Predicting trajectory of crane-lifted load using LSTM network: A comparative study of simulated and real-world scenarios

The use of robotics automation in on-site construction is crucial for achieving precise engineering and quality output. However, there is a lack of modern literature reviews on using robotic arms as small-scale model rotary cranes for predicting suspended load trajectories. This paper presents the first comprehensive attempt to use Long Short Term Memory (LSTM), a deep learning architecture, to approximate load trajectories using a dataset collected through simulation and real-world conditions. The presented framework establishes reasonably high correlations between crane motion and load movement, indicating its effectiveness and robustness. Unlike previous works, this study uses two approaches to collect the dataset: simulation and real-world conditions. For the real-world scenario, a time-based robotic crane controller is equipped with an IMU tracking camera to record on-the-fly movement of the hanging load. For simulation data, the Unity3D platform is used to mimic a virtual environment scenario and perform effortless data generation. The proposed framework's novelty is demonstrated by an impressive RMSE of 8.11 and graphical visualizations that provide further insights into the analyses and evaluations conducted. Overall, this study is essential for incentivizing future research in relevant on-site construction applications to enhance productivity and promote worker safety.