Autonomous wheel loading involves selecting actions that maximize the total performance over many repetitions. The actions should be well adapted to the current state of the pile and its future states. Selecting the best actions is difficult since the pile states are consequences of previous actions and thus are highly unknown. To aid the selection of actions, this paper investigates data-driven models to predict the loaded mass, time, work, and resulting pile state of a loading action given the initial pile state. Deep neural networks were trained on data using over 10,000 simulations to an accuracy of 91-97,% with the pile state represented either by a heightmap or by its slope and curvature. The net outcome of sequential loading actions is predicted by repeating the model inference at five milliseconds per loading. As errors accumulate during the inferences, long-horizon predictions need to be combined with a physics-based model.

翻译：自主轮式装载涉及选择能最大化多次重复作业总性能的动作。这些动作应充分适应当前料堆状态及其未来状态。由于料堆状态是先前动作的产物且具有高度不确定性，选择最优动作极为困难。为辅助动作选择，本文研究基于数据驱动的模型，用于预测给定初始料堆状态下装载动作的装载质量、时间、做功及最终料堆状态。采用深度神经网络在包含超过10,000次仿真的数据上进行训练，当料堆状态以高度图或其斜率和曲率表征时，模型准确率达到91%-97%。通过以每次装载5毫秒的速度重复模型推理，可预测连续装载动作的净效果。由于推理过程中误差会累积，长时域预测需与基于物理的模型相结合。