Robots That Think On Their Feet Now Possible

January 27, 1999

Robots, unlike stand-up comics, are not adept at thinking on their feet. Should a heckler interrupt a planned routine, or an irresistible off-the-cuff opportunity arise, the comic can sense these changing situations and adjust. A robot on the factory floor, though, is stuck with its plan amidst a changing landscape, and will keep spot welding, for instance, on a non-existent automobile frame because of a delay on the assembly line.

Now, an engineer at Washington University in St. Louis and two of his former graduate students have stated a theory and devised an algorithm that will make robots as deft and nimble as Robin Williams on "The Tonight Show." Moreover, they have demonstrated their theory and algorithm with robots in the Washington University laboratory.

T.J. Tarn, Ph.D., professor of systems science and mathematics in Washington University's School of Engineering and Applied Science, Mumin Song, Ph.D., technical specialist with Ford Motor Co. in Detroit, and Ning Xi, Ph.D., assistant professor of electrical engineering at Michigan State University, are the first to develop a theory that integrates low-level data sensing, or gathering, with high-level planning and decision-making processes.

The theory, encapsulated in an algorithm, completely automates the process whereby a system must adapt to changing conditions. For years, manufacturers with automated systems have dealt with floor malfunctions in an ad hoc way, involving a lot of human intervention and wasted time and dollars. With the adaptation of the Tarn/Song/Xi theory, manufacturers will be able to let the robot -- or other automated process -- go with or adapt to the flow on its own.

In the case of a robot on the factory floor, the robot would use the all-purpose algorithm to halt work when the assembly line is out of synch and recommence activity once things were in order again. This leaves the plant manager out of the picture, allowing him or her to pursue other tasks. Similarly, in air traffic control, an airplane's automated computer system could relay data through the algorithm to traffic control headquarters, letting headquarters know the plane's location and other parameters to determine the plane's ultimate arrival time. This would lessen the strain of the controller's job.

The Washington University algorithm is called the Max-Plus Algebra Model. It combines task-scheduling, action-planning and control, and it solves a problem that is two decades old.

"The biggest problem in scheduling is to get the machine to fit into the whole system," says Tarn, who is director of Washington University's Center for Robotics and Automation. "Scheduling is on the high-level of control; real-time sensing is on the low level. How to coordinate the two levels automatically has been a wide-open problem for 20 years in the field of intelligent control. We've solved it theoretically with this model, and then we've proven it in the laboratory."

The three were awarded the Best Paper Award at the 1998 Japan-USA Symposium on Flexible Automation, in Otsu, Japan. The paper has been published in the conference proceedings. The work is sponsored by the National Science Foundation.

The Max-Plus model will have applications in automatic control systems far and wide. It can be used in many automated situations where high-level controls must be coordinated with low-level ones. It also could be a boon to the medical industry, where certain surgical procedures, such as artificial hip replacements that require a super-human steady hand, are performed with robotic instruments.

Stop And Think

In Tarn's Washington University laboratory, the engineers programmed a Puma robot to pick up three objects of different height, which spun randomly on a rotating disk. The robot was programmed to pick up the objects in descending order, from tallest to shortest, and then place them in a pre-assigned place. A camera within the robot identified the object by height and instructed the robot on which object to pick up.

The researchers placed an obstacle in the way of the robot, which, as programmed, immediately stopped its activity. When they removed the obstacle, the robot began its task again without human assistance and pursued the exact object it was supposed to choose.

The smooth transition was made possible by the Max-Plus model, which analyzes the real-time disturbances, communicates the problem to the high-level control, and halts the robot and tells it to proceed when the road is clear.

"What the model does is enable the robot to stop and think," explains Song, whose doctoral dissertation was based on the project. "Stopping is the key thing. It gives the machine time to "think" and then feed back data to the upper level."

"In this kind of situation today, an engineer goes into an emergency mode, pushes a button and stops the whole manufacturing process because the robot will keep going as it had been told to, and you have a chaotic situation," Tarn explains. "This is very undesirable. But with our model, the algorithm knows where the robot is in the process, stops the robot and communicates the data to the high-level manager's computer. This way, you don't have to shut down the whole system, which is where the cost problem lies. The algorithm also enables the robot to re-start its task once the problem is corrected."

Many automated systems have computer codes installed that can deal with certain programmed malfunctions. However, the codes are heuristic or rule-based, meaning that they can only deal with known manufacturing errors that have arisen before and have been described mathematically. But, as in the case of the stand-up comic, who knows what an audience is going to throw your way?

"Heuristic code does not begin to exhaust all problems," says Tarn. "This algorithm is all-purpose -- it can deal with any unstructured event. It is getting a good deal of attention from industry."
-end-
Editor's note: Photos of T.J. Tarn and a Puma robot are available upon request.



Washington University in St. Louis

Related Robots Articles from Brightsurf:

On the way to lifelike robots
In order for robots to be able to achieve more than simple automated machines in the future, they must not only have their own ''brain''.

Children think robots can help the elderly -- but not their own grandparents
A study that asked children to assess three different robots showed that they responded most positively to simple robots shaped like flower pots, and were most sceptical of Pepper the robot, which looks more human.

Nanomaterial gives robots chameleon skin
A new film made of gold nanoparticles changes color in response to any type of movement.

How many jobs do robots really replace?
MIT economist Daron Acemoglu's new research puts a number on the job costs of automation.

Robots popular with older adults
A new study by psychologists from the University of Jena (Germany) does not confirm that robot skepticism among elder people is often suspected in science.

Showing robots how to do your chores
By observing humans, robots learn to perform complex tasks, such as setting a table.

Designing better nursing care with robots
Robots are becoming an increasingly important part of human care, according to researchers based in Japan.

Darn you, R2! When can we blame robots?
A recent study finds that people are likely to blame robots for workplace accidents, but only if they believe the robots are autonomous.

Robots need a new philosophy to get a grip
Robots need to know the reason why they are doing a job if they are to effectively and safely work alongside people in the near future.

How can robots land like birds?
Birds can perch on a wide variety of surfaces, thick or thin, rough or slick.

Read More: Robots News and Robots Current Events
Brightsurf.com is a participant in the Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a means for sites to earn advertising fees by advertising and linking to Amazon.com.