人機協作 (HRC) 對於實現多品種、小批量 (HVLV) 製造系統至關重要。然而，在 HVLV 系統中，產品裝配發生重大變化期間，及時有效調整操作員工序，極具挑戰。此外，機器人很難識別人類的動態行為並快速適應協作需求。為了解決這些問題，本研究提出了一種多模式多模態賦能機制，旨在提高 HVLV 裝配環境的直觀性和團隊流暢性。首先，我們引入一個隱藏的半馬爾可夫模型（HSMM），它將不同的裝配速度（可見狀態）與不同的操作員風險感知（隱藏狀態）聯繫起來。協作機器人可以使用該模型，根據預測的人類行為來調整其活動。同時，本研究提出了一種基於增強現實 (AR) 的裝配資訊顯示裝置，藉由即時提供檢測對象之裝配資訊，以增強操作員的組裝能力。前述技術的效率和腦力負荷影響，本研究分別使用 HRC 團隊流暢度測量和 NASA 任務負荷指數 (NASATLX) 工具來衡量。一組參與者通過四種裝配場景，進行驗證：手動裝配、基本 HRC、HSMM 輔助 HRC 以及 HSMM 和 AR 輔助 HRC 裝配。研究結果表明，利用 HSMM 和 AR 輔助的 HRC 具有顯著的優勢。本研究顯示，裝配活動的完成速度提高了 13%，人類和機器人的周期時間和空閒時間亦有效降低。此外，NASATLX數據表明操作人員心智負擔減少，組裝活動完成的輕鬆度增加。這些團隊流暢性的提高和精神壓力的減輕，證明了 HSMM 和 AR 輔助的 HRC 應用，極具潛力。

Human-robot collaboration (HRC) is critical in enabling high-variety, and low-volume (HVLV) manufacturing systems. However, in HVLV settings, efficiently tweaking the operator's function during significant shifts in product assembly generates challenges. At the same time, robots struggle to identify dynamic human behaviours and adapt quickly to collaboration needs. To address these concerns, this research presents a multimodal empowerment tool aimed at improving intuitiveness and team fluency in HVLV assembly scenarios. To begin, we introduce a hidden semi-Markov model (HSMM) that links different assembly speeds (operator visible states) with different operator risk perceptions (hidden states). The robot can adapt its activities based on predicted human behaviours using this model. Simultaneously, the study proposes an augmented reality (AR)-based assembly information display powered by object detection to empower operators. The efficiency and mental workload effects of these technologies are measured using HRC team fluency measures and the NASA Task Load Index (NASATLX) tool, respectively. A set of participants works through four assembly scenarios: manual, basic HRC, HSMM-assisted HRC, and HSMM and AR-assisted HRC assembly. The findings show that utilizing HSMM and AR-assisted HRC has substantial advantages. Assembly activities were completed 13% faster, with reduced cycle times and idle times for both humans and robots. Furthermore, the NASATLX responses suggested a reduction in mental workload and an increase in the ease with which assembly activities were completed. These gains in team fluency and reduced mental stress show the HSMM and AR-assisted HRC applications' significant potential.

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