This paper presents a method using data-driven models for selecting actions and predicting the total performance of autonomous wheel loader operations over many loading cycles in a changing environment. The performance includes loaded mass, loading time, work. The data-driven models input the control parameters of a loading action and the heightmap of the initial pile state to output the inference of either the performance or the resulting pile state. By iteratively utilizing the resulting pile state as the initial pile state for consecutive predictions, the prediction method enables long-horizon forecasting. Deep neural networks were trained on data from over 10,000 random loading actions in gravel piles of different shapes using 3D multibody dynamics simulation. The models predict the performance and the resulting pile state with, on average, 95% accuracy in 1.2 ms, and 97% in 4.5 ms, respectively. The performance prediction was found to be even faster in exchange for accuracy by reducing the model size with the lower dimensional representation of the pile state using its slope and curvature. The feasibility of long-horizon predictions was confirmed with 40 sequential loading actions at a large pile. With the aid of a physics-based model, the pile state predictions are kept sufficiently accurate for longer-horizon use.

翻译：本文提出一种利用数据驱动模型的方法，用于在变化环境中选择动作并预测自主轮式装载机多个装载周期内的整体性能。性能指标包括装载质量、装载时间和功。数据驱动模型以装载动作的控制参数和初始料堆状态的高度图作为输入，输出性能或料堆状态的推断结果。通过迭代利用后续料堆状态作为连续预测的初始料堆状态，该预测方法可实现长时域预测。深度神经网络基于三维多体动力学仿真中超过10,000次随机装载动作（不同形状的碎石堆）的数据进行训练。模型预测性能的平均准确率为95%（1.2毫秒内），预测料堆状态的平均准确率为97%（4.5毫秒内）。通过采用料堆状态的斜率与曲率进行低维表示来缩小模型尺寸，可在牺牲部分准确率的同时提升性能预测速度。通过大型料堆上40次连续装载动作验证了长时域预测的可行性。借助基于物理的模型，料堆状态预测在更长时域应用中仍能保持足够精度。