Human Robot Interaction and Intelligent System Design

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 19506

Special Issue Editors

Faculty of Mathematics, Informatics and Natural Science, Universität Hamburg, D-22527 Hamburg, Germany
Interests: robot imitation learning; human-robot physical interaction
Special Issues, Collections and Topics in MDPI journals
Department of Engineering and Design, University of Sussex, Brighton BN1 9RH, UK
Interests: human-robot interaction; assistive robotics; human motor control and control theory and applications

Special Issue Information

Dear Colleagues,

Humans and robots are expected to work together closely, interactively and collaboratively, sharing working spaces, in a large number of task scenarios. First, intelligent systems need to be designed with the integration of novel and multiple sensors to capture different types of sensing information (such as image, sound, bio-signal, force, tactile) in the working environments. Correspondingly, advanced signal processing and fusion techniques are required to extract important features from multimodal data. With these as inputs, intelligent learning (e.g., imitation learning, deep learning and reinforcement learning), control (such as adaptive control, bio-inspired control) and optimization (such as black box and model-based techniques) algorithms are then needed to improve the robot manipulation abilities and to improve human–robot interaction performances. The goal of the Special Issue “Human Robot Interaction and Intelligent System Design” is to cover recent advancements in system design, advanced sensing, learning, control and optimization for human–robot interaction, as well as its novel applications.

Prof. Dr. Chenguang Yang
Dr. Chao Zeng
Dr. Yanan Li
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.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 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.

Keywords

  • human–robot interaction
  • human factors
  • intelligent systems
  • interface design
  • robot learning
  • intelligent control
  • advanced sensing
  • sensing fusion
  • motion planning
  • dexterous manipulation
  • compliant manipulation
  • multi-robot systems
  • human-inspired learning
  • bio-inspired control
  • imitation learning
  • reinforcement learning
  • machine learning in robotics
  • optimization techniques
  • robot applications

Published Papers (12 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

15 pages, 8018 KiB  
Article
Transformer-Based Integrated Framework for Joint Reconstruction and Segmentation in Accelerated Knee MRI
by Hongki Lim
Electronics 2023, 12(21), 4434; https://doi.org/10.3390/electronics12214434 - 27 Oct 2023
Viewed by 786
Abstract
Magnetic Resonance Imaging (MRI) reconstruction and segmentation are crucial for medical diagnostics and treatment planning. Despite advances, achieving high performance in both tasks remains challenging, especially in the context of accelerated MRI acquisition. Motivated by this challenge, the objective of this study is [...] Read more.
Magnetic Resonance Imaging (MRI) reconstruction and segmentation are crucial for medical diagnostics and treatment planning. Despite advances, achieving high performance in both tasks remains challenging, especially in the context of accelerated MRI acquisition. Motivated by this challenge, the objective of this study is to develop an integrated approach for MRI image reconstruction and segmentation specifically tailored for accelerated acquisition scenarios. The proposed method unifies these tasks by incorporating segmentation feedback into an iterative reconstruction algorithm and using a transformer-based encoder–decoder architecture. This architecture consists of a shared encoder and task-specific decoders, and employs a feature distillation process between the decoders. The proposed model is evaluated on the Stanford Knee MRI with Multi-Task Evaluation (SKM-TEA) dataset against established methods such as SegNetMRI and IDSLR-Seg. The results show improvements in the PSNR, SSIM, Dice, and Hausdorff distance metrics. An ablation study confirms the contribution of feature distillation and segmentation feedback to the performance gains. The advancements demonstrated in this study have the potential to impact clinical practice by facilitating more accurate diagnosis and better-informed treatment plans. Full article
(This article belongs to the Special Issue Human Robot Interaction and Intelligent System Design)
Show Figures

Figure 1

28 pages, 9751 KiB  
Article
An Indoor Tags Position Perception Method Based on GWO–MLP Algorithm for RFID Robot
by Honggang Wang, Yu Zhang, Sicheng Li, Qinyan Huang, Ruoyu Pan, Shengli Pang and Jingfeng Yang
Electronics 2023, 12(19), 4076; https://doi.org/10.3390/electronics12194076 - 28 Sep 2023
Viewed by 661
Abstract
This paper proposes a tag position perception method for scenarios such as package retrieval in unmanned warehouses and book management in libraries. This method can accurately predict the distribution of tag space positions in real–time during RFID robot inventory. Firstly, the signal strength [...] Read more.
This paper proposes a tag position perception method for scenarios such as package retrieval in unmanned warehouses and book management in libraries. This method can accurately predict the distribution of tag space positions in real–time during RFID robot inventory. Firstly, the signal strength (RSSI) and speed of identification (SoI) are used as features. The grey wolf optimization multi–layer perceptron neural network model (GWO–MLP) is employed to predict the distance of tag groups. Secondly, a tag orientation prediction algorithm is designed to estimate the orientation of the tag groups. Finally, the periodicity of the phase is determined by the characteristic of RSSI attenuation as the tag–to–antenna distance increases, solving the problem of position ambiguity caused by phase periodicity. The experiment has shown that this method achieves a high accuracy rate of 96.67% and 97% in predicting the distance and orientation of tag groups, respectively. The average error in distance perception for the single tag is less than 3 cm, enabling precise perception of RFID tag positions. This method facilitates more efficient operation management and accurate item traceability. Full article
(This article belongs to the Special Issue Human Robot Interaction and Intelligent System Design)
Show Figures

Figure 1

20 pages, 7463 KiB  
Article
Cognitive Workload Classification in Industry 5.0 Applications: Electroencephalography-Based Bi-Directional Gated Network Approach
by Muhammad Abrar Afzal, Zhenyu Gu, Bilal Afzal and Syed Umer Bukhari
Electronics 2023, 12(19), 4008; https://doi.org/10.3390/electronics12194008 - 23 Sep 2023
Viewed by 990
Abstract
In the era of Industry 5.0, effectively managing cognitive workload is crucial for optimizing human performance and ensuring operational efficiency. Using an EEG-based Bi-directional Gated Network (BDGN) approach, this study tries to figure out how to classify cognitive workload in Industry 5.0 applications. [...] Read more.
In the era of Industry 5.0, effectively managing cognitive workload is crucial for optimizing human performance and ensuring operational efficiency. Using an EEG-based Bi-directional Gated Network (BDGN) approach, this study tries to figure out how to classify cognitive workload in Industry 5.0 applications. The proposed approach incorporates LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) models in a hybrid architecture to leverage their complementary strengths. This research highlights the utilization of the developed model alongside the MQTT (Message Queuing Telemetry Transport) protocol to facilitate real-time end-to-end data transmission. The deployed AI model performs the classification of cognitive workload based on the received data. The main findings of this research reveal an impressive accuracy of 98% in cognitive workload classification, validating the efficacy of the suggested BDGN approach. This study emphasizes the significance of leveraging EEG-based approaches in Industry 5.0 applications for cognitive workload management. Full article
(This article belongs to the Special Issue Human Robot Interaction and Intelligent System Design)
Show Figures

Figure 1

22 pages, 10307 KiB  
Article
A Modular Haptic Agent System with Encountered-Type Active Interaction
by Xiaonuo Dongye, Dongdong Weng, Haiyan Jiang and Lulu Feng
Electronics 2023, 12(9), 2069; https://doi.org/10.3390/electronics12092069 - 30 Apr 2023
Cited by 1 | Viewed by 1356
Abstract
Virtual agents are artificial intelligence systems that can interact with users in virtual reality (VR), providing users with companionship and entertainment. Virtual pets have become the most popular virtual agents due to their many benefits. However, haptic interaction with virtual pets involves two [...] Read more.
Virtual agents are artificial intelligence systems that can interact with users in virtual reality (VR), providing users with companionship and entertainment. Virtual pets have become the most popular virtual agents due to their many benefits. However, haptic interaction with virtual pets involves two challenges: the rapid construction of various haptic proxies, and the design of agent-initiated active interaction. In this paper, we propose a modular haptic agent (MHA) prototype system, enabling the tactile simulation and encountered-type haptic interaction of common virtual pet agents through a modular design method and a haptic mapping method. Meanwhile, the MHA system with haptic interaction is actively initiated by the agents according to the user’s intention, which makes the virtual agents appear more autonomous and provides a better experience of human–agent interaction. Finally, we conduct three user studies to demonstrate that the MHA system has more advantages in terms of realism, interactivity, attraction, and raising user emotions. Overall, MHA is a system that can build multiple companion agents, provide active interaction and has the potential to quickly build diverse haptic agents for an intelligent and comfortable virtual world. Full article
(This article belongs to the Special Issue Human Robot Interaction and Intelligent System Design)
Show Figures

Figure 1

19 pages, 3051 KiB  
Article
Pyramidal Predictive Network: A Model for Visual-Frame Prediction Based on Predictive Coding Theory
by Chaofan Ling, Junpei Zhong and Weihua Li
Electronics 2022, 11(18), 2969; https://doi.org/10.3390/electronics11182969 - 19 Sep 2022
Cited by 1 | Viewed by 1380
Abstract
Visual-frame prediction is a pixel-dense prediction task that infers future frames from past frames. A lack of appearance details, low prediction accuracy and a high computational overhead are still major problems associated with current models or methods. In this paper, we propose a [...] Read more.
Visual-frame prediction is a pixel-dense prediction task that infers future frames from past frames. A lack of appearance details, low prediction accuracy and a high computational overhead are still major problems associated with current models or methods. In this paper, we propose a novel neural network model inspired by the well-known predictive coding theory to deal with these problems. Predictive coding provides an interesting and reliable computational framework. We combined this approach with other theories, such as the theory that the cerebral cortex oscillates at different frequencies at different levels, to design an efficient and reliable predictive network model for visual-frame prediction. Specifically, the model is composed of a series of recurrent and convolutional units forming the top-down and bottom-up streams, respectively. The update frequency of neural units on each of the layers decreases with the increase in the network level, which means that neurons of a higher level can capture information in longer time dimensions. According to the experimental results, this model showed better compactness and comparable predictive performance with those of existing works, implying lower computational cost and higher prediction accuracy. Full article
(This article belongs to the Special Issue Human Robot Interaction and Intelligent System Design)
Show Figures

Figure 1

14 pages, 2055 KiB  
Article
Power-Efficient Trainable Neural Networks towards Accurate Measurement of Irregular Cavity Volume
by Xin Zhang, Yueqiu Jiang, Hongwei Gao, Wei Yang, Zhihong Liang and Bo Liu
Electronics 2022, 11(13), 2073; https://doi.org/10.3390/electronics11132073 - 01 Jul 2022
Viewed by 1067
Abstract
Irregular cavity volume measurement is a critical step in industrial production. This technology is used in a wide variety of applications. Traditional studies, such as waterflooding-based methods, have suffered from the following shortcomings, i.e., significant measurement error, low efficiency, complicated operation, and corrosion [...] Read more.
Irregular cavity volume measurement is a critical step in industrial production. This technology is used in a wide variety of applications. Traditional studies, such as waterflooding-based methods, have suffered from the following shortcomings, i.e., significant measurement error, low efficiency, complicated operation, and corrosion of devices. Recently, neural networks based on the air compression principle have been proposed to achieve irregular cavity volume measurement. However, the balance between data quality, network computation speed, convergence, and measurement accuracy is still underexplored. In this paper, we propose novel neural networks to achieve accurate measurement of irregular cavity volume. First, we propose a measurement method based on the air compression principle to analyze seven key parameters comprehensively. Moreover, we integrate the Hilbert–Schmidt independence criterion (HSIC) into fully connected neural networks (FCNNs) to build a trainable framework. This enables the proposed method to achieve power-efficient training. We evaluate the proposed neural network in the real world and compare it with typical procedures. The results show that the proposed method achieves the top performance for measurement accuracy and efficiency. Full article
(This article belongs to the Special Issue Human Robot Interaction and Intelligent System Design)
Show Figures

Figure 1

17 pages, 7038 KiB  
Article
An Improved Supervoxel Clustering Algorithm of 3D Point Clouds for the Localization of Industrial Robots
by Zhexin Xie, Peidong Liang, Jin Tao, Liang Zeng, Ziyang Zhao, Xiang Cheng, Jianhuan Zhang and Chentao Zhang
Electronics 2022, 11(10), 1612; https://doi.org/10.3390/electronics11101612 - 18 May 2022
Cited by 5 | Viewed by 2246
Abstract
Supervoxels have a widespread application of instance segmentation on account of the merit of providing a highly approximate representation with fewer data. However, low accuracy, mainly caused by point cloud adhesion in the localization of industrial robots, is a crucial issue. An improved [...] Read more.
Supervoxels have a widespread application of instance segmentation on account of the merit of providing a highly approximate representation with fewer data. However, low accuracy, mainly caused by point cloud adhesion in the localization of industrial robots, is a crucial issue. An improved bottom-up clustering method based on supervoxels was proposed for better accuracy. Firstly, point cloud data were preprocessed to eliminate the noise points and background. Then, improved supervoxel over-segmentation with moving least squares (MLS) surface fitting was employed to segment the point clouds of workpieces into supervoxel clusters. Every supervoxel cluster can be refined by MLS surface fitting, which reduces the occurrence that over-segmentation divides the point clouds of two objects into a patch. Additionally, an adaptive merging algorithm based on fusion features and convexity judgment was proposed to accomplish the clustering of the individual workpiece. An experimental platform was set up to verify the proposed method. The experimental results showed that the recognition accuracy and the recognition rate in three different kinds of workpieces were all over 0.980 and 0.935, respectively. Combined with the sample consensus initial alignment (SAC-IA) coarse registration and iterative closest point (ICP) fine registration, the coarse-to-fine strategy was adopted to obtain the location of the segmented workpieces in the experiments. The experimental results demonstrate that the proposed clustering algorithm can accomplish the localization of industrial robots with higher accuracy and lower registration time. Full article
(This article belongs to the Special Issue Human Robot Interaction and Intelligent System Design)
Show Figures

Figure 1

11 pages, 1999 KiB  
Article
A Bus-Scheduling Method Based on Multi-Sensor Data Fusion and Payment Authenticity Verification
by Wenan Gong, Ting Zeng, Haina Song, Jiayi Su, Honggang Wang, Lin Hu, Jinchao Xiao, Xiaosong Liu, Ming Li and Jingfeng Yang
Electronics 2022, 11(10), 1522; https://doi.org/10.3390/electronics11101522 - 10 May 2022
Cited by 2 | Viewed by 1503
Abstract
It is of great significance to ensure public transportation management capabilities by improving urban public transport services. One method is to solve the problems related to the quality of data submitted for public funding as well as the accuracy and transparency of the [...] Read more.
It is of great significance to ensure public transportation management capabilities by improving urban public transport services. One method is to solve the problems related to the quality of data submitted for public funding as well as the accuracy and transparency of the supervision and review processes; moreover, improving public-transportation-service systems is a viable method to solve such problems. With technological advancements and the application of new technologies such as automatic driving and multiple payment, it has gradually become difficult for user-data verification systems, based on the original single bus payment method, to cater to these new technologies. Diversified payment and complex management methods have highlighted the need for new verification methods. Firstly, in this paper, we constructed the Origin–Destination (OD) model of bus-passenger flows by using real-time transmission of passenger-multiple-payment data, on-board-video passenger flow detection data and vehicle real-time positioning data. On this basis, the bus waybill data of other intelligent bus systems and the wait data of bus stations were integrated, so as to establish the travel chain theory by matching passenger flow and the temporal and spatial distribution model. Then, an OD analysis of public-transport passenger flows could be carried out, with a detailed analysis of vehicle, station and line-passenger flow, and the travel characteristics of public transport passenger flow could be excavated. Then, according to the means-end chain theory, the spatiotemporal distribution of the passenger flow data was obtained to carry out an OD analysis of the passenger flow, so as to perform a refinement analysis of the vehicle, station, and passenger flow. Thereby, the characteristics of the passenger flow were explored. Subsequently, payment-authenticity-verification models were established for the data-validity assessment, video-data analysis, passenger-flow estimation, and early warnings in order to determine the authenticity of the payment data. Lastly, based on the multi-sensor passenger flow data fusion and the data authenticity verification models, combined with the application of new technologies such as the use of autonomous buses, the test was promoted. That is, by taking intelligent bus scheduling as the scenario, the research method was tested and verified with real-time passenger flow data according to historical data. The results showed that the method accurately predicted the passenger flow, and had a positive role in improving the efficiency of payment-data-authenticity verification. The application of the method can enhance the management and service quality of public transportation. Full article
(This article belongs to the Special Issue Human Robot Interaction and Intelligent System Design)
Show Figures

Figure 1

12 pages, 4307 KiB  
Article
Intelligent Bus Scheduling Control Based on On-Board Bus Controller and Simulated Annealing Genetic Algorithm
by Jiehan Yu, Zhendong Xie, Zhiguo Dong, Haina Song, Jiayi Su, Honggang Wang, Jinchao Xiao, Xiaosong Liu and Jingfeng Yang
Electronics 2022, 11(10), 1520; https://doi.org/10.3390/electronics11101520 - 10 May 2022
Cited by 5 | Viewed by 1361
Abstract
The stable and fast service of a bus network is one of the important indicators of the service quality and management level of urban public transport. With the continuous expansion of cities, the bus network complexity has been increasing accordingly. The application of [...] Read more.
The stable and fast service of a bus network is one of the important indicators of the service quality and management level of urban public transport. With the continuous expansion of cities, the bus network complexity has been increasing accordingly. The application of new technologies such as self-driving buses has made the bus network more complex and its vulnerability more obvious. Therefore, how to collect information on passenger flow, traffic flow, and transport distribution using intelligent means, and how to establish an effective intelligent bus scheduling control method have been important questions surrounding the improvement of the level of urban bus operation. To address this challenge, this paper proposes the design method of a bus controller based on data collection and the edge computing requirements of autonomous driving buses; and installs them widely on buses. In addition, an intelligent bus control scheduling method based on the simulated annealing genetic algorithm was developed according to the current scheduling requirements. The proposed method combines the strong local search ability of the simulated annealing algorithm, which prevents the search process from falling into a local optimum, and the strong search ability of the genetic algorithm in the overall search process, leading an intelligent bus control scheduling method based on the simulated annealing genetic algorithm. The proposed method was verified by experiments on the optimal scheduling of multi-destination public transport as an example, we verified the research method, and finally, simulated it using historical data. There is good model prediction of the experimental results. Therefore, the intelligent traffic control can be realized through efficient bus scheduling, thus improving the robustness of the bus network operation. Full article
(This article belongs to the Special Issue Human Robot Interaction and Intelligent System Design)
Show Figures

Figure 1

17 pages, 1204 KiB  
Article
Finite-Time Neural Network Fault-Tolerant Control for Robotic Manipulators under Multiple Constraints
by Zhao Zhang, Lingxi Peng, Jianing Zhang and Xiaowei Wang
Electronics 2022, 11(9), 1343; https://doi.org/10.3390/electronics11091343 - 23 Apr 2022
Cited by 5 | Viewed by 1507
Abstract
In this study, a backstepping-based fault-tolerant controller for a robotic manipulator system with input and output constraints was developed. First, a barrier Lyapunov function was adopted to ensure that the system output satisfied time-varying constraints. Subsequently, the actuator input saturation and asymmetric dead-zone [...] Read more.
In this study, a backstepping-based fault-tolerant controller for a robotic manipulator system with input and output constraints was developed. First, a barrier Lyapunov function was adopted to ensure that the system output satisfied time-varying constraints. Subsequently, the actuator input saturation and asymmetric dead-zone characteristics were also considered, and the actuator characteristics were described using a continuous function. The impacts of actuator failures and unknown dynamical parameters of the system were eliminated by employing Gaussian radial basis function neural networks. The external disturbances were compensated for, using a disturbance observer. Meanwhile, a finite-time dynamic surface technique was adopted to accelerate the convergence of the system errors. Finally, simulation of a 2-degrees-of-freedom robotic manipulator system showed the effectiveness of the proposed controller. Full article
(This article belongs to the Special Issue Human Robot Interaction and Intelligent System Design)
Show Figures

Figure 1

17 pages, 6665 KiB  
Article
Research of Hand–Eye System with 3D Vision towards Flexible Assembly Application
by Peidong Liang, Wenwei Lin, Guantai Luo and Chentao Zhang
Electronics 2022, 11(3), 354; https://doi.org/10.3390/electronics11030354 - 24 Jan 2022
Cited by 9 | Viewed by 2695
Abstract
In order to improve industrial production efficiency, a hand–eye system based on 3D vision is proposed and the proposed system is applied to the assembly task of workpieces. First, a hand–eye calibration optimization algorithm based on data filtering is proposed in this paper. [...] Read more.
In order to improve industrial production efficiency, a hand–eye system based on 3D vision is proposed and the proposed system is applied to the assembly task of workpieces. First, a hand–eye calibration optimization algorithm based on data filtering is proposed in this paper. This method ensures the accuracy required for hand–eye calibration by filtering out part of the improper data. Furthermore, the improved U-net is adopted for image segmentation and SAC-IA coarse registration ICP fine registration method is adopted for point cloud registration. This method ensures that the 6D pose estimation of the object is more accurate. Through the hand–eye calibration method based on data filtering, the average error of hand–eye calibration is reduced by 0.42 mm to 0.08 mm. Compared with other models, the improved U-net proposed in this paper has higher accuracy for depth image segmentation, and the Acc coefficient and Dice coefficient achieve 0.961 and 0.876, respectively. The average translation error, average rotation error and average time-consuming of the object recognition and pose estimation methods proposed in this paper are 1.19 mm, 1.27°, and 7.5 s, respectively. The experimental results show that the proposed system in this paper can complete high-precision assembly tasks. Full article
(This article belongs to the Special Issue Human Robot Interaction and Intelligent System Design)
Show Figures

Figure 1

11 pages, 4591 KiB  
Article
Efficient Iterative Regularization Method for Total Variation-Based Image Restoration
by Ge Ma, Ziwei Yan, Zhifu Li and Zhijia Zhao
Electronics 2022, 11(2), 258; https://doi.org/10.3390/electronics11020258 - 14 Jan 2022
Cited by 2 | Viewed by 1876
Abstract
Total variation (TV) regularization has received much attention in image restoration applications because of its advantages in denoising and preserving details. A common approach to address TV-based image restoration is to design a specific algorithm for solving typical cost function, [...] Read more.
Total variation (TV) regularization has received much attention in image restoration applications because of its advantages in denoising and preserving details. A common approach to address TV-based image restoration is to design a specific algorithm for solving typical cost function, which consists of conventional 2 fidelity term and TV regularization. In this work, a novel objective function and an efficient algorithm are proposed. Firstly, a pseudoinverse transform-based fidelity term is imposed on TV regularization, and a closely-related optimization problem is established. Then, the split Bregman framework is used to decouple the complex inverse problem into subproblems to reduce computational complexity. Finally, numerical experiments show that the proposed method can obtain satisfactory restoration results with fewer iterations. Combined with the restoration effect and efficiency, this method is superior to the competitive algorithm. Significantly, the proposed method has the advantage of a simple solving structure, which can be easily extended to other image processing applications. Full article
(This article belongs to the Special Issue Human Robot Interaction and Intelligent System Design)
Show Figures

Figure 1

Back to TopTop