Visual Guided Approach-to-grasp for Humanoid Robots

Vision based control for robots has been an active area of research for more than 30 years and significant progresses in the theory and application have been reported (Hutchinson et al., 1996; Kragic & Christensen, 2002; Chaumette & Hutchinson, 2006). Vision is a very important non-contact measurement method for robots. Especially in the field of humanoid robots, where the robot works in an unstructured and complex environment designed for human, visual control can make the robot more robust and flexible to unknown changes in the environment (Hauck et al., 1999). Humanoid robot equipped with vision system is a typical hand-eye coordination system. With cameras mounted on the head, the humanoid robot can manipulate objects with his hands. Generally, the most common task for the humanoid robot is the approach-to-grasp task (Horaud et al., 1998). There are many aspects concerned with the visual guidance of a humanoid robot, such as vision system configuration and calibration, visual measurement, and visual control. One of the important issues in applying vision system is the calibration of the system, including camera calibration and head-eye calibration. Calibration has received wide attentions in the communities of photogrammetry, computer vision, and robotics (Clarke & Fryer, 1998). Many researchers have contributed elegant solutions to this classical problem, such as Faugeras and Toscani, Tsai, Heikkila and Silven, Zhang, Ma, Xu. (Faugeras & Toscani, 1986; Tsai, 1987; Heikkila & Silven, 1997; Zhang, 2000; Ma, 1996; Xu et al., 2006a). Extensive efforts have been made to achieve the automatic or self calibration of the whole vision system with high accuracy (Tsai & Lenz, 1989). Usually, in order to gain a wide field of view, the humanoid robot employs cameras with lens of short focal length, which have a relatively large distortion. This requires a more complex nonlinear model to represent the distortion and makes the accurate calibration more difficult (Ma et al., 2003). Another difficulty in applying vision system is the estimation of the position and orientation of an object relative to the camera, known as visual measurement. Traditionally, the position of a point can be determined with its projections on two or more cameras based on epipolar geometry (Harley & Zisserman, 2004). Han et al. measured the pose of a door knob relative to the end-effector of the manipulator with a specially designed mark attached on the knob