Methods for Environment Recognition based on Active Behaviour Selection and Simple Sensor History

The ability to operate in a variety of environments is an important topic in humanoid robotics research. One of the ultimate goals of this research is smooth operation in everyday environments. However, movement in a real-world environment such as a family's house is challenging because the viscous friction and elasticity of each floor, which directly influence the robot's motion and are difficult to immediately measure, differ from place to place. There has been a lot of previous research into ways for the robots to recognize the environment. For instance, Fennema et al. (Fennema et al., 1987) and Yamamoto et al. (Yamamoto et al., 1999) proposed environment recognition methods based on range and visual information for wheeled robot navigation. Regarding humanoid robots, Kagami et al. (Kagami et al., 2003) proposed a method to generate motions for obstacle avoidance based on visual information. They measured features of the environment precisely before moving or fed back sensor information to a robot's controller with a short sampling period. It is still difficult to measure the viscous friction or elasticity of the floor before moving or by using short term sampling data, and they did not deal with such features. Thus, we propose a method for recognizing the features of environments and selecting appropriate behaviours based on the histories of simple sensor outputs, in order to achieve a humanoid robot able to move around a house. Figure 1 shows how our research differs from previous research according to length of the sensor history and number of types of sensors. The key idea of our method is to use a long sensor history to determine the features of the environment. To measure such features, almost all previous research (Shats et al., 1991; Holweg et al., 1996) proposed methods that used several kinds of sensors with a large amount of calculations to quickly process the sensor outputs. However, such approaches are unreasonable because the robot lacks sufficient space on its body for the attached sensors and processors. Hence we propose using sensor history to measure them because there are close relationships between sensor histories, motions, and environments. When the robot performs specific motions in specific environments, we can see those features in the sensor history that describe the motion and the environment. Furthermore, such features as viscous friction or floor elasticity do not change quickly. Thus we can use a long history of sensor data to measure them.