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About this Article
Written by: Gunes Ercal
Written on: November 11th, 2000
Tags: computer science, electrical engineering, mechanical engineering
Thumbnail by: Handitec/Wikimedia Commons
About the Author
Gunes studied at the University of Southern California in 2000.
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Volume III Issue II > Minerva: A Pioneer in Everyday Robots
Robots are often relegated to the realm of fantasy. But Minerva, an interactive tour-guide robot, which was successfully exhibited in the Smithsonian Museum, has brought robotics to everyday life.

Robots: Cuddly or Deadly?

From the terrifying annihilators of Terminator II to the cute, artificial creature of Short Circuit, contemporary science fiction has done so much to shape people's conception of robots that it may be difficult to assess the real possibilities of robots in everyday life. That is why you might be pleasantly surprised by Minerva, a robot designed by a research team from Carnegie Mellon with the purpose of assisting people in public places. Specifically, she exists in Smithsonian's National Museum of American History. She actively approaches people, offers tours, and guides them from exhibit to exhibit. She even entertains by singing or smiling when she is happy. But just as a person who is temporarily blinded or blocked by another person's hand or body would become frustrated after a while, Minerva too becomes frustrated when someone blocks her way and responds by honking her horn. Her combination of abilities makes her a pioneering example of a successful autonomous robotic tour guide in a crowded, public environment. By examining her ability to learn maps and her human-robot interface, one can see that Minerva is a prime specimen to demonstrate the growing relevance of autonomous mobile robotics in everyday life.

Navigation

One primary task of any tour guide, robot or human, is clearly to navigate. In fact, navigation is required for any mobile agent. Knowledge required for navigation can be broken into two distinct components: knowledge of current position and knowledge of how to get to a desired position from a current position. The knowledge of current position carries the name of localization while the knowledge of how to get to a desired position from the current position is mapping. Also, in building a robust, autonomous robot, adaptation is a necessary ingredient [1]. In the context of navigation, adaptation can come in the form of the robot's ability to react appropriately to a wide and diverse range of situations. In implementing such flexibility, the robot must also be as independent as possible of its own physical deficiencies in perception.
In the case of Minerva, these perceptive shortcomings include limits on the range of her lasers and sonar range-finders, which do not allow the robot to "see" anything to use as a reference point in sufficiently wide-open spaces [2]. Minerva's odometer also collects errors fairly quickly over long ranges, eventually causing dramatic errors in the robot's belief of its position by pure dead reckoning. Even cameras can fail in regions that lack sufficient visual structure, such as blank walls or ceilings [2]. Therefore, in obtaining localization and mapping information, Minerva should be as independent as possible of her dead reckoning and sonar data. The presence of many moving people immensely complicates the perceived environment. The robot must dynamically be able to differentiate what is an environmental feature and what is a human being, a task that requires considerable adaptation. All in all, adaptation is key.

Learning to Navigate

In fact, she should try to learn for herself where she is and how she should get to where she intends to be. She should dynamically create her own map information. However, since Minerva does not accept any of her sensory information as absolutely accurate, and in fact takes minimal perceptory accuracy for granted, there is significant uncertainty in her learning. Probability is the most appropriate analytical tool to use for problems involving uncertainty. The most well-known and effective localization algorithms about uncertainty are the probabilistic Markov localization algorithms [3]. Many of these algorithms are passive. That is to say, while figuring her position, the robot using passive localization does not re-adjust herself to optimize the angle at which she can obtain information.