- 學位論文
移動機器人之認知結構：人類行為與意圖的研究
Cognitive Architecture for Mobile Robots: Studies of Human Behavior and Intention
摘要
隨著科技的進步，社交機器人開始逐步進入人們的生活。目前許多研究團隊致力於讓機器人適應環境、了解人類做事方式並且與人們合作做事。為了要讓機器人能夠更進一步讓人們接受，機器人需要具備理解人類行為的能力。 本論文目的旨在發展一套機器人具備人類行為認識與意圖認知的認知架構，並使用上述資訊讓機器人自主行動。在人類行為中，我們提出了多模態情緒辨識系統、人類行為認知地圖。情緒辨識系統中，我們結合臉部與身體資訊並將其結合給予更加穩定的辨識結果，以此理解人類的情緒。在人類行為認知地圖中，記錄且辨識人類在特定地點常有的行為，紀錄空間與人類行為的關係。藉著於意圖偵測中觀察人類注視方向與距離、速度等觀測值來建立對話時候的意圖模型。最後在機器人的行動規劃中，使用深度增強學習演算法學習空間影響與人類行為認知地圖的資訊，並給予機器人自主行走的能力。 最後本論文展示結合上述各功能的認知架構之演算法。使得機器人能夠展現出預測行人意圖並主動給予協助的能力，同時機器人也能表現出符合社會規範的行為。
並列摘要
In recent years, social robotics have developed and entered our lives. In fact, social robotics are acting more and more like humans. Many researchers are focused on developing different behaviors which can let robots adapt to various environments, better understand human decisions, and further collaborate with people. To reach this goal and have the robot be more easily accepted as part of human society, robots must have the ability to understand human behavior. This dissertation attempts to develop a cognitive architecture which combines the understanding of human behaviors and intentions. In the human behavior understanding module, a multimodal emotion recognition system and a human behavior cognitive map are proposed. The multimodal emotion system is built by fusing facial and bodily information to achieve a more stable recognition result. In the human behavior cognitive map, many human actions are recognized and record relationships between space and human actions in a specific environment. Furthermore, the intention model during conversation is established by observing the human gaze direction, distance between human and robot, and walking velocity toward the robot. The motion planning method is built by deep reinforcement understanding, which learns the spatial effective and human behavior cognitive map, giving the robot autonomous walking ability. Finally, the experiment demonstrates the cognitive architecture created by combining the above functions. As a result, the robot exhibits the ability to understand human intentions and offers to actively help with people’s needs, and the robot can also behave in accordance with social norms.
參考文獻
延伸閱讀
- Cheng, C. Y. (2020). 機器人的人類群體行為與人類移動認知地圖建構 [master's thesis, National Taiwan University]. Airiti Library. https://doi.org/10.6342/NTU202003428
- Yu, Y. C. (2008). 基於環境與人類行為學習之機器人運動控制 [master's thesis, National Taiwan University]. Airiti Library. https://doi.org/10.6342/NTU.2008.01447
- 謝亞錚（2008）。機器人輔助程式設計學習之學習成效與學生心智模型探討〔碩士論文，國立臺灣師範大學〕。華藝線上圖書館。https://www.airitilibrary.com/Article/Detail?DocID=U0021-0804200910294039
- Hentschel, M., & Wagner, B. (2011). An Adaptive Memory Model for Long-Term Navigation of Autonomous Mobile Robots. Journal of Robotics, 2011(), 188-196. https://doi.org/10.1155/2011/506245
- 張文龍（2007）。Design and Implementation of Human-Robot Interaction Control for an Autonomous Mobile Robot〔碩士論文，中原大學〕。華藝線上圖書館。https://doi.org/10.6840/cycu200700522