Excavators are crucial heavy equipment on construction sites, performing diverse earthwork tasks. The construction worksite is experiencing a labor shortage due to an aging workforce. Training new operators requires significant time and resources. Furthermore, the construction environment is hazardous, with a higher rate of excavator-related accidents. Autonomous excavators offer an effective solution by reducing the need for operators in risky environments and substituting skilled workers. Trajectory planning algorithms are vital for autonomous excavators, with skilled operators’ paths serving as important references. However, many studies do not adequately consider skilled operators’ methods or the actual excavation environment. This paper introduced a rule-based algorithm for excavation trajectory planning using terrain data. Based on analysis results of skilled operators’ paths, the proposed algorithm categorizes the excavation process into three stages, depending on the usage rate of the excavator"s joints. Terrain data were derived by projecting point clouds from a stereo depth camera onto a side plane. The path was modified if the excavation volume exceeded a set limit to avoid excessive load. The algorithm was tested with a 30-ton excavator, demonstrating validation of operability and efficiency similar to that of skilled operators.

Research on the automation of many types of construction equipment, including motor graders, is being actively conducted. In a motor grader cabin, the operator has difficulty observing the working environment because of a constructed field of view. Thus, workers rely on their experience and senses. Further, the working environment of the blade must be observed, and a control algorithm should be created to enable autonomous operation. In this study, a blade rotation control strategy considering the soil distribution was proposed. First, a co-simulation environment was constructed using RecurDyn for multibody dynamics analysis and EDEM for discrete element method simulation, and simulations were performed to determine the correlation between soil distribution and the blade rotation angle. Work quality and blade load were analyzed according to the simulation results. The optimal blade rotation angle according to soil distribution was obtained to develop the strategy for autonomous flattening and scattering work. The proposed control strategy was implemented in a 1/4 full-scale motor grader experimental setup. An experiment to evaluate work quality was conducted to validate the effectiveness of the proposed methods. The experimental results indicated that the proposed strategy effectively performed scattering work.