GENERATION OF EXCAVATION BEHAVIOR OF EXCAVATOR USING REINFORCEMENT LEARNING

Abstract

In recent years, efforts have been made to improve the productivity of construction work using information technology. However, since humans operate the construction equipment with machine control and machine guidance, manpower has not been saved. Therefore, if autonomous control of construction equipment by reinforcement learning is possible, it will be possible to reduce manpower by automatic construction. In this study, a reinforcement learning algorithm, PPO, was used to generate drilling motions by an agent assuming a excavator. As a result, the motion was successfully generated and at the same time, the speed of the motion was increased and the amount of drilling was maximized. In addition, the future prospects of civil engineering construction by reinforcement learning are described based on the findings.