Adaptive Excavation Automation in Complex Soil Environments Using Reinforcement Learning

This paper presents an advanced excavation automation framework that improves trajectory efficiency and improves operational stability in diverse and complex soil conditions. The framework integrates the fundamental equation of earthmoving (FEE) with the proximal policy optimization (PPO) algorithm to enable adaptive trajectory planning during excavation. Using these estimates, the PPO algorithm iteratively optimizes excavation strategies to minimize applied force and improve trajectory efficiency, contributing to potential energy savings. The validation of the proposed framework is performed through simulations conducted under varying soil conditions and initial height configurations. The results show an 18.1% reduction in total excavation resistance compared to baseline models, achieving a mean absolute error (MAE) of 0.065 m. These findings confirm the effectiveness of the framework in reducing excavation resistance and improving task precision in simulated environments.