Our laboratory was one of the first laboratories to exploit data
parallelism in real-time computer vision. We have been responsible for
the first set of
low-level image processing algorithms written for the PIPE dataflow
architecture and these algorithms have been a part of our technology
transfer program to companies such as Siemens, Lockheed, FMC, and
North American Philips. Machine vision has become an important
research topic as the systems currently being developed are finally
capable of processing rates to sustain real-time control from visual
feedback. Below we discuss 3 current projects that involve real-time
machine vision.
Tracking and Grasping Moving Objects
We are interested in exploring the interplay of hand-eye coordination
for dynamic grasping tasks where objects to be grasped are moving.
Coordination between an organism's sensing
modalities and motor control system is a hallmark of intelligent
behavior, and we are pursuing the goal of building an integrated
sensing and actuation system that can operate in dynamic as opposed to
static environments. The system we are building is a multi-sensor
system that integrates work in
real-time vision, robotic arm control and stable grasping of objects.
Our first attempts at this have
resulted in a system that can track and stably grasp a moving model
train in real-time \cite{alle90,atym92,alle93a} (see Figure
\ref{graspseq}).
The algorithms we have
developed are quite general and
applicable to a variety of complex robotic tasks that require visual
feedback for arm and hand control. Currently, we are extending this
work to tracking in a full 3-D space, and instituting 2
new control algorithms that employ multiple moving object detection
and a wrist mounted camera for finer tracking and grasping. This work
is important in showing 1) that the current level of computational
devices for vision and real-time control are sufficient for dynamic
tasks that include moving objects, 2) optic-flow is robust enough to
be computed in real-time for stereo matching and 3) it can define
strategies for robotic grasping with visual feedback that may be
motivated by human arm movement studies.
