Akademik Veri Yönetim Sistemi
2019 International Conference on Artificial Intelligence, Robotics and Control, AIRC 2019, Cairo, Mısır, 14 - 16 Aralık 2019, ss.7-13
Position, velocity
and acceleration information are important for mobile robots. Due to the wheel
slippages, encoder data may not be reliable and IMU data also contains a cumulative
error. Errors of inertial measurements are accumulated over velocity and
position estimates and as time increases, these errors grow higher. Due to
robot hardware and the operating surface, ground truth may not be available. In
this work recurrent deep neural network is proposed in order to reduce the
error in speed and yaw angle estimates coming from encoder and IMU data. Neural
networks are commonly used to capture the behavior of linear and non-linear
systems. Since ground-wheel interaction forces are modeled with non-linear
models such as the Magic formula and determining parameters of those models
require time and test set-ups, there is a need for simpler methods to model the
behavior of simple robots. Neural networks could be used to model non-linear
systems. In this work, a recurrent deep neural network is proposed to estimate
the speed and yaw angle of a two-wheeled differentially driven mobile robot.
Using the information coming from the camera positioned above the test area as
ground truth, the network is trained. After that, the output of the network is
recorded in the absence of ground truth information in the network. Finally,
the performance of the network is evaluated using network output, sensor data
calculation, and ground truth.