Maneuvering of Autonomous Vehicles through Proportional, Integral and Derivative Control Variables

Accuracy and Precision are two major goals that needs to be attained while working with autonomous vehicles or robots. Accuracy goals can be achieved in two ways i.e. using mechanical and or programming techniques. For e.g. (i) the mechanical techniques would be based on the strength of materials being used and the static variable estimations (ii) the programming techniques would be by manipulating the electrical switching of the controls and as well the power controls of the output speeds using proportional, Integral and Derivative PID controls. (iii) The sensor techniques would be using an RGB Vision camera, using Gyro, Magnetometer or Compass, Accelerometer, 9-axis Inertial Measurement Unit (IMU) sensor, and 3-axis Infra-red, and Sonar sensors. In the present study, we will be discussing our recent findings by using the above two techniques in achieving up to an accuracy of 90% cross validation accuracy for autonomous movements. We built an autonomous robot by considering the above target goals of precision and accuracy and used it in the 2017 IEEE hardware competitions for performing the desired tasks. The sensors being used for reading the input data are optical shaft encoders that work by converting angular position of the motor into a digital output value. By feeding these values from the encoder input through the estimated PID control formula, the precision is greatly increased. In addition, the accuracy will also be maximized with Gyro sensor. The Gyro sensor will help the robot in completing tasks such as following a straight line and turning an exact number of degrees. With the use of Gyro, we will always know the direction the robot that is facing. This helps in controlling the desired turn angle and direction.