Tracking a moving object through several frames, provided changes from frame to frame are on the order of +-(10 + "X Range") pixels in the X direction and +-(10 + "Y Range") in the Y direction is done automatically because of a relatively large area of exploration during the search for an optimal (new) position for a particular control point and a very strong force exerted by large values of the image gradient.
In this paper we describe a control methodology for
catching a fast moving object with a robot manipulator,
where visual information is employed to track the
trajectory of the target. Sensing, planning and control
are performed in real-time to cope with possible
unpredictable trajectory changes of the moving target,
and prediction techniques are adopted to compensate the
time delays introduced by visual processing and by the
robot controller. A simple but reliable model of the
robot controller has been taken into account in the
control architecture for improving the performance of the
system. Experimental results have shown that the robot
system is capable of tracking and catching an object
moving on a plane at velocities of up to 700 mm/s and
accelerations of up to 1500 mm/s2.
This paper studies the problem of tracking a ballistic object in
the reentry phase by processing radar measurements. A suitable
(highly nonlinear) model of target motion is developed and the
theoretical Cramer—Rao lower bounds (CRLB) of estimation
error are derived. The estimation performance (error mean and
This paper studies the problem of tracking a ballistic object in
the reentry phase by processing radar measurements. A suitable
(highly nonlinear) model of target motion is developed and the
theoretical Cramer—Rao lower bounds (CRLB) of estimation
error are derived. The estimation performance (error mean and
This paper studies the problem of tracking a ballistic object in
the reentry phase by processing radar measurements. A suitable
(highly nonlinear) model of target motion is developed and the
theoretical Cramer—Rao lower bounds (CRLB) of estimation
error are derived. The estimation performance (error mean and
