Mobile robot navigation

From Wikipedia, the free encyclopedia

This article may not meet the general notability guideline or one of the following specific guidelines for inclusion on Wikipedia: Biographies, Books, Companies, Fiction, Music, Neologisms, Numbers, Web content, or several proposals for new guidelines. If you are familiar with the subject matter, please expand or rewrite the article to establish its notability. The best way to address this concern is to reference published, third-party sources about the subject. If notability cannot be established, the article is more likely to be considered for redirection, merge or ultimately deletion, per Wikipedia:Guide to deletion.
This article has been tagged since January 2008.

For any mobile device, the ability to navigate in its environment is one of the most important capabilities of all. Staying operational, i.e. avoiding dangerous situations such as collisions and staying within safe operating conditions (temperature, radiation, exposure to weather, etc.) come first, but if any tasks are to be performed that relate to specific places in the robot environment, navigation is a must. In the following, we will present an overview of the skill of navigation and try to identify the basic blocks of a robot navigation system, types of navigation systems, and closer look at its related building components.

1 Navigation
2 Vision-Based Navigation
- 2.1 Indoor Navigation
3 References

[edit] Navigation

Robot navigation means its ability to determine its own position in its frame of reference and then to plan a path towards some goal location. In order to navigate in its environment, the robot or any another mobility device requires representation i.e. a map of the environment and the ability to interpret that representation. Navigation can be defined as the combination of the Three fundamental competences:

Self-Localisation
Path Planning
Map-Building and Map-Interpretation

Map in this context denotes any one-to-one mapping of the world onto an internal representation. In robots, this representation takes the form of artificial neural network excitation patterns.

Localisation denotes the robot's ability to establish its own position and orientation within the frame of reference. Path planning is effectively an extension of localisation, in that it requires the determination of the robot's current position and a position of a goal location, both within the same frame of reference or coordinates. Map building can be in the shape of a metric map or any notation describing locations in the robot frame of reference.

[edit] Vision-Based Navigation

Vision-Based Navigation uses optical sensors include laser-based range finder and photometric cameras using CCD arrays to extract the visual features required to the localisation in the surrounding environment. However, there are a range of techniques for navigation and localisation using vision information, the main components of each technique are:

representations of the environment.
sensing models.
localisation algorithms.

In order to give an overview of vision-based navigation and its techniques, we classify these techniques under indoor navigation and outdoor navigation.

[edit] Indoor Navigation

The easiest way of making a robot go to a goal location is simply to guide it to this location. This guidance can be done in different ways: burying an iductive loop or magnets in the floor, painting lines on the floor, or by placing beacons, markers, barcodes etc. in the environment. Such Automated Guided Vehicles (AGVs) are used in industrial scenarios for transportation tasks.

There are a very wide variety of indoor navigation systems. In the following, we will try to describe some of these systems.
The basic reference of indoor and outddor navigation systems is^[1]

VISION-BASED POSITIONING