利用鍵結圖發展人與機器人安全互動系統

Abstract

此論文將利用鍵結圖發展人機安全互動系統，並且應用在主動-被動變剛性彈性驅動器上。鍵結圖主要用來建立系統模型、設計模型匹配控制器與強健式錯誤偵測與隔離系統。為了將人機安全互動系統導入人因元素，我們利用Kinect偵測人類的骨架點，再利用使用者在空間中的位置與速度參數，來調控驅動器的剛性值，藉此達到人機安全互動的目的。 模型匹配控制器，能夠使受控系統的輸出響應追蹤至欲匹配的模型輸出響應。本論文提出完整鍵結圖模型匹配控制器的設計流程，並且應用在主動-被動變剛性彈性驅動器上。在論文中，模型匹配控制器用來控制受控系統的剛性值，以達到人類即使在與機器人碰撞時，也能夠保護人類不受傷害。若系統中的關鍵元件發生損壞或錯誤，將可能導致整體系統不穩定或發散。為了能夠使系統具有主動自我錯誤偵測，本論文發展強健式錯誤偵測與隔離系統，能夠有效偵測損壞的關鍵元件。並當偵測到元件發生錯誤被偵測後，能夠立即切換至合適的控制策略，以確保人類在安全環境下工作。 本論文提出的人與機器人安全互動系統，已在實際機器人系統上實現，所得結果符合預期。

This dissertation aims to develop an intelligent safe human–robot interaction (sHRI) system and apply it to an active–passive variable stiffness elastic actuator (APVSEA). A Bond graph is used to construct the system model, model matching controller (MMC), and robust fault detection and isolation system. To import the human factor into the sHRI system, Kinect was adopted to detect points on the human skeleton. Human joint positions and their velocities in space are used to dynamically adjust the actuator stiffness for sHRI. MMC can force the output response of a plant to that of a reference. In this study, a complete MMC design flowchart is proposed. Moreover, an MMC was implemented in the APVSEA system. The MMC is used to change a plant stiffness so that if a human collides with a robot, the human will not sustain injuries. If any key system components break or fail, the entire system may destabilize or become divergent. Thus, this study develops a robust fault detection and isolation (RFDI) system for effectively detecting key component faults. When a fault in the system is detected, the RFDI system is switched to a suitable control system to guarantee human safety. In summary, this study proposes an intelligent sHRI system that can vary the stiffness of a plant and detect fault components. Furthermore, by importing the human factor, the sHRI system becomes even more smart.