(11th-March-2020)
Landmarks are values that make qualitative distinctions in the individual being modeled. In the coffee example, some important qualitative distinctions include whether the coffee cup is empty, partially full, or full. These landmark values are all that is needed to predict what happens if the cup is tipped upside down or if coffee is poured into the cup.
Orders-of-magnitude reasoning involves approximate reasoning that ignores minor distinctions. For example, a partially full coffee cup may be full enough to deliver, half empty, or nearly empty. These fuzzy terms have ill-defined borders. Some relationship exists between the actual amount of coffee in the cup and the qualitative description, but there may not be strict numerical divisors.
Qualitative derivatives indicate whether some value is increasing, decreasing, or staying the same.
The robot has a position sensor that gives its current coordinates and orientation. It has a single whisker sensor that sticks out in front and slightly to the right and detects when it has hit an obstacle. In the example below, the whisker points 30o to the right of the direction the robot is facing. The robot does not have a map, and the environment can change (e.g., obstacles can move).
A layered controller for such a delivery robot is depicted in Figure. The robot is given a high-level plan to execute. The plan is a sequence of named locations to visit in order. The robot needs to sense the world and to move in the world in order to carry out the plan. The details of the lower layer are not shown in this figure.
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