After three weeks of development we finally finished our project in robotics. The task was to construct a robot from lego parts from the lego NXT kit. It wasn’t so straighforward how to achieve that since a bad design could cause a lot of trouble in the end. Finally, we ended up with a simple tripod.

Then, the software part was to programm the robot to perform a taks, known as global localization. In simple words, imagine that you are given a map but you have no idea about the position of the robot in the map. Then, by using a ultrasound sensor you have to find the position and the using this information to drive the robot to a target. As you see, an error in the estimation can be disastrous.

The method we used for the localization part is know as Monte Carlo Localization. This method (uniformly) samples a number of hypotheses (particles) and then by comparing the measurements with the hypothetical measurements we re-sample hypotheses by weightening them so as the more consistent a measurement is, the more probabilities are that a hypothesis will be re-chosen.

After a serveral iterations of the MCL, an estimation of the position is found. Then, using this estimation, with the hope that it is also the correct position, we guide the robot to the target. So, how do we chose how to get from a position to another ? A simple and safe solution to that was to compute the Delauney triangulation of the map and then compute the dual graph of it, . Then we find the closest node of the dual graph to the robot and to the target. So, we have a starting position, a node (A) of that is close to the starting point, a node (B) of that is close to the target and a path from A to B on . Voila, this is the path that we have to follow so as to be as safe as possible that we won’t hit on a wall.

Well, here it is on action.

That was fun