The following list contains additional information and comments about the project.
Wed Apr 5 18:21:48 CDT 2006: Assignment opened
This page describes the third assignment for the course 74.795-L01 Mobile Robotics Using Local Vision.
The goal of this assignment is to implement a practical mapping algorithm for a small intelligent mobile robot.
We setup a line world in the Autonmous Agent's lab in EITC E2 504.
There are also four extra localization markers placed in the environment. The localization markes are round two coloured 30cm tall poles. The poles A1 and A2 are red on top of yellow and the poles B1 and B2 have yellow on top of red.
Implement a Bayesian filter based approach to mapping in the line world environment. Five purple 30cm tall obstacles will be placed in the environment.
The winner of this assignment will be determined during a demonstration which will take place on the 12th April 2006 at 17:00 in the Autonomous Agents lab.
The race will consist of the robot starting in one place on the playing field. The robot has 20 minutes to explore its environment. After that period, the robot must generate a map of the playing field showing the position of the five obstacles.
Each robot is assigned a scale factor. The scale factor is used to compensate for the fact that it is more difficult for a smaller robot to drive at the same speed as a larger robot. The scale factor is determined by the maximum dimension of the robot.
The scale factor is calculated as (Max Dimension of Robot)/10cm.
This means, a robot with a size of 10cm is assigned a scale factor of 1.0; a robot with a maximum dimension of 5cm is assigned a scale factor of 0.5 and a robot with a maximum dimension of 40cm is assigned a scale factor of 4.
The score of the robot is determined by the sum of the distances between the measured and real coordinates of the five obstacles.
The robot with the best (lowest) scale time is declared the winner.
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