Regular Papers

International Journal of Control, Automation, and Systems 2024; 22(7): 2273-2282

https://doi.org/10.1007/s12555-023-0288-8

© The International Journal of Control, Automation, and Systems

Design and Modeling of a Two-wheeled Differential Drive Robot

John Moritz*, Mishek Musa, and Uche Wejinya

University of Arkansas

Abstract

There are many existing publications on balancing two-wheeled, differential drive robot (TWDDR) covering dynamic modeling, kinematic modeling, path planning, control architecture design and/or simulations. However, there are few papers that cover all of these in a comprehensive manner that is approachable to beginner robotics researchers. This paper provides step-by-step details of the robotic design process including dynamic modeling, kinematic modeling, linearization, autonomous navigation, path planning, and stability control. A cascaded PID control architecture is presented that is capable of stabilizing the robot in less than 1 s with minimal steady-state error and performing large force and torque disturbance rejection. Additionally, a high-level path planning algorithm based on artificial potential fields is demonstrated.

Keywords Artificial potential fields, mobile robotics, nonlinear dynamics, PID control.

Article

Regular Papers

International Journal of Control, Automation, and Systems 2024; 22(7): 2273-2282

Published online July 1, 2024 https://doi.org/10.1007/s12555-023-0288-8

Copyright © The International Journal of Control, Automation, and Systems.

Design and Modeling of a Two-wheeled Differential Drive Robot

John Moritz*, Mishek Musa, and Uche Wejinya

University of Arkansas

Abstract

There are many existing publications on balancing two-wheeled, differential drive robot (TWDDR) covering dynamic modeling, kinematic modeling, path planning, control architecture design and/or simulations. However, there are few papers that cover all of these in a comprehensive manner that is approachable to beginner robotics researchers. This paper provides step-by-step details of the robotic design process including dynamic modeling, kinematic modeling, linearization, autonomous navigation, path planning, and stability control. A cascaded PID control architecture is presented that is capable of stabilizing the robot in less than 1 s with minimal steady-state error and performing large force and torque disturbance rejection. Additionally, a high-level path planning algorithm based on artificial potential fields is demonstrated.

Keywords: Artificial potential fields, mobile robotics, nonlinear dynamics, PID control.

IJCAS
July 2024

Vol. 22, No. 7, pp. 2055~2340

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