|
Advances in Vision-Guided Robots
A robot searches for a potential explosive device at a noisy arcade.
Image: Sandia National Laboratories
For years, vision-guided robots have been a mainstay for
manufacturing and assembly tasks such as inspecting and sorting parts.
These operations tend to be carried out in highly constrained, structured
environments with zero obstacles. However, advances in processing power
and sensor technologies are now enabling robots to undertake more
unstructured tasks such as autonomous flying, driving, and mobile service
activities, which require better vision systems to identify and avoid
obstacles.
“For example, vision-based systems are now used for detecting and
tracking people and vehicles for driving safety and self-driving systems”,
63
says Martial Hebert, director of Carnegie Mellon University’s Robotics
Institute. “Progress is being made in the fundamental approaches, as well
as supporting technologies such as computing and high-quality sensing.
Personal robotics are developing increasing interaction capabilities. The
overall trend is toward closer collaboration between robots and humans”.
Cutting-Edge Research
Sandia National Laboratories (SNL) is conducting extensive research
in the field of telerobotics, where a typical application would be a human
robot operator who relies on the robot’s onboard cameras to convey a sense
of presence at the remote location. “However, cameras on pan-tilt units are
in many ways a poor replacement for human vision”, says Robert J.
Anderson, principle member of the Robotic and Security Systems
Department at SNL. “Human eyesight has better resolution, a very large
peripheral field of view, and the ability to make quick glances to fill in a
mental model of a space. When operators work with standard cameras,
they tend to get tunnel vision, and rapidly forget the locations of objects
just out of the field of view”.
To improve this situation, SNL researchers have combined live
vision with constructed 3D models of a world to enhance a remote
operator’s sense of space. By using gaming technology, such as the Kinect
sensor from Microsoft, they can scan and create a model of a remote
site. Rather than a single 2D display camera view, the operator can view
the remote robot from any direction, much like first-person shooter video
game, providing either an over-the-shoulder view of an avatar, a tracking
view, or a world view. “This drastically reduces the problem of tunnel
vision in operating remote robots”, says Anderson.
Although GPS has become cheap enough and reliable enough to
enable navigation in a collision-free space, there is always the potential for
collision during mobile, ground-based operations. However, a new
generation of inexpensive 3D vision systems will make it possible for
robots to navigate autonomously in cluttered environments, and to
dynamically interact with the human world.
This was recently demonstrated by the Singapore-MIT Alliance for
Research and Technology (SMART), which constructed a self-driving golf
64
cart that successfully navigated around people and other obstacles during a
test run at a large public garden in Singapore. The sensor network was built
from off-the-shelf components.
“If you have a simple suite of strategically placed sensors and
augment that with reliable algorithms, you will get robust results that
require less computation and have less of a chance to get confused by
‘fusing sensors,’ or situations where one sensor says one thing and another
sensor says something different”, says Daniela Rus, professor of electrical
engineering and computer science at MIT and team leader.
Manufacturing Transformation
No industry has been more transformed by vision-guided robots than
manufacturing. The earliest robots were designed for simple pick-and-
place operation. Now, with technologic advances in sensors, computing
power, and imaging hardware, vision-guided robots are much more
functional and greatly improve product quality, throughput, and
operational efficiency.
According to an article by Axium Solutions, a manufacturer of
vision-guided robotic solutions for material handling and assembly, on
roboticstomorrow.com, “Enhanced computing power helps developers
create more robust and complex algorithms. Improvements in pattern
matching and support for 3D data enabled new applications like random
bin picking, 3D pose determination, and 3D inspection and quality control.
This probably explains, at least partially, why new records for machine
vision systems and components in North America were established in the
last two years”.
Hardware improvements for vision-guided robotics include better
time-of-flight sensors, sheet-of-light triangulation scanners, and structured
light and stereo 3D cameras. “Recent advances in sheet-of-light
triangulation scanners open many new possibilities for inline inspection
and control applications requiring high 3D data density and high speed”,
states Axium. “The latest CMOS sensors can reach a scanning speed up to
several thousands of high resolutions 3D profiles per second”.
The main advantage of structured light and stereo 3D cameras over
sheet-of-light triangulation is that it relative motion between the sensor and
65
the part is not required. This allows fast generation of 3D point clouds with
sufficient accuracy for good scene understanding and robot control.
“These and other recent developments in algorithms and sensor
technologies make it possible to efficiently implement vision-guided
robotics tasks for manufacturers”, Axium concludes. “Therefore, we are
optimistic that more and more projects will integrate machine vision and
robots in the following years”.
Do'stlaringiz bilan baham: |
