A Haptic Shared Control Architecture for Tracking of a Moving Object

Zhu, Yuanchao; Yang, Canjun; Tu, Zhangpeng; Ling, Yucheng; Chen, Yanhu

doi:10.1109/tie.2022.3189067

Cited by 3 publications

(3 citation statements)

References 28 publications

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“…trust [17,45,48,57,73,77] physical attribute of the robot [39,70,72] user interface [37,38,42,46,51,60,63,64,73] dynamic of the situation [75] acceptance [17,57,71,77] robot errors [36,55] control modality [37,65,71] task complexity [36,55,66,69,80] human characteristics [39] trajectory and movement [53,55,78] feedback [39,48,51,52,62,72] design of workspace [39,73,77] physical workload [50] role of the robot [61,76,…”

Section: Human Factors Robot Factors Communication Factors Environmen...mentioning

confidence: 99%

“…Additional methods such as gesture recognition [38,39,63,65,72,73], voice control [36,38,[63][64][65], and eye gaze tracking [65,68] were employed, enhancing the versatility of interactions. Haptic feedback [62] and extended reality (XR) interfaces [38,39,46,52,59,60,63] were noted for their immersive and tactile capabilities. Furthermore, EMG-based interfaces [37,39] and Brain-Computer Interfaces (BCIs) [36,39,40] have been adopted, indicating progress in user command and collaboration with robots.…”

Section: User Interfacementioning

confidence: 99%

“…Lemasurier et al, suggested that effective signalling of robot intent, like the LED bracelet, can be a solution [51]. Zhu et al suggested a haptic shared control architecture, which integrates human and robot control with fuzzy logic inference in tracking tasks [62].…”

Section: Potential Solutionsmentioning

confidence: 99%

See 2 more Smart Citations

What Affects Human Decision Making in Human–Robot Collaboration?: A Scoping Review

Liu,

Caldwell,

Rittenbruch

et al. 2024

Robotics

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The advent of Industry 4.0 has heralded advancements in Human–robot Collaboration (HRC), necessitating a deeper understanding of the factors influencing human decision making within this domain. This scoping review examines the breadth of research conducted on HRC, with a particular focus on identifying factors that affect human decision making during collaborative tasks and finding potential solutions to improve human decision making. We conducted a comprehensive search across databases including Scopus, IEEE Xplore and ACM Digital Library, employing a snowballing technique to ensure the inclusion of all pertinent studies, and adopting the PRISMA Extension for Scoping Reviews (PRISMA-ScR) for the reviewing process. Some of the important aspects were identified: (i) studies’ design and setting; (ii) types of human–robot interaction, types of cobots and types of tasks; (iii) factors related to human decision making; and (iv) types of user interfaces for human–robot interaction. Results indicate that cognitive workload and user interface are key in influencing decision making in HRC. Future research should consider social dynamics and psychological safety, use mixed methods for deeper insights and consider diverse cobots and tasks to expand decision-making studies. Emerging XR technologies offer the potential to enhance interaction and thus improve decision making, underscoring the need for intuitive communication and human-centred design.

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