Using Artificial Neural Network to Provide Realistic Lifting Capacity in the Mobile Crane Simulation

Simulations are often used for training novice operators to avoid accidents, while they are still polishing their skills. To ensure the experience gained in the simulation be applicable in real-world scenarios, the simulation has to be made as realistic as possible. This paper investigated how to make the lifting capacity of a virtual mobile crane behave similarly like its real counterpart. We initially planned to use information from the load charts, which document how the lifting capacity of a mobile crane works, but the data in the load charts were very limited. To mitigate this issue, we trained an artificial neural network (ANN) using 90% of random data from two official load charts of a real mobile crane. The trained model could predict the lifting capacity based on the real-time states of the boom length, the load radius, and the counterweight of the virtual mobile crane. To evaluate the accuracy of the ANN predictions, we conducted a real-time experiment inside the simulation, where we compared the lifting capacity predicted by the ANN and the remaining 10% of the data from the load charts. The results showed that the ANN could predict the lifting capacity with small deviation rates. The deviation rates also had no significant impact on the lifting capacity, except when both boom length and load radius were approaching their maximum states. Therefore, the predicted lifting capacity generated by the ANN could be assumed to be close enough to the values in the load charts.This is an Author Accepted Manuscript version of the following chapter: S. Roysson, T. A. Sitompul, and R. Lindell, Using Artificial Neural Network to Provide Realistic Lifting Capacity in the Mobile Crane Simulation, published in Proceedings of the 22nd Engineering Applications of Neural Networks Conference, edited by L. Iliadis, J. Macintyre, C. Jayne, and E. Pimenidis, 2021, Springer reproduced with permission of Springer. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-80568-5 37.ImmerSafe - Immersive Visual Technologies for Safety-critical Application