Application of Reinforcement Learning to Excavation Path Generation for Autonomous Excavator

In recent years, construction demand has increased although the number of construction workers has decreased. To solve this problem, The initiative of Digital Transformation (DX) has been active, and research and development on automation of hydraulic excavators has been carried out. It is difficult to set the operation in advance to excavate a stable amount of soil safely because the quality and shape of soil vary. It is expected that excavation paths can be generated according to the quality and shape of the soil by adapting reinforcement learning to the generation of excavation paths for autonomous excavators. This paper describes the results of applying reinforcement learning to the generation of excavation paths for autonomous excavator.