Robot Navigation

Mobile robots must get around in their environment without wasting motion, without running into things, and without tipping over or falling down a flight of stairs. The nature of a robot navigation system depends on the size of the work area, the type of robot used, and the sorts of tasks the robot is required to perform. In this section, we’ll look at four common methods of robot navigation.

Clinometer

Untitled
Response functions for clinometers. At A, the output voltage is a simple function of slope. At B, up-slope causes positive output voltage; downslope causes negative output voltage.
A clinometer is a device for measuring the steepness of a sloping surface. Mobile robots use clinometers to avoid inclines that might cause them to tip over or that are too steep for them to ascend while carrying a load.
 
The floor in a building is almost always horizontal. Thus, its incline is zero. But sometimes there are inclines such as ramps. A good example is the kind of ramp used for wheelchairs, in which a very small elevation change occurs. A rolling robot cannot climb stairs, but it can use a wheelchair ramp, provided the ramp is not so steep that it would upset the robot’s balance or cause it to lose its payload.
 
In a clinometer, a transducer produces an electrical signal whenever the device is tipped from the horizontal. The greater the angle of incline, the greater the electrical output, as shown in the graph of above figure A. A clinometer might also show whether an incline goes down or up. A downward slope might cause a negative voltage at the transducer output, and an upward slope a positive voltage, as shown in the graph at above figure B.

Edge Detection

Untitled
In edge detection, a robot uses visual boundaries to facilitate navigation. This is what the controller might see in a robot car driving down the center line of a highway
The term edge detection refers to the ability of a robot vision system to locate boundaries. It also refers to the robot’s knowledge of what to do with respect to those boundaries. A robot car, bus, or truck can use edge detection to see the edges of a road and use the data to keep itself on the road. But it must stay a certain distance from the right-hand edge of the pavement to avoid crossing into the lane of oncoming traffic (above figure). It also must stay off the road shoulder. It must be able to tell the difference between pavement and other surfaces, such as gravel, grass, sand, and snow.
 
The interior of a home or business contains straight-line edge boundaries of all kinds, and each boundary represents a potential point of reference for a mobile robot’s edge detection system. The controller in a personal home robot must be programmed to know the difference between, say, the line where carpet ends and tile begins, and the line where a flight of stairs begins. The vertical line produced by the intersection of two walls would present a different situation than the vertical line produced by the edge of a doorway, even though they might appear identical. Thus, edge detection cannot function very well without a certain amount of AI in the robot controller.

Embedded Path

An embedded path is a means of guiding a robot along a specific route. This scheme is commonly used by a mobile robot called an automated guided vehicle (AGV). A common embedded path consists of a buried, current-carrying wire. The current in the wire produces a magnetic field that the robot can follow. This method of guidance has been suggested as a way to keep a car on a highway, even if the driver isn’t paying attention. The wire needs a constant supply of electricity for this guidance method to work. If this current is interrupted for any reason, the robot will lose its way unless some backup navigation method (or human control) is substituted.
 
Alternatives to wires, such as colored or reflective paints or tapes, do not need a supply of power, and this gives them an advantage. Tape is easy to remove and put somewhere else; this is difficult to do with paint and practically impossible with wires embedded in concrete. However, tape is obscured by snowfall; and at night, glare from oncoming headlights might be confused for reflections from the tape.

Range Sensing and Plotting

Range sensing is the measurement of distances to objects in a robot’s environment in a single dimension. Range plotting is the creation of a graph of the distance (range) to objects, as a function of the direction in two or three dimensions.
 
In linear or one-dimensional (1D) range sensing, a signal is sent out, and the robot measures the time it takes for the echo to come back. This signal can be sound, in which case the device is sonar. Or it can be a radio wave; this constitutes radar. Laser beams can also be used. Close-range, one-dimensional range sensing is known as proximity sensing.
 
Two-dimensional (2D) range plotting involves mapping the distance to various objects, as a function of direction in a geometric plane. The echo return time for a sonar signal, for example, might be measured every few degrees around a complete circle in the horizontal plane, resulting in a set of range points. A better plot would be obtained if the range were plotted every degree, every tenth of a degree, or even every hundredth of a degree. But no matter how detailed the direction resolution, a 2D range plot is done in only one plane, such as the floor level in a room, or some horizontal plane above the floor. The greater the number of echo samples in a complete circle (that is, the smaller the angle between samples), the more detail can be resolved at a given distance from the robot, and the greater the distance at which a given amount of detail can be resolved.
 
Three-dimensional (3D) range plotting is done in spherical coordinates: azimuth (compass bearing), elevation (degrees above the horizontal), and range (distance). The distance must be measured for a large number of diverse orientations. In a furnished room, a 3D sonar range plot would show ceiling fixtures, things on the floor, objects on top of a desk, and other details not visible with a 2D plot. The greater the number of echo samples in a complete sphere surrounding the robot, the more detail can be resolved at a given distance, and the greater the range at which a given amount of detail can be resolved.