Learning by Digging : A Differentiable Prediction Model for an Autonomous Wheel Loader

Sammanfattning: Wheel loaders are heavy duty machines that are ubiquitous on construction sites and in mines all over the world. Fully autonomous wheel loaders remains an open problem but the industry is hoping that increasing their level of autonomy will help to reduce costs and energy consumption while also increasing workplace safety. Operating a wheel loader efficiently requires dig plans that extend over multiple dig cycles and not just one at a time. This calls for a model that can predict both the performance of a dig action and the resulting shape of the pile. In this thesis project, we use simulations to develop a data-driven artificial neural network model that can predict the outcome of a dig action. The model is able to predict the wheel loader’s productivity with an average error of 7.3% and the altered shape of the pile with an average relative error of 4.5%. We also show that automatic differentiation techniques can be used to accurately differentiate the model with respect to input. This makes it possible to use gradient-based optimization methods to find the dig action that maximises the performance of the wheel loader.