Working with movable obstacles using on-line environment perception reconstruction using active sensing and color range sensor

We propose a strategy for a robot to operate in an environment with movable obstacles using only onboard sensors, with no previous knowledge of the objects in that environment. Movable obstacles are detected using active sensing and a color range sensor, and when an obstacle is moved, the perception of the environment is reconstructed. Active sensing is defined as the classification of an object as either movable or static after the robot tries to push the object using its arm. This classification is collectively based on force sensor inputs, joint angles, and color range sensor inputs. In order to gather information from the environment, we use a color range sensor consisting of a TOF (Time of Flight) range sensor and conventional stereo cameras. Finally, we show experimental result in the environment with movable obstacles such as a table and chairs. Humanoid robot HRP-2 detects that a chair is a movable obstacle, moves the chair to clear a path to its goal, and then reaches the goal.