  Visual servoing is a well-known task in robotics. However, there are still
challenges when multiple visual sources are combined to accurately guide the
robot or occlusions appear. In this paper we present a novel visual servoing
approach using hybrid multi-camera input data to lead a robot arm accurately to
dynamically moving target points in the presence of partial occlusions. The
approach uses four RGBD sensors as Eye-to-Hand (EtoH) visual input, and an
arm-mounted stereo camera as Eye-in-Hand (EinH). A Master supervisor task
selects between using the EtoH or the EinH, depending on the distance between
the robot and target. The Master also selects the subset of EtoH cameras that
best perceive the target. When the EinH sensor is used, if the target becomes
occluded or goes out of the sensor's view-frustum, the Master switches back to
the EtoH sensors to re-track the object. Using this adaptive visual input data,
the robot is then controlled using an iterative planner that uses position,
orientation and joint configuration to estimate the trajectory. Since the
target is dynamic, this trajectory is updated every time-step. Experiments show
good performance in four different situations: tracking a ball, targeting a
bulls-eye, guiding a straw to a mouth and delivering an item to a moving hand.
The experiments cover both simple situations such as a ball that is mostly
visible from all cameras, and more complex situations such as the mouth which
is partially occluded from some of the sensors.
