Abstract:In sandblasting tasks for complex steel structure maintenance, teleoperation is required to keep humans away from occupational risk and hazard. On the other hand, teleoperation typically degrades system‐human performances, resulting in poor product quality and must be designed such that the performances remain as high as possible. However, designing the teleoperation system regarding to a single performance measure may lead to an improper design. In this article, we propose two novel loss‐function‐based human‐… Show more
“…A tele-abrasive system is a shared-control system where an operator and an automated machine operate the system together [14,15]. It is a teleoperation system that mainly consists of two main components, a human operator and a tele-abrasive machine, as shown in Figure 1.…”
Section: Tele-abrasive Operationmentioning
confidence: 99%
“…However, in teleoperation tasks, although human arm movement is a main part of a shared-control system, human movement error has received very little research attention. In this general area, [14] proposed an adaptive network-based fuzzy inference system (ANFIS) model to control human arm movement velocity in a gas tungsten arc welding task. Furthermore, [20] considered the problem of the physical interface between a human and a computer, which requires an approach to help the human operator perform a hand movement task in a more comfortable and, hence, more reliable way.…”
Section: Tele-abrasive Operationmentioning
confidence: 99%
“…where ŷ−t 1 is the predicted current state. Then, the confidence of the prediction, P −t , is estimated by (14):…”
Section: Using Linear Estimation With the Kalman Filtermentioning
In a tele-abrasive task, it is principally human arm movements that cause variation in the position of the abrasive nozzle, thereby resulting in high operating costs and low productivity. It is difficult to design a system that can minimize the variation that accrues from operators behaving differently, which is difficult to predict. Although skilled operators can reduce this variation, becoming a skillful operator requires a lengthy training period. In this work, a two-stage variation streaming technique was used to extract variation sources in a tele-abrasive system. Furthermore, we propose an integrated human–computer approach to control variation in these systems—an approach that applies an innovative human arm movement pattern incorporated with a Kalman filter into a standard system. A virtual tele-abrasive system was used to validate our approach. Furthermore, compared with conventional systems, the proposed approach will help operators to perform abrasive tasks more comfortably and require a shorter training period.
“…A tele-abrasive system is a shared-control system where an operator and an automated machine operate the system together [14,15]. It is a teleoperation system that mainly consists of two main components, a human operator and a tele-abrasive machine, as shown in Figure 1.…”
Section: Tele-abrasive Operationmentioning
confidence: 99%
“…However, in teleoperation tasks, although human arm movement is a main part of a shared-control system, human movement error has received very little research attention. In this general area, [14] proposed an adaptive network-based fuzzy inference system (ANFIS) model to control human arm movement velocity in a gas tungsten arc welding task. Furthermore, [20] considered the problem of the physical interface between a human and a computer, which requires an approach to help the human operator perform a hand movement task in a more comfortable and, hence, more reliable way.…”
Section: Tele-abrasive Operationmentioning
confidence: 99%
“…where ŷ−t 1 is the predicted current state. Then, the confidence of the prediction, P −t , is estimated by (14):…”
Section: Using Linear Estimation With the Kalman Filtermentioning
In a tele-abrasive task, it is principally human arm movements that cause variation in the position of the abrasive nozzle, thereby resulting in high operating costs and low productivity. It is difficult to design a system that can minimize the variation that accrues from operators behaving differently, which is difficult to predict. Although skilled operators can reduce this variation, becoming a skillful operator requires a lengthy training period. In this work, a two-stage variation streaming technique was used to extract variation sources in a tele-abrasive system. Furthermore, we propose an integrated human–computer approach to control variation in these systems—an approach that applies an innovative human arm movement pattern incorporated with a Kalman filter into a standard system. A virtual tele-abrasive system was used to validate our approach. Furthermore, compared with conventional systems, the proposed approach will help operators to perform abrasive tasks more comfortably and require a shorter training period.
“…The preferable outcomes of the task is to ensure that the surface is completely clean as fast as possible. To do so, the width of the blasting path is set to be equivalent to or slightly smaller than the blasting spot size to achieve optimal operating time and cost [6], shown in Fig. 2.…”
In tele-sandblasting task, human arm movement is a critical source of producing variation in position of sandblasting nozzle resulting in high operating cost and low productivity. Each operator behaves differently leading to unpredictable movements. Skilled operators are able to reduce the variation; however, developing skills requires a training period. In this paper, we proposed a new approach which is the use of a novel operator's arm movement pattern incorporated with a Kalman filter to reduce the effect of human-arm movement error. A virtual tele-sandblasting system is used to validate our approach. The experimental results verify that our proposed approach is able to significantly reduce the effect of human arm movement error. The approach helps operators to perform the task more comfortably and takes short training time.
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