Page 23 - Introduction to Autonomous Mobile Robots

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Figure 1.13 Chapter 1
KHEPERA is a small mobile robot for research and education. It is only about 60 mm in diameter.
Various additional modules such as cameras and grippers are available. More then 700 units had
already been sold by the end of 1998. KHEPERA is manufactured and distributed by K-Team SA,
Switzerland (http://www.k-team.com). © K-Team SA.

For example, AGV (autonomous guided vehicle) robots (figure 1.8) autonomously
deliver parts between various assembly stations by following special electrical guidewires
using a custom sensor. The Helpmate service robot transports food and medication
throughout hospitals by tracking the position of ceiling lights, which are manually specified
to the robot beforehand (figure 1.9). Several companies have developed autonomous clean-
ing robots, mainly for large buildings (figure 1.10). One such cleaning robot is in use at the
Paris Metro. Other specialized cleaning robots take advantage of the regular geometric pat-
tern of aisles in supermarkets to facilitate the localization and navigation tasks.
Research into high-level questions of cognition, localization, and navigation can be per-
formed using standard research robot platforms that are tuned to the laboratory environ-
ment. This is one of the largest current markets for mobile robots. Various mobile robot
platforms are available for programming, ranging in terms of size and terrain capability.
The most popular research robots are those of ActivMedia Robotics, K-Team SA, and I-
Robot (figures 1.11, 1.12, 1.13) and also very small robots like the Alice from EPFL (Swiss
Federal Institute of Technology at Lausanne) (figure 1.14).
Although mobile robots have a broad set of applications and markets as summarized
above, there is one fact that is true of virtually every successful mobile robot: its design
involves the integration of many different bodies of knowledge. No mean feat, this makes
mobile robotics as interdisciplinary a field as there can be. To solve locomotion problems,
the mobile roboticist must understand mechanism and kinematics; dynamics and control
theory. To create robust perceptual systems, the mobile roboticist must leverage the fields
of signal analysis and specialized bodies of knowledge such as computer vision to properly