J. J. Leonard and Hugh Durrant-Whyte, in the last decade, briefly described the general problem of mobile robot navigation by three questions: “Where am I?,” “Where am I going?,” and “How should I get there?.”
I was shocked to realize that these 3 basic questions which we, ofcourse take for granted(!) are actually immensely complex implementations in robotic systems.
What I found interesting is that to date there is no truly elegant solution even for the “Where am I?” part! A collection of many partial solutions can roughly be categorized into two groups: relative and absolute position measurements. For lack of a single generally good method, developers of automated guided vehicles (AGVs), unmanned flying vehicles (UAV’s) and mobile robots usually combine two or more methods, one from each category. These two super-groups can be further divided into sub-groups as shown below.
Relative Position Measurements
a. Odometry: This method uses encoders to measure wheel rotation and/or steering orientation. Odometry has the advantage that it is totally self-contained, and it is always capable of providing the vehicle with an estimate of its position. The disadvantage of odometry is that the position error grows without bound unless an independent reference is used periodically to reduce the error [Cox, 1991]. This little technique can also be found inside ball-mice!
b. Inertial Navigation: This method uses gyroscopes and sometimes accelerometers to measure rate of rotation and acceleration. Measurements are integrated once (or twice) to yield position. Inertial navigation systems also have the advantage that they are self-contained. On the downside, inertial sensor data drifts with time because of the need to integrate rate data to yield position; any small constant error increases without bound after integration. Inertial sensors are thus unsuitable for accurate positioning over an extended period of time. Another problem with inertial navigation is the high equipment cost. For example, highly accurate gyros, used in airplanes, are inhibitively expensive. Very recently fiber-optic gyros (also called laser gyros), which are said to be very accurate, have fallen dramatically in price and have become a very attractive solution for mobile robot navigation.
Absolute Position Measurements
c. Active Beacons: This method computes the absolute position of the robot from measuring the direction of incidence of three or more actively transmitted beacons. The transmitters, usually using light or radio frequencies, must be located at known sites in the environment. Also known as ‘Triangulation‘.
d. Artificial Landmark Recognition: In this method distinctive artificial landmarks are placed at known locations in the environment. The advantage of artificial landmarks is that they can be designed for optimal detectability even under adverse environmental conditions. As with active beacons, three or more landmarks must be “in view” to allow position estimation. Landmark positioning has the advantage that the position errors are bounded, but detection of external landmarks and real-time position fixing may not always be possible. Unlike the usually point-shaped beacons, artificial landmarks may be defined as a set of features, e.g., a shape or an area. Additional information, for example distance, can be derived from measuring the geometric properties of the landmark, but this approach is computationally intensive and not very accurate.
e. Natural Landmark Recognition: Here the landmarks are distinctive features in the environment. There is no need for preparation of the environment, but the environment must be known in advance. The reliability of this method is not as high as with artificial landmarks.
f. Model Matching: In this method information acquired from the robot’s onboard sensors is compared to a map or world model of the environment. If features from the sensor-based map and the world model map match, then the vehicle’s absolute location can be estimated. Map-based positioning often includes improving global maps based on the new sensory observations in a dynamic environment and integrating local maps into the global map to cover previously unexplored areas. The maps used in navigation include two major types: geometric maps and topological maps. Geometric maps represent the world in a global coordinate system, while topological maps represent the world as a network of nodes and arcs.
The area of robot navigation is still in active research stage. Furthermore, its an interdisciplinary field. If you are considering of incorporating a reliable Sensor or Transducer in your project then keep these few lines in mind:
– What are your needs/requirements? How much accuracy do you require? Precision? Analog/Digital? Software or only hardware implementation?
– Power requirements.
– Cost! This can even chalenge your accuracy and implementation type. Compromises are inevitable!
– Design. Another important consideration. Could be even a biological nano-bot, or a new GPS system for your car.
Keep these things in mind and spend sometime in trying out new things. Who knows you might comeup with a technique of your own or an idea for your current project? For example I recently came to know that old computer ball-mice used ‘IR Shaft encoders’ with about 64 pulses/rev of resolution!