  When robots are able to see and respond to their surroundings, a whole new
world of possibilities opens up. To bring these possibilities to life, the
robotics industry is increasingly adopting camera-based vision systems,
especially when a robotic system needs to interact with a dynamic environment
or moving target. However, this kind of vision system is known to have low data
transmission rates, packet loss during communication and noisy measurements as
major disadvantages. These problems can perturb the control performance and the
quality of the robot-environment interaction. To improve the quality of visual
information, in this paper, we propose to model the dynamics of the motion of a
target object and use this model to implement an Extended Kalman Filter based
on Intermittent Observations of the vision system. The effectiveness of the
proposed approach was tested through experiments with a robotic arm, a camera
device in an eye-to-hand configuration, and an oscillating suspended block as a
target to follow.
