JDERobot

I’ve finished my project. Our approach specifically addresses issues such as safe navigation in unmodified and dynamic environments, like Departamental II of this university. We’ve solved the following problems:

- Navigation in dynamic environments. Public places are often packed with people. People behave not necessarily cooperatively. Our approach provides means for safe and effective navigation through crowds.

- Navigation in unmodified environments. No modification of the environment is necessary for the robot’s operation.

- Localization. In every operation, our robot continuously tracks its position using its maps. Position estimates are necessary for the robot to know where to move when navigating to a specific goal, and to ensure the robot does not accidentally leave its operational area.

To navigate reliably in indoor environments, a mobile robot must know where it is. Thus, reliable position estimation is a key problem in mobile robotics. We believe that probabilistic approaches are among the most promising candidates to providing a comprehensive and real-time solution to the robot localization problem.

So, in this video we’ve used Monte Carlo localization method where we represent the probability density involved by maintaining a set of samples that are randomly drawn from it. We show experimentally that the resulting method is able to efficiently localize a mobile robot without knowledge of its starting location.

Link for more information: http://jde.gsyc.es/index.php/jmvega_guide_robot