5.1: Robotic Sensors
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The development of sensors is classically driven by industries other than robotics. These include submarines, automatically opening doors, safety devices for industry, servos for remote-controlled toys, and more recently the cell-phone, automobiles and gaming consoles. These industries are mostly responsible for making “exotic” sensors available at low cost by identifying mass-market applications, e.g., accelerometers and gyroscopes now being used in mass-market smart phones or the 3D depth sensor “Kinect” as part of its XBox gaming console.
Query
Recap the sensors that you are interacting with daily. What sensors do you have in your phone, in your house and kitchen, or in your car?
As we will see later on, sensors are hard to classify by their application. In fact, most problems benefit from every possible source of information that they can obtain. For example, localization can be achieved by counting encoder increments, but also by measuring acceleration, or using vision. All of these approaches differ drastically in their precision and the kind of data that they provide, but none of them is able to completely solve the localization problem on its own. This chapter is therefore organized by the physical quantities (and derivatives thereof), a sensor is measuring, instead of the higher level state estimates it can contribute to.
Query
Think about the kind of data that you could obtain from an encoder, an accelerometer, or a vision sensor on a nonholonomic robot. What are the fundamental differences?
Although an encoder is able to measure position, it is used in this function only on robotic arms. If the robot is nonholonomic, closed tours in its configuration space, i.e., robot motions that return the encoder values to their initial position, do not necessarily drive the robot back to its starting point. In those robots, encoders are therefore mainly useful to measure speed. An accelerometer instead, by definition, measures the derivative of speed. Vision, finally, allows to calculate the absolute position (or the integral of speed) if the environment is equipped with unique features. An additional fundamental difference between those three sensors is the amount and kind of data they provide. An accelerometer samples real-valued quantities that are digitized with some precision. An odometer instead delivers discrete values that correspond to encoder increments. Finally, a vision sensor delivers an array of digitized real-valued quantities (namely colors). Although the information content of this sensor exceeds that of the other sensors by far, cherry-picking the information that are really useful remains a hard, and largely unsolved, problem.
