General Information

Title: Application of PID controller and CNN to control Duckiebot robot
Authors: Marek Długosz, Paweł Skruch, Marcin Szelest, Artur Morys-Magiera
Institution: AGH University of Science and Technology, Poland
Citation: M. Długosz, P. Skruch, M. Szelest and A. Morys-Magiera, "Application of PID controller and CNN to control Duckiebot robot," 2023 21st International Conference on Emerging eLearning Technologies and Applications (ICETA), Stary Smokovec, Slovakia, 2023, pp. 105-110, doi: 10.1109/ICETA61311.2023.10344003.

PID and Convolutional Neural Networks (CNN) in Duckietown

Ever wondered how the legendary PID controller compares to a more “modern” convolutional neural network (CNN) design, in controlling a Duckiebot in driving in Duckietown?

This work analyzes the performance differences between classical control techniques and machine learning-based approaches for autonomous navigation. The Duckiebot follows a designated path using image-based feedback, where the PID controller corrects deviations through proportional, integral, and derivative adjustments. The CNN-based method leverages image feature extraction to generate control commands, reducing reliance on predefined system models.

Key aspects covered include differential drive mechanics, real-time image processing, and ROS-based implementation. The study also outlines the impact of training data selection on CNN performance. Comparative analysis highlights the strengths and limitations of both approaches. The conclusions emphasize the applicability of PID and CNN techniques in Duckietown, demonstrating their role in advancing robotic autonomy.

Highlights - PID and Convolutional Neural Network (CNN) in Duckietown

Here is a visual tour of the work of the authors. For all the details, check out the full paper.

PID-controlled Duckiebot robot used for autonomous navigation in Duckietown — Figure 1. Duckiebot in Duckietown.

PID-based kinematic system of Duckiebot in Duckietown — Figure 2. Kinematic Model of Duckiebot in Duckietown.

PID controller schematic for Duckiebot navigation in Duckietown — Figure 3. PID-Based Control System for Duckiebot in Duckietown.

PID-based error detection for Duckiebot line following in Duckietown — Figure 4. Error Calculation for PID Line Following in Duckiebot.

PID-controlled Duckiebot image processing in Duckietown for position error detection — Figure 5. Image Processing for PID Error Detection in Duckiebot.

PID alternative CNN-based Duckiebot control system in Duckietown — Figure 6. CNN-Based Control System for Duckiebot in Duckietown.

PID-free CNN architecture for Duckiebot control in Duckietown — Figure 7. CNN Architecture for Duckiebot in Duckietown.

Histogram of PID and CNN training data for Duckiebot control in Duckietown — Figure 8. Data Distribution for Duckiebot Training in Duckietown.

PID versus CNN image preprocessing for Duckiebot control in Duckietown — Figure 9. Image Preprocessing for CNN-Based Duckiebot Control in Duckietown.

PID and CNN control development setup for Duckiebot in Duckietown — Figure 10. Duckiebot Development Environment for PID and CNN Control.

Abstract

In the author’s words:

The paper presents the design and practical implementation by students of a control system using a classic PID controller and a controller using artificial neural networks. The control object is a Duckiebot robot, and the task it is to perform is to drive the robot along a designated line (line follower).

The purpose of the proposed activities is to familiarize students with the advantages and disadvantages of the two controllers used and for them to acquire the ability to implement control systems in practice. The article briefly describes how the two controllers work, how to practically implement them, and how to practically implement the exercise.

Conclusion - PID and Convolutional Neural Network (CNN) in Duckietown

Here are the conclusions from the author of this paper:

“The PID controller is used successfully in many control systems, and its implementation is relatively simple. There are also a number of methods and algorithms for adjusting controller parameters for this type of controller.

PID controllers, on the other hand, are not free of disadvantages. One of them is the requirement of prior knowledge of, even roughly, the model of the process one wants to control. Thus, it is necessary to identify both the structure of the process model and its parameters. Identification tasks are complex tasks, requiring a great deal of knowledge about the nature of the process itself. There are also methods for identifying process models based on the results of practical experiments, however sometimes it may not be possible to conduct such experiments. When using a PID controller, one should also be aware that it was developed for processes, operation of which can be described by linear models. Unfortunately, the behavior of the vast majority of dynamic systems is described by non-linear models.

The consequence of this fact is that, in such cases, the PID controller works using linear approximations of nonlinear systems, which can lead to various errors, inaccuracies, etc. Unlike the classic PID controller, controllers using artificial neural networks do not need to know the mathematical model of the process they control and its parameters.

The ability to design different neural network architectures, such as convolutional, recurrent, or deep neural networks, makes it possible to adapt the neural regulator to the specific process it is supposed to control. On the other hand, the multiplicity of neural network architectures and their design means that we can never be sure whether a given neural network structure is optimal.

The selection of neural controller parameters is done automatically using appropriate network training algorithms. The key element influencing the accuracy of neural regulator operation is the data used for training the neural network. The disadvantage of regulators using neural networks is the inability to demonstrate the stability of operation of the systems they control.

In case of the PID regulator, despite the use of approximate models of the process, it is very often possible to prove that a closed control system will operate stably in any or a certain range of values of variables. Unfortunately, such an analysis cannot be carried out in the case of neural regulators. In summary, the implementation of two different controllers to perform the same task provides an opportunity to learn the advantages and disadvantages of each.”

Project Authors

Marek Długosz is a Professor at the Akademia Górniczo-Hutnicza (AGH) – University of Science and Technology, Poland.

Paweł Skruch is currently working as the Manager and Principal Engineer AI at Aptiv, Switzerland.

Marcin Szelest is currently affiliated with the AGH University of Krakow, Kracow, Poland.

Artur Morys-Magiera is a a PhD candidate at AGH University of Krakow, Poland.

Learn more

Duckietown is a platform for creating and disseminating robotics and AI learning experiences.

It is modular, customizable and state-of-the-art, and designed to teach, learn, and do research. From exploring the fundamentals of computer science and automation to pushing the boundaries of knowledge, Duckietown evolves with the skills of the user.