Visual servoing allows to control the motion of a robot using
information from its visual sensors to achieve manipulation tasks.
In this work we design and implement a robust visual servoing framework
for reaching and grasping behaviours for a humanoid service robot
with limited control capabilities. Our approach successfully exploits a
5-degrees of freedom manipulator, overcoming the control limitations of
the robot while avoiding singularities and stereo vision techniques. Using
a single camera, we combine a marker-less model based tracker for
the target object, a pattern tracking for the end-effector to deal with
the robot’s inaccurate kinematics, and alternate pose based visual servo
technique with eye-in-hand and eye-to-hand configurations to achieve a
fully functional grasping system. The overall method shows better results
for grasping than conventional motion planing and simple inverse kinematics
techniques for this robotic morphology, demonstrating a 48.8% of
increment in the grasping success rate.