We propose a method by which to assist an operator in switching an internal model by visually presenting information about the dynamic characteristics of the destination in accordance with changes in dynamic characteristics when switching teleoperated hydraulic excavators. Experimental results show that when switching without presentation, the operation time significantly increased by 8:8%, compared with that observed when not switching. However, when using the proposed method, there was no increase in work time owing to switching. Therefore, the proposed method makes it possible to immediately switch the internal model of the operator.

In this paper we consider a composite optimization problem that minimizes the sum of a weakly smooth function and a convex function with either a bounded domain or a uniformly convex structure. In particular, we first present a parameter-dependent conditional gradient method for this problem, whose step sizes require prior knowledge of the parameters associated with the Hölder continuity of the gradient of the weakly smooth function, and establish its rate of convergence. Given that these parameters could be unknown or known but possibly conservative, such a method may suffer from implementation issue or slow convergence. We therefore propose a parameter-free conditional gradient method whose step size is determined by using a constructive local quadratic upper approximation and an adaptive line search scheme, without using any problem parameter. We show that this method achieves the same rate of convergence as the parameter-dependent conditional gradient method. Preliminary experiments are also conducted and illustrate the superior performance of the parameter-free conditional gradient method over the methods with some other step size rules.

The construction industry in Japan has a shortage of workers and skilled operators because of the decrease in the working population due to the low birth rate and aging population. The number of operators of hydraulic excavators, which account for the largest proportion of construction machinery, is also decreasing and it has become difficult to employ operators with expert skills. Teleoperated hydraulic excavators can address this problem. It is difficult, however, to carry out safe and efficient construction work using a teleoperated excavator because of the lack of information available to the operator, such as the posture of the machinery and condition of the work object, in comparison with operating an actual excavator. Therefore, we propose a machine instability feedback system that calculates the attachment posture and digging reaction force and feeds the hydraulic excavator power margin directly back to the operator. This becomes an index of the margin until the excavator body, not the attachment, starts to move by the digging reaction force. We conducted a subject study using an excavator simulator to verify the effect of presenting the machine instability information to the operator. The results confirm that the operator could safely carry out digging work with acceptable work efficiency when machine instability information was provided.

Sense of agency (SoA) has long been a focus area in the field of human-machine interactions, because it is thought to relate to progress and improvements in developing interface systems that users can control with intention. However, there is currently no systematic SoA evaluation method within the context of heavy machinery operation. With our research, we intended to develop a SoA scale for heavy machinery operation (SoAS-HMO). The scale evaluated the psychological experiences of operators while they voluntarily operated heavy machinery. To build this scale, we conducted two surveys: a semi-structured interview survey to initially prepare the scale items, and a questionnaire survey to explore and confirm the scale structure. Subsequent factor analysis revealed one factor and twenty-one items that were related to the SoA experienced during operation, with sufficient reliability and validity, specifically with goodness of fit index (GFI) = 0.933, adjusted GFI (AGFI) = 0.911, comparative fit index (CFI) = 0.909., and Cronbach's a = 0.97. Therefore., this scale can successfully measure the subjective SoA of the operator and be applied to evaluate machinery efficiency. Our study provides progress towards improving heavy machinery systems that can be intentionally controlled by operators.

We developed a system to evaluate the skill of operating a hydraulic excavator using a remotely controlled (RC) excavator and virtual reality (VR) technology. We re-modeled the RC excavator such that it can be operated in the same manner as an actual excavator, and proceeded to measure the excavator's state. To evaluate the skill of operating this system, we calculated several indices from data recorded during excavation work. We calculated the same indices from data recorded during excavation performed by an actual excavator, and verified that there exists a high correlation between the indices of the RC excavator and those of the actual excavator.