A framework of state identification for operational support based on task-phase and attentional-condition identification

This paper proposes a state identification framework to support the complicated dual-arm operations in construction work. The operational support in construction machinery filed requires the compatibility with different types of support and the commonality among various operator skill levels. The proposed framework is therefore organized into two functions: real-time task phase identification and time-series attentional condition identification. The task phase is defined by utilizing the joint load applied according to the environment constraint condition. The attentional condition is defined as one of the internal work-state condition classified by the necessity level of operational support, and is dependent on the vectorial or time-series date selected by the identified task phase. Experiments are conducted using the hydraulic dual arm system to perform transporting and removing tasks. Results show that the number of error contacts, internal force applied, and mental workload is decreased without time-consumption increase. The result confirmed that the proposed framework greatly contribute to improving each operator's work performance.