Edge Detection

The environment of the simulation consists of:

• A table surface
• A robot of circular shape

The robot has 2 circular edge sensors (detect the edges of the table). The robot's controller is a primitive analog circuit that reverses the direction of both motors' rotation for almost a second whenever any of the sensors is triggered.

The objective of the robot is to avoid falling off of the table.

Refer to the BrainOne code example for more information.

Object Detection

The environment of the simulation consists of:

• A table surface, surrounded with walls
• A robot of circular shape
• A heavy object on the surface

The robot has 2 whisker-like touch sensors. The robot's controller is a primitive analog circuit that reverses the direction of both motors' rotation for almost a second whenever any of the sensors is triggered.

The objective of the robot is to avoid colliding with any objects.

Refer to the BrainTwo code example for more information.

Line Following

The environment of the simulation consists of:

• A table surface
• Several connected white lines on the surface of the table
• A robot of circular shape
• A heavy object on the surface

The robot has 2 IR line sensors. The robot's brain is a programmable microcontroller.

The objective of the robot is to find the line and follow it.

Refer to the LineFollowingBrain code example for more information.

Brain One

The environment of the simulation consists of:

• A table surface of irregular shape
• 3 thin walls
• A light circular object (undetectable by robots' touch sensors)
• A heavy pentagonal object
• A robot of circular shape

The robot has 2 circular edge sensors (detect the edges of the table) and 2 whisker-shaped touch sensors (detect walls and sufficiently heavy objects). The robot's controller is a primitive analog circuit that reverses the direction of the motor rotation for almost a second whenever any of the sensors is triggered.

The objective of the robot is to avoid falling off of the table and colliding with the walls using its sensors.

Refer to the BrainOne code example for more information.

Brain Two

The environment of the simulation consists of:

• A table surface of irregular shape
• 3 thin walls
• A light circular object (undetectable by robots' touch sensors)
• A heavy pentagonal object
• A robot of circular shape

The robot has 2 circular edge sensors (detect the edges of the table) and 2 whisker-shaped touch sensors (detect walls and sufficiently heavy objects). The robot's controller is a primitive analog circuit that reverses the direction of both motors' rotation for almost a second whenever any of the sensors is triggered.

The objective of the robot is to avoid falling off of the table and colliding with the walls using its sensors.

Refer to the BrainTwo code example for more information.

Actuators

The environment of the simulation consists of:

• A table surface
• Several connected white lines on the surface of the table
• A robot of circular shape
• A heavy object on the surface

The robot has 2 IR line sensors. The robot's brain is a programmable microcontroller. The outputs of the brain are also connected to the four LEDs.

The objective of the robot is to find the line and follow it.

Refer to the LineFollowingBrain code example for more information.

Complex Behaviour

The environment of the simulation consists of:

• A table surface of circular shape
• A heavy pentagonal object
• Several lines leading from one side of the table to the goal on the other side
• A moving robot in the center of the table, 'Squid'
• A robot of circular shape

The robot has 2 circular edge sensors (detect the edges of the table), 2 IR range detectors (detect walls and sufficiently large objects) and 2 small line sensors. The robot's controller is a programmable controller, i.e. Basic Stamp. Whenever the robot enters the 'searching for the line' mode, the LED on its front lights up.

The objective of the robot is to find it's way to the goal on the other side of a table by following the lines and avoiding the moving 'Squid' robot.

Refer to the SquidTestBrain code example for more information.