Haptics-based force balance controller for tower crane payload sway controls
Anti-sway control is an important issue affecting the safety and efficiency of tower crane operation, but the role of the human operator in this control loop is largely unknown. This paper proposes and designs a force-feedback based control method for anti-sway control. The system connects the tips...
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Published in | Automation in construction Vol. 144; p. 104597 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Elsevier B.V
01.12.2022
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Subjects | |
Online Access | Get full text |
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Summary: | Anti-sway control is an important issue affecting the safety and efficiency of tower crane operation, but the role of the human operator in this control loop is largely unknown. This paper proposes and designs a force-feedback based control method for anti-sway control. The system connects the tips of two haptic devices by a 3D printed pole and uses it to provide the balance status of the payload. The sway error is represented by the position and rotation changes of the pole. Meanwhile, the operator can use this haptic controller to adjust the payload pose by applying the counterbalance force to the pole. A human-subject experiment (n = 34) was performed to test the comparative benefits of the proposed method. The results show that the proposed haptics-based force balance control method outperformed the automatic method in both performance and subjective evaluations. The findings inspire the design of new human-in-the-loop approaches for heavy machine stability controls.
•This paper presents a new haptic controller for tower crane antisway controls.•Two 6-DOF haptic devices are connected via a pole to reproduce the feeling of sway errors.•Operator can use this haptic controller to adjust the payload pose by applying the counterbalance force.•A human-subject experiment (n = 34) was performed to test the benefits of the proposed system.•The system outperformed the automatic and traditional methods in both performance and self reported metrics. |
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ISSN: | 0926-5805 1872-7891 |
DOI: | 10.1016/j.autcon.2022.104597 |