Novel Hybrid Physics‑Informed Deep Neural Network for Dynamic Load Prediction of Electric Cable Shovel

Electric cable shovel(ECS)is a complex production equipment,which is widely utilized in open-pit mines.Rational valuations of load is the foundation for the development of intelligent or unmanned ECS,since it directly influences the planning of digging trajectories and energy consumption.Load prediction of ECS mainly consists of two types of methods:physics-based modeling and data-driven methods.The former approach is based on known physical laws,usually,it is necessarily approximations of reality due to incomplete knowledge of certain processes,which introduces bias.The latter captures features/patterns from data in an end-to-end manner without dwelling on domain expertise but requires a large amount of accurately labeled data to achieve generalization,which introduces variance.In addition,some parts of load are non-observable and latent,which cannot be measured from actual system sensing,so they can’t be predicted by data-driven methods.Herein,an innovative hybrid physics-informed deep neural network(HPINN)architecture,which combines physics-based models and data-driven methods to predict dynamic load of ECS,is presented.In the proposed framework,some parts of the theoretical model are incorporated,while capturing the difficult-to-model part by training a highly expressive approximator with data.Prior physics knowledge,such as Lagrangian mechanics and the conservation of energy,is considered extra constraints,and embedded in the overall loss function to enforce model training in a feasible solution space.The satisfactory performance of the proposed framework is verified through both synthetic and actual measurement dataset.

Toward autonomous mining:design and development of an unmanned electric shovel via point cloud-based optimal trajectory planning

With the proposal of intelligent mines,unmanned mining has become a research hotspot in recent years.In the field of autonomous excavation,environmental perception and excavation trajectory planning are two key issues because they have considerable influences on operation performance.In this study,an unmanned electric shovel(UES)is developed,and key robotization processes consisting of environment modeling and optimal excavation trajectory planning are presented.Initially,the point cloud of the material surface is collected and reconstructed by polynomial response surface(PRS)method.Then,by establishing the dynamical model of the UES,a point to point(PTP)excavation trajectory planning method is developed to improve both the mining efficiency and fill factor and to reduce the energy consumption.Based on optimal trajectory command,the UES performs autonomous excavation.The experimental results show that the proposed surface reconstruction method can accurately represent the material surface.On the basis of reconstructed surface,the PTP trajectory planning method rapidly obtains a reasonable mining trajectory with high fill factor and mining efficiency.Compared with the common excavation trajectory planning approaches,the proposed method tends to be more capable in terms of mining time and energy consumption,ensuring high-performance excavation of the UES in practical mining environment.