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Applied Intelligent Control and Perception in Robotics and Automation

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: closed (20 November 2021) | Viewed by 23943

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Special Issue Editor

Prof. Dr. Federico Cuesta

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Guest Editor

Department of Systems Engineering and Automatic Control, University of Seville, E-41092 Sevilla, Spain
Interests: intelligent control; nonlinear control and stability; mobile robots; mechatronics; automation; control of microgrids and power converters

Special Issue Information

Dear Colleagues,

Intelligent control has been widely used in recent decades as a concept to encompass techniques ranging from fuzzy logic, neural networks, genetic algorithms, evolutionary computing, multiagent systems, and other Artificial Intelligence methods, to recent advances in deep learning (especially for perception) and machine learning, among others.

One of the main advantages of these methods is that they can be used as a valuable tool to perform knowledge management, modeling complex systems, advanced optimization, supervised learning (classification), unsupervised learning (clustering), etc.

These techniques by themselves, or even combined with other methods such as robust, adaptive or model predictive control, for instance, have obtained significant results in applications in different domains such as control and perception in robotics, autonomous vehicles, human–robot interaction, factory automation, industry 4.0, process control or microgrids, and energy systems, among others.

The objective of this Special Issue is to gather research and review papers that include the applications of these methods to solve problems in the above fields, exchange experiences, and promote synergies.

Topics of interest to this Special Issue include but are not limited to the following:

Intelligent control and systems;
Fuzzy logic, neural networks, genetic algorithms;
Artificial Intelligence;
Deep learning and machine learning;
Optimization;
Knowledge management;
Multiagent and multirobot systems;
Computer vision and advanced perception;
Mobile robots;
Autonomous vehicles and systems;
Human–robot interaction;
Robotics and automation;
Industry 4.0;
Process control;
Microgrids and energy systems.

Best regards,

Prof. Dr. Federico Cuesta
Guest Editor

Manuscript Submission Information

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Keywords

Intelligent control and systems
Fuzzy logic, Neural Networks, Genetic Algorithms
Artificial Intelligence
Deep Learning and Machine Learning
Optimization
Knowledge management
Multi-agent and multi-robot systems
Computer vision and advanced perception
Mobile robots
Autonomous vehicles and systems
Human-robot interaction
Robotics and Automation
Industry 4.0
Process control
Microgrids and energy systems

Published Papers (6 papers)

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Research

14 pages, 1823 KiB

Open AccessArticle

Vision-Based Robotic Arm Control Algorithm Using Deep Reinforcement Learning for Autonomous Objects Grasping

by Hiba Sekkat, Smail Tigani, Rachid Saadane and Abdellah Chehri

Appl. Sci. 2021, 11(17), 7917; https://0-doi-org.brum.beds.ac.uk/10.3390/app11177917 - 27 Aug 2021

Cited by 19 | Viewed by 7932

Abstract

While working side-by-side, humans and robots complete each other nowadays, and we may say that they work hand in hand. This study aims to evolve the grasping task by reaching the intended object based on deep reinforcement learning. Thereby, in this paper, we propose a deep deterministic policy gradient approach that can be applied to a numerous-degrees-of-freedom robotic arm towards autonomous objects grasping according to their classification and a given task. In this study, this approach is realized by a five-degrees-of-freedom robotic arm that reaches the targeted object using the inverse kinematics method. You Only Look Once v5 is employed for object detection, and backward projection is used to detect the three-dimensional position of the target. After computing the angles of the joints at the detected position by inverse kinematics, the robot’s arm is moved towards the target object’s emplacement thanks to the algorithm. Our approach provides a neural inverse kinematics solution that increases overall performance, and its simulation results reveal its advantages compared to the traditional one. The robot’s end grip joint can reach the targeted location by calculating the angle of every joint with an acceptable range of error. However, the accuracy of the angle and the posture are satisfied. Experiments reveal the performance of our proposal compared to the state-of-the-art approaches in vision-based grasp tasks. This is a new approach to grasp an object by referring to inverse kinematics. This method is not only easier than the standard one but is also more meaningful for multi-degrees of freedom robots. Full article

(This article belongs to the Special Issue Applied Intelligent Control and Perception in Robotics and Automation)

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21 pages, 3455 KiB

Open AccessArticle

Reactive Obstacle–Avoidance Systems for Wheeled Mobile Robots Based on Artificial Intelligence

by A. Medina-Santiago, Luis Alberto Morales-Rosales, Carlos Arturo Hernández-Gracidas, Ignacio Algredo-Badillo, Ana Dalia Pano-Azucena and Jorge Antonio Orozco Torres

Appl. Sci. 2021, 11(14), 6468; https://0-doi-org.brum.beds.ac.uk/10.3390/app11146468 - 13 Jul 2021

Cited by 4 | Viewed by 2335

Abstract

Obstacle–Avoidance robots have become an essential field of study in recent years. This paper analyzes two cases that extend reactive systems focused on obstacle detection and its avoidance. The scenarios explored get data from their environments through sensors and generate information for the models based on artificial intelligence to obtain a reactive decision. The main contribution is focused on the discussion of aspects that allow for comparing both approaches, such as the heuristic approach implemented, requirements, restrictions, response time, and performance. The first case presents a mobile robot that applies a fuzzy inference system (FIS) to achieve soft turning basing its decision on depth image information. The second case introduces a mobile robot based on a multilayer perceptron (MLP) architecture, which is a class of feedforward artificial neural network (ANN), and ultrasonic sensors to decide how to move in an uncontrolled environment. The analysis of both options offers perspectives to choose between reactive Obstacle–Avoidance systems based on ultrasonic or Kinect sensors, models that infer optimal decisions applying fuzzy logic or artificial neural networks, with key elements and methods to design mobile robots with wheels. Therefore, we show how AI or Fuzzy Logic techniques allow us to design mobile robots that learn from their “experience” by making them safe and adjustable for new tasks, unlike traditional robots that use large programs to perform a specific task. Full article

(This article belongs to the Special Issue Applied Intelligent Control and Perception in Robotics and Automation)

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23 pages, 6789 KiB

Open AccessArticle

Design and Implementation of an Autonomous Electric Vehicle for Self-Driving Control under GNSS-Denied Environments

by Ali Barzegar, Oualid Doukhi and Deok-Jin Lee

Appl. Sci. 2021, 11(8), 3688; https://0-doi-org.brum.beds.ac.uk/10.3390/app11083688 - 19 Apr 2021

Cited by 6 | Viewed by 3615

Abstract

In this study, the hardware and software design and implementation of an autonomous electric vehicle are addressed. We aimed to develop an autonomous electric vehicle for path tracking. Control and navigation algorithms are developed and implemented. The vehicle is able to perform path-tracking maneuvers under environments in which the positioning signals from the Global Navigation Satellite System (GNSS) are not accessible. The proposed control approach uses a modified constrained input-output nonlinear model predictive controller (NMPC) for path-tracking control. The proposed localization algorithm used in this study guarantees almost accurate position estimation under GNSS-denied environments. We discuss the procedure for designing the vehicle hardware, electronic drivers, communication architecture, localization algorithm, and controller architecture. The system’s full state is estimated by fusing visual inertial odometry (VIO) measurements with wheel odometry data using an extended Kalman filter (EKF). Simulation and real-time experiments are performed. The obtained results demonstrate that our designed autonomous vehicle is capable of performing path-tracking maneuvers without using Global Navigation Satellite System positioning data. The designed vehicle can perform challenging path-tracking maneuvers with a speed of up to 1 m per second. Full article

(This article belongs to the Special Issue Applied Intelligent Control and Perception in Robotics and Automation)

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26 pages, 13804 KiB

Open AccessFeature PaperArticle

An Android and Arduino Based Low-Cost Educational Robot with Applied Intelligent Control and Machine Learning

by Francisco M. Lopez-Rodriguez and Federico Cuesta

Appl. Sci. 2021, 11(1), 48; https://0-doi-org.brum.beds.ac.uk/10.3390/app11010048 - 23 Dec 2020

Cited by 11 | Viewed by 5042

Abstract

Applied Science requires testbeds to carry out experiments and validate in practice the results of the application of the methods. This article presents a low-cost (35–40 euros) educational mobile robot, based on Android and Arduino, integrated with Robot Operating System (ROS), together with its application for learning and teaching in the domain of intelligent automatic control, computer vision and Machine Learning. Specifically, the practical application to visual path tracking integrated with a Fuzzy Collision Risk system, that avoids collision with obstacles ahead, is shown. Likewise, a Wi-Fi positioning system is presented, which allows identifying in which room the robot is located, based on self-collected data and Machine Learning. Full article

(This article belongs to the Special Issue Applied Intelligent Control and Perception in Robotics and Automation)

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22 pages, 5258 KiB

Open AccessArticle

Contour Tracking Control of a Linear Motors-Driven X-Y-Y Stage Using Auto-Tuning Cross-Coupled 2DOF PID Control Approach

by Syuan-Yi Chen, Zi-Jie Chien, Wei-Yen Wang and Hsin-Han Chiang

Appl. Sci. 2020, 10(24), 9036; https://0-doi-org.brum.beds.ac.uk/10.3390/app10249036 - 17 Dec 2020

Cited by 4 | Viewed by 2095

Abstract

Linear motors (LMs) are widely used in numerous industry automation where precise and fast motions are required to convert electric energy into linear actuation without the need of any switching mechanism. This study aims to develop a control strategy of auto-tuning cross-coupled two-degree-of-freedom proportional-integral-derivative (ACC2PID) to achieve extremely high-precision contour control of a LMs-driven X-Y-Y stage. Three 2PID controllers are developed to control the mover positions in individual axes while two compensators are designed to eliminate the contour errors in biaxial motions. Furthermore, an improved artificial bee colony algorithm is employed as a powerful optimization technique so that all the control parameters can be concurrently evaluated and optimized online while ensuring the non-fragility of the proposed controller. In this way, the tracking error in each axis and contour errors of the biaxial motions can be concurrently minimized, and further, satisfactory positioning accuracy and synchronization performance can be achieved. Finally, the experimental comparison results confirm the validity of the proposed ACC2PID control system regarding the multi-axis contour tracking control of the highly uncertain and nonlinear LMs-driven X-Y-Y stage. Full article

(This article belongs to the Special Issue Applied Intelligent Control and Perception in Robotics and Automation)

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15 pages, 10677 KiB

Open AccessArticle

Design of an Active Vision System for High-Level Isolation Units through Q-Learning

by Andrea Gil Ruiz, Juan G. Victores, Bartek Łukawski and Carlos Balaguer

Appl. Sci. 2020, 10(17), 5927; https://0-doi-org.brum.beds.ac.uk/10.3390/app10175927 - 27 Aug 2020

Cited by 2 | Viewed by 2011

Abstract

The inspection of Personal Protective Equipment (PPE) is one of the most necessary measures when treating patients affected by infectious diseases, such as Ebola or COVID-19. Assuring the integrity of health personnel in contact with infected patients has become an important concern in developed countries. This work focuses on the study of Reinforcement Learning (RL) techniques for controlling a scanner prototype in the presence of blood traces on the PPE that could arise after contact with pathological patients. A preliminary study on the design of an agent-environment system able to simulate the required task is presented. The task has been adapted to an environment for the OpenAI Gym toolkit. The evaluation of the agent’s performance has considered the effects of different topological designs and tuning hyperparameters of the Q-Learning model-free algorithm. Results have been evaluated on the basis of average reward and timesteps per episode. The sample-average method applied to the learning rate parameter, as well as a specific epsilon decaying method worked best for the trained agents. The obtained results report promising outcomes of an inspection system able to center and magnify contaminants in the real scanner system. Full article

(This article belongs to the Special Issue Applied Intelligent Control and Perception in Robotics and Automation)

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