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Using a Kalman filter to better estimate the position of a non-holonomic, 4 wheeled robot using Webots

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Four-wheeled-robot-localization-with-Kalman-Filter

These are the files for a simulated robot and its controller built using Webots. The robot has 4 driven wheels and two independent steering motors making it a non-holonomic system. This warrants the use of an Ackerman steering mechanism for accurate positioning and the localization is done using wheel odometry and inertial measurements. As a result of the simulated surface with variable friction, bounce and unevenness, the mapping needs to be strengthened using a Kalman filter.

Given a list of coordinates, the robot will autonomously drive itself to each coordinate in the list in that order and stop when it reaches the final point. To try this out for yourself, all you need to do is install Webots, download the "kf_1" folder and load this world into the program. The default robot controller should be "kf_controller_final".

The robot can:

  • Calculate it's current location at any given moment with the help of a Kalman filter
  • Traverse to any coordinate in the plane provided the distance between any two consecutive coordinates is slightly larger than the length of the robot
  • Check whether it has reached a point, iterate to the next waypoint, calculate odometry and inertial values and can be coded to move at different speeds

The robot can't:

  • Detect or avoid obstacles in its path
  • Move its wheels at an angular velocity higher than 10 rad/s

Further improvements might include:

  • Obstacle detection/ avoidance and higher level path awareness and planning
  • Not having to stop while changing the steering angle of its wheels

This project is GPL-3.0 Liscenced to Aditya Nair under GogiPuttar.

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Using a Kalman filter to better estimate the position of a non-holonomic, 4 wheeled robot using Webots

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