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dc.contributor.authorZhang, Tianci-
dc.description.abstractCrane lifting is an important but common task in industrial plants. In order to find a safe and cost effective path of lifting, a Master–Slave Parallel Genetic Algorithm and implement the algorithm on Graphics Processing Units using CUDA programming is developed to calculate a lifting path plan. Before this algorithm is introduced to crane industry, a tower crane model should be established to test the integrity between software algorithm calculation and physical crane lifting. Furthermore, because of the complexity and volatility of construction sites, a sensor-based collision avoidance system tailored for tower crane is crucial for tower crane lifting. In this report, a 1:64 tower crane fully functional tower crane model based on Terex SK 415-20 is designed and erected. Master–Slave Parallel Genetic Algorithm is used to calculate the optimized lifting path which can be read by the tower crane model. After that, the crane moves accordingly in the 3 degree-of-freedom space and performs the lifting task. Meanwhile, the sensor-based collision avoidance system is activated and monitors potential collision hazard. Once obstacles are detected, the collision avoidance system is able to stop the tower crane and manual remote control is activated. At last, the tower crane model is tested and reviewed. Suggestions are given for future study on crane lifting and related study.en_US
dc.format.extent77 p.en_US
dc.rightsNanyang Technological University-
dc.titleSensor-enabled crane liftingen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorCai Yiyuen_US
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen_US
dc.description.degreeBachelor of Engineering (Mechanical Engineering)en_US
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Appears in Collections:MAE Student Reports (FYP/IA/PA/PI)
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