Development of operator support system with primitive static states for intelligent construction machinery

Double-front construction machinery (DFCM), which has been developed for adaptation to complicated work, demands higher operational skills to control double fronts with more multiple joints. To handle DFCM skillfully for performing more complicated works, intelligent functions that can autonomously distinguish between diverse states and also provide informational and operational supports to their operators are inevitably required. In particular, a state identification method strongly requires high reliability and robustness due to the complexity of the construction work environment and the variety of the operator's skill level. However, most of the current construction machinery has unique functions that only reproduce the movements that are originated by the operator. In this study, which focuses on DFCM, we addressed the need for a new conceptual design of an operator support system (OSS) and evaluated it using our newly developed simulator. Our experimental results showed that the OSS improves the work performance, including decreasing the operational time for completing a task, reducing the mental workload on the operators, and the number of error operations.