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Applications of Intelligent Robots: Sensing, Interaction, Navigation and Control Systems

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensors and Robotics".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 6708

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Special Issue Editors

Dr. Daniel Galan

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Guest Editor

Department of Automatic Control, Electrical and Electronics Engieneering and Industrial Computing, Universidad Politécnica de Madrid, Madrid, Spain
Interests: control education; machine learning; autonomous systems; fuzzy control; social robots; human–robot interaction
Special Issues, Collections and Topics in MDPI journals

Dr. Ramon A. Suarez Fernandez

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Guest Editor

Department of Automatic Control, Electrical and Electronics Engineering and Industrial Computing, Universidad Politécnica de Madrid, Madrid, Spain
Interests: robots; underwater robots; robot control; nonlinear control

Dr. Francisco Javier Badesa

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Guest Editor

Department of Automatic Control, Electrical and Electronics Engineering and Industrial Computing, Universidad Politécnica de Madrid, Madrid, Spain
Interests: rehabilitation robotics; assistive robotics; exoskeleton; human-robot interaction; bio-signal processing; machine learning

Special Issue Information

Dear Colleagues,

The latest advances in artificial intelligence are revolutionizing all sectors, including robotics. On the one hand, industrial robotics has produced significant advances in sensing, perception, manipulation, and collaboration with humans. On the other hand, social robotics has benefited from these developments by generating better conversational agents, improving the identification of emotions, and expanding the range of tasks it can perform.

This Special Issue aims to showcase all research in which robotics is enriched by integrating the latest trends in artificial intelligence. Authors are invited to submit high-quality papers on topics including (but not limited to) the following:

Robot localization and navigation.
Rehabilitation robotics.
Assistive robotics.
Exoskeleton.
Autonomous robots (air, land, sea, submarine, or aerospace) in unstructured environments.
Social robotics.
Emotion recognition.
Human–robot interaction.
Bio-signal processing.
Machine learning
Cognitive robotics.
System control.
Human–robot collaboration technologies.
Multi-agent robotic systems.

Dr. Daniel Galan
Dr. Ramon A. Suarez Fernandez
Dr. Francisco Javier Badesa
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (6 papers)

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Research

17 pages, 7168 KiB

Open AccessArticle

Fast 50 Hz Updated Static Infrared Positioning System Based on Triangulation Method

by Maciej Ciężkowski and Rafał Kociszewski

Sensors 2024, 24(5), 1389; https://doi.org/10.3390/s24051389 - 21 Feb 2024

Viewed by 433

Abstract

One of the important issues being explored in Industry 4.0 is collaborative mobile robots. This collaboration requires precise navigation systems, especially indoor navigation systems where GNSS (Global Navigation Satellite System) cannot be used. To enable the precise localization of robots, different variations of navigation systems are being developed, mainly based on trilateration and triangulation methods. Triangulation systems are distinguished by the fact that they allow for the precise determination of an object’s orientation, which is important for mobile robots. An important feature of positioning systems is the frequency of position updates based on measurements. For most systems, it is 10–20 Hz. In our work, we propose a high-speed 50 Hz positioning system based on the triangulation method with infrared transmitters and receivers. In addition, our system is completely static, i.e., it has no moving/rotating measurement sensors, which makes it more resistant to disturbances (caused by vibrations, wear and tear of components, etc.). In this paper, we describe the principle of the system as well as its design. Finally, we present tests of the built system, which show a beacon bearing accuracy of

Δ φ

= 0.51

°

, which corresponds to a positioning accuracy of

Δ R

= 6.55 cm, with a position update frequency of

f_{u p d a t e}

= 50 Hz. Full article

(This article belongs to the Special Issue Applications of Intelligent Robots: Sensing, Interaction, Navigation and Control Systems)

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20 pages, 8194 KiB

Open AccessArticle

Novel near E-Field Topography Sensor for Human–Machine Interfacing in Robotic Applications

by Dariusz J. Skoraczynski and Chao Chen

Sensors 2024, 24(5), 1379; https://doi.org/10.3390/s24051379 - 21 Feb 2024

Viewed by 543

Abstract

This work investigates a new sensing technology for use in robotic human–machine interface (HMI) applications. The proposed method uses near E-field sensing to measure small changes in the limb surface topography due to muscle actuation over time. The sensors introduced in this work provide a non-contact, low-computational-cost, and low-noise method for sensing muscle activity. By evaluating the key sensor characteristics, such as accuracy, hysteresis, and resolution, the performance of this sensor is validated. Then, to understand the potential performance in intention detection, the unmodified digital output of the sensor is analysed against movements of the hand and fingers. This is done to demonstrate the worst-case scenario and to show that the sensor provides highly targeted and relevant data on muscle activation before any further processing. Finally, a convolutional neural network is used to perform joint angle prediction over nine degrees of freedom, achieving high-level regression performance with an RMSE value of less than six degrees for thumb and wrist movements and 11 degrees for finger movements. This work demonstrates the promising performance of this novel approach to sensing for use in human–machine interfaces. Full article

(This article belongs to the Special Issue Applications of Intelligent Robots: Sensing, Interaction, Navigation and Control Systems)

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21 pages, 6387 KiB

Open AccessArticle

A Novel Robotic Controller Using Neural Engineering Framework-Based Spiking Neural Networks

by Dailin Marrero, John Kern and Claudio Urrea

Sensors 2024, 24(2), 491; https://doi.org/10.3390/s24020491 - 12 Jan 2024

Viewed by 831

Abstract

This paper investigates spiking neural networks (SNN) for novel robotic controllers with the aim of improving accuracy in trajectory tracking. By emulating the operation of the human brain through the incorporation of temporal coding mechanisms, SNN offer greater adaptability and efficiency in information processing, providing significant advantages in the representation of temporal information in robotic arm control compared to conventional neural networks. Exploring specific implementations of SNN in robot control, this study analyzes neuron models and learning mechanisms inherent to SNN. Based on the principles of the Neural Engineering Framework (NEF), a novel spiking PID controller is designed and simulated for a 3-DoF robotic arm using Nengo and MATLAB R2022b. The controller demonstrated good accuracy and efficiency in following designated trajectories, showing minimal deviations, overshoots, or oscillations. A thorough quantitative assessment, utilizing performance metrics like root mean square error (RMSE) and the integral of the absolute value of the time-weighted error (ITAE), provides additional validation for the efficacy of the SNN-based controller. Competitive performance was observed, surpassing a fuzzy controller by 5% in terms of the ITAE index and a conventional PID controller by 6% in the ITAE index and 30% in RMSE performance. This work highlights the utility of NEF and SNN in developing effective robotic controllers, laying the groundwork for future research focused on SNN adaptability in dynamic environments and advanced robotic applications. Full article

(This article belongs to the Special Issue Applications of Intelligent Robots: Sensing, Interaction, Navigation and Control Systems)

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17 pages, 16981 KiB

Open AccessArticle

Human–Robot Interaction Using Learning from Demonstrations and a Wearable Glove with Multiple Sensors

by Rajmeet Singh, Saeed Mozaffari, Masoud Akhshik, Mohammed Jalal Ahamed, Simon Rondeau-Gagné and Shahpour Alirezaee

Sensors 2023, 23(24), 9780; https://doi.org/10.3390/s23249780 - 12 Dec 2023

Cited by 1 | Viewed by 881

Abstract

Human–robot interaction is of the utmost importance as it enables seamless collaboration and communication between humans and robots, leading to enhanced productivity and efficiency. It involves gathering data from humans, transmitting the data to a robot for execution, and providing feedback to the human. To perform complex tasks, such as robotic grasping and manipulation, which require both human intelligence and robotic capabilities, effective interaction modes are required. To address this issue, we use a wearable glove to collect relevant data from a human demonstrator for improved human–robot interaction. Accelerometer, pressure, and flexi sensors were embedded in the wearable glove to measure motion and force information for handling objects of different sizes, materials, and conditions. A machine learning algorithm is proposed to recognize grasp orientation and position, based on the multi-sensor fusion method. Full article

(This article belongs to the Special Issue Applications of Intelligent Robots: Sensing, Interaction, Navigation and Control Systems)

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12 pages, 2378 KiB

Open AccessArticle

Analysis of Differences in Single-Joint Movement of Dominant and Non-Dominant Hands for Human-like Robotic Control

by Samyoung Kim, Kyuengbo Min, Yeongdae Kim, Shigeyuki Igarashi, Daeyoung Kim, Hyeonseok Kim and Jongho Lee

Sensors 2023, 23(23), 9443; https://doi.org/10.3390/s23239443 - 27 Nov 2023

Viewed by 690

Abstract

Although several previous studies on laterality of upper limb motor control have reported functional differences, this conclusion has not been agreed upon. It may be conjectured that the inconsistent results were caused because upper limb motor control was observed in multi-joint tasks that could generate different inter-joint motor coordination for each arm. Resolving this, we employed a single wrist joint tracking task to reduce the effect of multi-joint dynamics and examined the differences between the dominant and non-dominant hands in terms of motor control. Specifically, we defined two sections to induce feedback (FB) and feedforward (FF) controls: the first section involved a visible target for FB control, and the other section involved an invisible target for FF control. We examined the differences in the position errors of the tracer and the target. Fourteen healthy participants performed the task. As a result, we found that during FB control, the dominant hand performed better than the non-dominant hand, while we did not observe significant differences in FF control. In other words, in a single-joint movement that is not under the influence of the multi-joint coordination, only FB control showed laterality and not FF control. Furthermore, we confirmed that the dominant hand outperformed the non-dominant hand in terms of responding to situations that required a change in control strategy. Full article

(This article belongs to the Special Issue Applications of Intelligent Robots: Sensing, Interaction, Navigation and Control Systems)

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15 pages, 2830 KiB

Open AccessArticle

Particle Swarm Algorithm Path-Planning Method for Mobile Robots Based on Artificial Potential Fields

by Li Zheng, Wenjie Yu, Guangxu Li, Guangxu Qin and Yunchuan Luo

Sensors 2023, 23(13), 6082; https://doi.org/10.3390/s23136082 - 01 Jul 2023

Cited by 8 | Viewed by 2805

Abstract

Path planning is an important part of the navigation control system of mobile robots since it plays a decisive role in whether mobile robots can realize autonomy and intelligence. The particle swarm algorithm can effectively solve the path-planning problem of a mobile robot, but the traditional particle swarm algorithm has the problems of a too-long path, poor global search ability, and local development ability. Moreover, the existence of obstacles makes the actual environment more complex, thus putting forward more stringent requirements on the environmental adaptation ability, path-planning accuracy, and path-planning efficiency of mobile robots. In this study, an artificial potential field-based particle swarm algorithm (apfrPSO) was proposed. First, the method generates robot planning paths by adjusting the inertia weight parameter and ranking the position vector of particles (rPSO), and second, the artificial potential field method is introduced. Through comparative numerical experiments with other state-of-the-art algorithms, the results show that the algorithm proposed was very competitive. Full article

(This article belongs to the Special Issue Applications of Intelligent Robots: Sensing, Interaction, Navigation and Control Systems)

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