Page 166 - Autonomous Mobile Robots

150 Autonomous Mobile Robots

4.1 INTRODUCTION
Landmarks are routinely used by biological systems as reference points during
navigation. Theiremploymentinroboticnavigationrequiresthedevelopmentof
satisfactory sensor technologies for landmark selection and recognition, which
poses a big challenge. During the last two decades, landmarks and triangulation
techniques have been widely used in navigation of autonomous mobile robots
in industry [1,2]. Such a navigation strategy relies on identification and sub-
sequent recognition of distinctive environment features or objects that are either
known a priori or extracted dynamically. This process has inherent difficulties
in practice due to sensor noise and environment uncertainty [3]. This chapter
outlines a number of landmark-based navigation algorithms that are able to
locate the mobile robot and update landmarks autonomously.
Autonomous mobile robots need the capability to explore and navigate
in dynamic or unknown environments in order to be useful in a wide range
of real-world applications. Over the last few decades, many different types
of sensing and navigation techniques have been developed in the field of
mobile robots, some of which have achieved very promising results based
on different sensors such as odometry, laser scanners, inertial sensors, gyro,
sonar, and vision [4]. This trend has been mainly driven by the necessity
of deployment of mobile robots in unstructured environments or coexisting
with humans. However, since there is huge uncertainty in the real world and
no sensor is perfect, it remains a great challenge today to build robust and
intelligent navigation systems for mobile robots to operate safely in the real
world.
In general, the methods for locating mobile robots in the real world
are divided into two categories: relative positioning and absolute position-
ing. In relative positioning, odometry (or dead reckoning) [4] and inertial
navigation (gyros and accelerometers) [5] are commonly used to calculate
the robot positions from a starting reference point at a high updating rate.
Odometry is one of the most popular internal sensor for position estim-
ation because of its ease of use in real time. However the disadvantage
of odometry and inertial navigation is that it has an unbounded accumu-
lation of errors, and the mobile robot becomes lost easily. Therefore, fre-
quent correction based on information obtained from other sensors becomes
necessary.
In contrast, absolute positioning relies on detecting and recognizing dif-
ferent features in the robot environment in order for a mobile robot to reach
a destination and implement specified tasks. These environment features are
normally divided into four types [4] (i) active beacons that are fixed at known
positions and actively transmit ultrasonic [6], IR or RF signals for the calcu-
lation of the absolute robot position from the direction of receiving incidence;
(ii) artificial landmarks that are specially designed objects or markers placed at

© 2006 by Taylor & Francis Group, LLC

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