Soil-Adaptive Autonomous Excavation: Bulking Factor-Based Soil Density Estimation and Excavation Path Optimization with a Genetic Algorithm

In this study, we present a novel autonomous excavation method that achieves high efficiency under varying soil conditions. This method consists of two main steps, including first estimating the density of the soil and then generating an optimal excavation path based on the estimated density. The proposed method estimates soil density by taking advantage of the bulking phenomenon, which refers to an increase in the volume of excavated soil. This estimation relies solely on 3D point-cloud data obtained before and after excavation. Using the estimated soil density, an optimal excavation path is generated by applying a genetic algorithm in a physics simulator that replicates both the hydraulic excavator and the target ground. The algorithm explores a range of paths over multiple generations to find one that maximizes efficiency. The effectiveness of the proposed method was verified through simulations and field experiments. In particular, field experiments conducted in soft soil showed that the proposed method improved excavation efficiency by 27.7% compared with a baseline method using fixed parameters.