Robot Perception

Robots

Contents

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Good Starting Places

AI in the News

Embedded Vision Slideshow: New Age of Machines That See

Computer vision enables machines to understand things through visual inputs, sometimes even exceeding the capabilities of human vision. For decades, computer vision has been a niche technology, because computer vision equipment has been large, expensive, and complex to use. But recently, products like the Microsoft Kinect and vision-based automotive safety systems have demonstrated that computer vision can now be deployed even in cost-sensitive applications, and in ways that are easy for non-specialists to use. The term "embedded vision" is often used to refer to the incorporation of visual intelligence into a wide range of systems.

Source

Apr 26 2013, By Bier, Jeff

General Readings

Publication

On Seeing Robots

. It is argued that Situated Agents should be designed using a unitary
on-line computational model. The Constraint Net model of Zhang and Mackworth satisfies
that requirement. Two systems for situated perception built in our laboratory are described
to illustrate the new approach: one for visual monitoring of a robot’s arm, the other for
real-time visual control of multiple robots competing and cooperating in a dynamic world.

First proposal for robot soccer.

Source

By Mackworth, Alan, 1992
classic, soccer

News

AI in the News

Embedded Vision Slideshow: New Age of Machines That See

Computer vision enables machines to understand things through visual inputs, sometimes even exceeding the capabilities of human vision. For decades, computer vision has been a niche technology, because computer vision equipment has been large, expensive, and complex to use. But recently, products like the Microsoft Kinect and vision-based automotive safety systems have demonstrated that computer vision can now be deployed even in cost-sensitive applications, and in ways that are easy for non-specialists to use. The term "embedded vision" is often used to refer to the incorporation of visual intelligence into a wide range of systems.

Source

Apr 26 2013, By Bier, Jeff
AI in the News

Insects inspiring new technology: Autonomous navigation of mobile robots based on locust vision

The way in which the locust's distinctive visual system could be transferred into technology for state of the art vehicle collision sensors, surveillance technology and video games has been detailed as part of robotics research being carried out by an academic from the University of Lincoln (UK).

Source

Feb 21 2013

Videos

Video

Artificial Intelligence Arrives

Microsoft may have found a breakthrough in artificial intelligence with the creation of a robot that can sense humans and their movements, a Virtual Admin. Interview with Eric Horvitz.

Source

3 min, 2009, Discovery
Video

Kato, the Geocaching Robot

Kato is a geocaching robot at the University of Alberta. This video shows how Kato sees the world with its laser scanner and how it uses this information to navigate around obstacles to a hidden cache.

Source

4 min, 2008, AAAI

Classic Articles & Books

Pages

Publication

A Versatile Computer-Controlled Assembly System

A versatile assembly system, using TV cameras and
oomputer-controlled arm and moving table, is described.
It makes almple assemblies such aa a peg and rings and
a toy car. It separates parts from a heap, recognising
them with an overhead camera, then assembles them by
feel. It can be instructed to perform a new task
with different parte by spending an hour showing it
the parts and a day or two programming the assembly
manipulations. A hierarchical description of parts,
views, outlines etc. is used to construct models, and
a structure matching algorithm is used in recognition.

Later version appearing in Artificial Intelligence, Vol 6, pp. 129(1975)  (available for a fee).

Source

By Ambler, A.P. , H.G, Barrow, C M. Brown, R.H. Burstall, and R.J. Popplestone , 1973
Edinburgh, classic
Publication

The Stanford Hand-Eye Project

There is a large continuing project at Stanford Artificial Intelligence Laboratory aimed towards the development of a system capable of interesting perceptual-motor behavior. This paper presents a brief outline of the currently active efforts and suggests references for more detailed information. A more thorough discussion of the effort to organize a visual perception system is presented.

Source

By Feldman, J.A., G. M. Feldman, G. Falk, G. Grape, J. PearlmanI. Sobel, and J, M. Tenenbaum, 1969
SAIL, Stanford, classic
Publication

On Seeing Robots

. It is argued that Situated Agents should be designed using a unitary
on-line computational model. The Constraint Net model of Zhang and Mackworth satisfies
that requirement. Two systems for situated perception built in our laboratory are described
to illustrate the new approach: one for visual monitoring of a robot’s arm, the other for
real-time visual control of multiple robots competing and cooperating in a dynamic world.

First proposal for robot soccer.

Source

By Mackworth, Alan, 1992
classic, soccer
Publication

Obstacle avoidance and navigation in the real world by a seeing robot rover

The Stanford AI lab cart is a card-table sized mobile robot controlled remotely through a radio link, and equipped with a TV camera and transmitter. A computer has been programmed to drive the cart through cluttered indoor and outdoor spaces, gaining its knowledge about the world entirely from images broadcast by the onboard TV system.

The cart deduces the three dimensional location of objects around it, and its own motion among them, by noting their apparent relative shifts in successive images obtained from the moving TV camera. It maintains a model of the location of the ground, and registers objects it has seen as potential obstacles if they are sufficiently above the surface, but not too high. It plans a path to a user-specified destination which avoids these obstructions. This plan is changed as the moving cart perceives new obstacles on its journey.

The system is moderately reliable, but very slow. The cart moves about one meter every ten to fifteen minutes, in lurches. After rolling a meter, it stops, takes some pictures and thinks about them for a long time. Then it plans a new path, and executes a little of it, and pauses again.

Source

By Moravec, Hans Peter, 1980
Stanford, classic, dissertation
Publication

Object recognition tests on the Mark 15 robot

By Turner, K. ]. , 1971
classic
Publication

Computer Control of a Mechanical Arm through Visual Input

By Pingle, Karl K., Jonathan A. Singer, and William M. Wichman , 1968
classic

Pages

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