Advancements and challenges in earthmoving equipment automation using core technologies

Abstract

Earthmoving equipment automation is limited by interactions with complex media, unstructured terrain and highly variable environmental conditions. Although research in this field has continued for nearly thirty years, a truly fully autonomous system remains unrealized. This paper examines automation technologies for earthmoving equipment in typical repetitive short-cycle loading operations and presents a systematic survey of core literature spanning over thirty years. Key techniques are grouped into four categories, environmental perception, path planning and tracking, autonomous loading and unloading and safety and risk assessment. The analysis indicates that current research primarily addresses improvements to individual stages or subsystems while system level optimization of the entire workflow is still lacking. Moreover, data physics hybrid methods show strong potential in earthmoving equipment automation applications by compensating for errors in physical models and improving the generalization of data driven models. Finally, the major challenges are summarized and directions for future research are proposed.