As your projects gain complexity it may use many different sensors and actuators. You will need a way to effectively use all this information to produce reliable results.

There are two types of algorithm that can be used to do this. The first is called sensor fusion. This approach uses information from all of the sensors simultaneously to create a constant world model of its surroundings.

This sort of model is very difficult to program and requires massive amounts of computational power, and so is not really suited to the world of hobby robots.

This section of the tutorial will cover something called subsumption architecture. This is a way of building a simple behaviour model that is much simpler computationally based on priority.

For this example lets say that you have built a robot that is going to follow light around the room using LDR sensors. The robot also has an ultrasound proximity sensor and bump micro switches to detect obstacles.

A diagram of its subsumption architecture would look like this:

The diagram shows how priorities work in subsumption architecture.

The bump sensor takes the highest priority, which makes sense, if your robot is stuck against a wall you don't want to be worrying about following a light source.

Cruise has the lowest priority, its basically saying drive forwards until you find a light to follow (or an obstacle to avoid).

The ultrasound sensor has a lower priority than the bump sensor because you ideally want to avoid objects before you collide with them.