Comprehensive tower crane dynamics: An experimental dataset of trajectories and payload oscillations for system...

Area of Science:

• Robotics and Control Systems
• Mechanical Engineering
• Machine Learning Applications

Background:

• Cranes are crucial for material handling in industries like construction and shipping.
• Controlling crane trolley positioning and payload oscillation is challenging due to cable flexibility and crane dynamics, especially with tower rotation.
• Existing methods struggle with precise control, leading to inefficiencies and potential damage.

Purpose of the Study:

• To provide a comprehensive dataset of tower crane trajectories and payload oscillations.
• To facilitate research in advanced control strategies for cranes.
• To enable the development and training of machine learning-based automation systems for cranes.

Main Methods:

• A laboratory-scaled tower crane setup was utilized to collect data.
• Various operating scenarios were simulated, including tower rotation, diverse trolley trajectories, and different payload characteristics (mass, shape, cable length).
• Payload hoisting dynamics were also recorded.

Main Results:

• The dataset captures detailed information on crane movement and resulting payload oscillations under varied conditions.
• It covers a wide range of operational parameters relevant to real-world crane applications.
• The data serves as a benchmark for evaluating control algorithms.

Conclusions:

• The generated dataset is a valuable resource for understanding tower crane dynamics.
• It supports the advancement of machine learning approaches for enhanced crane control and automation.
• This data can lead to more efficient and safer material handling operations.