critical motions of crane parts captured by a sensor system. Second, as-is lift site conditions are automatically modeled and updated based on point cloud data. Lastly, the risk of colliding the crane parts and lifted load into nearby obstructions is pro-actively analyzed and warnings are provided to the operator through a graphical user interface. A prototype system was developed based on the framework and deployed on a mobile crane. Field test results indicate that the system can accurately reconstruct crane motion in real-time and provide pro-active warnings that allow the operator to make timely decisions to mitigate the risk.

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