Since the 1990s, the automated process within open-pit mines has been rapidly advanced. However, mineral transportation, as the most costly and dangerous production process, has not witnessed efficient achievements. Adverse weather conditions and complex work environments are two critical bottlenecks that impede the development and deployment of autonomous transportation of open-pit mines. To alleviate this issue, this research proposes a novel paradigm, named IMAPeM, designed to enable safe and efficient autonomous transportation with humans in the loop. IMAPeM includes three categories of miners: 1) biological miners; 2) digital miners; and 3) robotic miners, as well as three operational modes: 1) autonomous model (AM); 2) parallel model (PM); and 3) expert/emergency model (EM). IMAPeM employs these miners and modes depend on the complexity of the task, optimizing the utilization of digital and robotic miners while reducing the workload for biological miners. In addition, we developed the YUGONG system based on IMAPeM. The empirical implementation demonstrates the exceptional performance of the YUGONG system in autonomous transportation across diverse open-pit mines. This system contributes to the advancement of sustainable mining practices, which also carries profound significance for achieving long-term environmentally responsible mining operations.

We describe a technique to generate imagery with improved sharpness for individuals having refractive vision problems. Our method can reduce their dependence on corrective eyewear. It also benefits individuals with normal vision by improving visual acuity at a distance and of small details. Our approach does not require custom hardware. Instead, the calculated images can be shown on a standard computer display, on printed paper, or superimposed on a physical scene using a projector. Our technique uses a constrained total variation method to produce a deconvolution result which, upon observation, appears sharp at the edges. We introduce a novel relative total variation term that enables controlling ringing reduction, contrast gain, and sharpness. The end result is the ability to generate sharper appearing images, even for individuals with refractive vision problems including myopia, hyperopia, presbyopia, and astigmatism. Our approach has been validated in simulation, in camera-screen experiments, and in a study with human observers.