A Mobile Robot to Demonstrate Social Behavior about Queuing

為了要使得機器人更容易被人們所接受，機器人必須理解環境中的人類行為。在不久的將來，機器人將頻繁出現在人類生活環境中，像是學校、醫院、辦公大樓、博物館、以及一般家庭等等區域。而排隊為人類社會最常見的行為之一，所以機器人要融入人類社會中，排隊就變得非常的重要了。 在這篇論文中，了解人類排隊行為主要針對人與人群之間的相互關係作探討，並提出行人排隊模型，可以幫助機器人檢測以及學習人群效應。之後也探討環境中有那些因素會影響排隊的位置，第一個因素為公車站牌，人類在等待公車時，會站立在公車站牌附近，並有排隊的行為出現。利用SIFT演算法來偵測出公車站牌，並與雷射並用找出公車站牌的位置，最後學習人類等候公車。第二個因素為門，提出了一個演算法可偵測柵格地圖中為門的地方。門對於行人是一個交通要道，當排隊隊伍在門附近的情況時，會有禮貌空出一定範圍的距離讓行人可自由通行，藉由空間效應讓機器人了解這些位置是不可以久留的。We are at the developing stage of service robotics now. Robots provide services for people in the public and home environments. Before that, we need to let robots understand the social behaviors in order to letting robots know how to interact with human correctly. Furthermore, maybe we can achieve the step that robots will cooperate with people and join the coexistence society of humans and robots in the future. The way in which robots are going to live with humans has become an important issue. One of the most common human social behaviors is standing in line. Therefore, to integrate robots into human society, queuing is an important ability. In this thesis, behavior understanding mainly targets on a queuing task consisting of the interactions between people and the group. A pedestrian queuing model was developed that can help robots to detect and learn crowd effects in human society. I have added the queuing model environment constraint of entrance detection on an occupancy grid map, and bus stop detection. The entrance detection algorithm detects entrances as represented by all kinds of doors and allows the robot to identify the location as a traffic artery with an SSE, thus facilitating robot behavior that is acceptable in human environment. Using the SIFT algorithm makes the identification of bus stops easier to perform