This video demonstrates learning by example in an artificial neural network that controls the motion of a mobile robot. The robot uses four sonar sensors and three IR sensors to detect the ranges to nearby objects. A wireless controller is used to initially remote control the robot past some test objects while the robot records the sensor readings and motor control signals. This data is then used to train a 2x7 artificial neural network (2 motors and 7 sensors). Once the network is trained, it is used to control the robot without intervention from the operator.

For more information, see http://www.pirobot.org/blog/0007/

This is a followup to my earlier post describing the use of a simple neural network to control a light following robot. In the original demonstration, the connections between input and output neurons were hard coded with values that were known to steer the robot in the right way. In the current demonstration, the neural network is initialized with random connections and the correct behavior has to be learned.

In the video below, the robot begins with a random 2x2 neural network for controlling the motors based on the values of the two light sensors mounted on the front. A supervised learning algorithm employing the Delta Rule is used to train the network by utilizing a known solution to provide the teaching signals five times per second. At the beginning of the video, you can see that the robot turns away from the light and even goes backward. However, within 10-15 seconds, the network is already sufficiently trained to follow the light beam.

For more information, see http://www.pirobot.org/blog/0006/

Greetings Roboteers,

I just finished up a little demo regarding the use of a simple artificial neural network (ANN) to control a mobile robot. The demonstration is only meant to introduce the concepts and terminology of neural nets rather than being something particularly useful. Also, this blog entry does not deal with *learning* in ANN's which is what they are most famous for. That will be the topic of a forthcoming blog entry and demo.

Here is the link to the report. If you get bored with the math at the beginning, you can scroll down toward the end where there is a Youtube video demonstrating the robot in action.

http://www.pirobot.org/blog/0005/

I have been working recently on an omnidirectional vision system for a mobile robot. My latest results involve using the system for obstacle avoidance and navigation. You can find an explanation and a few short videos at:

http://www.pirobot.org/blog/0004/