Simulation of mobile robot navigation with sensor fusion on an uneven path

This paper describes the navigation of a two-wheel drive mobile robot along a predefined path under uneven road conditions where it cannot solely rely on encoders, GPS or an accelerometer individually. There are conditions when low friction or slippery ground surfaces such as sandy paths and pits cause one or both encoders to halt or rotate less as the robot moving forward. Areas covered with clouds, trees or structures can block GPS signals. Sudden pickups and halts give false information from accelerometers. Therefore Kalman filter based sensor fusion algorithm is implemented in order to get the best position estimation for the mobile robot using above sensor outputs. The Special feature of this algorithm is that it includes a simple method to overcome the effects of encoder errors due to the slipping of wheels of the mobile robot, which does not require complex computations to additional measurement units to directly measure the slipping of the wheels of the robot. Finally the validity of the proposed algorithm is demonstrated via simulation