Abstract
Crane operators are often unable to identify collision hazards due to limited visibility during blind lifts or when operating under cluttered conditions. This paper presents a multisensor-driven real-time crane monitoring system consisting of load tracking, obstacle detection, worker detection, collision warning, and 3D visualization modules. A combination of encoders, vision, and laser scanning systems is used to reconstruct a 3D workspace model of the crane environment and provide real-time spatial feedback to the operator. Field experimentation was carried out at an outdoor crane manufacturing facility under different blind lift scenarios. Results showed that the encoder-based load positioning system has a mean height estimation error within 0.33 m, the vision-based load positioning system has a mean centroid error of 0.454 m, and the worker detection system has a mean centroid error of 0.023 m. This study also provided important findings in terms of challenges and limitations in implementing a real-world crane monitoring system.
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