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Electronics
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New Technologies and Applications of Human-Robot Intelligence
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New Technologies and Applications of Human-Robot Intelligence

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

Deadline for manuscript submissions: closed (20 December 2023) | Viewed by 5967

Special Issue Editors

Prof. Dr. Yang Xu

E-Mail Website
Guest Editor
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: human–robot hybrid intelligence; swarm intelligence; multiagent and unmanned systems
Special Issues, Collections and Topics in MDPI journals
Dr. Haijun Xia

E-Mail Website
Guest Editor
Cognitive Science Department, University of California San Diego (UCSD), San Diego, CA 92093, USA
Interests: human–computer interaction; human–robot interaction; computer-supported cooperative work; information visualization
Dr. Xiaobin Tan

E-Mail Website
Guest Editor
School of Information Science, University of Science and Technology of China, Anhui 230022, China
Interests: automatic control and coordination; adjustable autonomy; human–machine intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Benefiting from advances in artificial intelligence, especially machine learning represented by deep learning and reinforcement learning, machine intelligence has achieved unprecedented development. In some simple application scenarios, robots can even independently make autonomous decisions and carry out controls without the involvement of human beings. However, in the face of such open and complex scenarios as military operations, scientific exploration and disaster rescue, human intelligence is necessary. First, from the perspective of security, human decision-making is the baseline of ensuring autonomous robots always do the right thing. Second, to date, without the guidance and help of human knowledge and intelligence, robots are not yet able to make timely and good decisions based on their own intelligence and learning ability. Third, the service of human beings and helping them to improve and have a more comfortable life have thus far been the goals of the development of robots and their intelligence. Therefore, robot machine intelligence that integrates human intelligence will be the ultimate form of human–robot interaction research.

Based on the latest achievements in artificial intelligence, machine learning, automatic control, unmanned system design and human–computer interaction technology, this Special Issue will focus on providing an overview of the recent advances, covering a wide realm of advanced technologies in human–robot intelligence. This Special Issue especially welcomes original papers on the theoretical advancements and practical applications of human–robot intelligence. Review papers or tutorial papers on this topic are also encouraged.

The topics of interest include, but are not limited to:

  1. Human–robot intelligence theory and technology;
  2. Human–robot intelligence for command and control (C2);
  3. Human–robot intelligence for unmanned systems;
  4. Intelligent information processing and coordination between humans and robots;
  5. Human behavior and machine intelligence;
  6. Human-assisted intelligence for robots;
  7. Multi-agent-based human–robot intelligence;
  8. Human interaction with swarm robots/unmanned systems;
  9. Adjustable autonomy for human–robot interaction;
  10. Human–robot interaction with heterogenous sensors and actuators;
  11. Human–robot interaction for smart cities;
  12. Intelligent social robot interaction;
  13. Communications and networking technology of human–robot intelligence;
  14. Collaborative task and effectiveness evaluation techniques of human–robot intelligence;
  15. Modeling and simulation technology of human–robot intelligence;
  16. Human–robot intelligence in real life;
  17. Human–robot intelligence in military and scientific operations;
  18. Other related theories, methods, and techniques for human–robot intelligence.

Prof. Dr. Yang Xu
Dr. Haijun Xia
Dr. Xiaobin Tan
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 intelligence
  • human–robot interaction
  • adjustable autonomy
  • command and control
  • intelligent unmanned systems
  • human-assisted intelligence
  • intelligent information processing and coordination

Published Papers (5 papers)

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Research

15 pages, 877 KiB  
Article
An Effective Method for Constructing a Robot Operating System Node Knowledge Graph Based on Open-Source Robotics Repositories
by Yuxin Zhao, Xinjun Mao and Yi Yang
Electronics 2023, 12(19), 4022; https://doi.org/10.3390/electronics12194022 - 24 Sep 2023
Viewed by 1119
Abstract
Robot software development can be considered as a component-driven process, and existing ROS components, such as an ROS node, can be reused to construct robot applications. By reusing the ROS node, the development process of robot software can be significantly accelerated. However, the [...] Read more.
Robot software development can be considered as a component-driven process, and existing ROS components, such as an ROS node, can be reused to construct robot applications. By reusing the ROS node, the development process of robot software can be significantly accelerated. However, the challenges in reusing ROS nodes primarily lie in the scattered organization of ROS node information. To address this challenge, this paper proposes a method to construct an ROS node knowledge graph (RNKG) based on high-quality open-source robot projects. In order to build a high-quality knowledge graph of ROS nodes, we first constructed a high-quality dataset of open-source robot projects. Since ROS node knowledge can exist in both text and code formats, we initially separated the data in the dataset into code data and text data, and then applied different knowledge extraction methods to extract corresponding entities. Finally, we integrated a series of ROS node knowledge and organized it into a knowledge graph. To validate the effectiveness of the constructed ROS node knowledge graph, we first verified the completeness of the entities and the accuracy of relationships in the knowledge graph. Next, we evaluated the performance of the ROS node knowledge graph in assisting developers with the downstream task of finding ROS nodes. These findings suggest that our proposed method for constructing an ROS node knowledge graph is feasible and demonstrate that the ROS node knowledge graph helps search ROS nodes. Full article
(This article belongs to the Special Issue New Technologies and Applications of Human-Robot Intelligence)
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16 pages, 3119 KiB  
Article
A Software-Defined Distributed Architecture for Controlling Unmanned Swarm Systems
by Xuyang An, Xuewei Yu, Weilong Song, Le Han, Tingting Yang, Zhaodong Li and Zhibao Su
Electronics 2023, 12(18), 3739; https://doi.org/10.3390/electronics12183739 - 05 Sep 2023
Viewed by 784
Abstract
An unmanned swarm is usually composed of a group of homogeneous or heterogeneous hardware platforms, software control systems, and interfaces for human–computer interaction that operate collectively to achieve a specific goal by information interaction. They exhibit robustness and fault tolerance when facing complex [...] Read more.
An unmanned swarm is usually composed of a group of homogeneous or heterogeneous hardware platforms, software control systems, and interfaces for human–computer interaction that operate collectively to achieve a specific goal by information interaction. They exhibit robustness and fault tolerance when facing complex missions, making it crucial in military, transportation, intelligent traffic, and other fields. However, the coupling between the hardware and software of a heterogeneous unmanned swarm can indeed have significant implications for system flexibility, software development and deployment, and hardware maintenance. Over the years, there has been a significant shift from traditional hardware-focused control systems to a greater emphasis on the core software layer. In this paper, a distributed network architecture is proposed to solve this problem, in which hardware resources are abstracted and represented to accomplish standardization and unification by defining a consistent and uniform set of data formats, and a resource pool of hardware data is constructed to realize the function that the number and scale of platforms is irrelevant, the task module can be plug-and-play at any time, and the software can be configured on demand. The resource scheduling of a single platform is achieved through process and thread communication using shared memory, while the resource scheduling of a cross platform is achieved through a network using request and response and subscription and notification. As a result, it can satisfy the development of functional modules in a software-defined mode and gradually improve the intelligence capability of an unmanned swarm. Based on the above architecture, the overall framework of the autonomous navigation system and the collaborative control system has been successfully established. Finally, a hardware-in-the-loop simulation environment is constructed, and the integration and verification of the proposed distributed architecture is carried out by the cooperative formation experiment, which proves the feasibility of this proposal. Full article
(This article belongs to the Special Issue New Technologies and Applications of Human-Robot Intelligence)
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17 pages, 1107 KiB  
Article
Cooperative Decisions of a Multi-Agent System for the Target-Pursuit Problem in Manned–Unmanned Environment
by Le Han, Weilong Song, Tingting Yang, Zeyu Tian, Xuewei Yu and Xuyang An
Electronics 2023, 12(17), 3630; https://doi.org/10.3390/electronics12173630 - 28 Aug 2023
Cited by 1 | Viewed by 656
Abstract
With the development of intelligent technology, multi-agent systems have been widely applied in military and civilian fields. Compared to a single platform, multi-agent systems can complete more dangerous, difficult, and heavy tasks. However, due to the limited autonomy of unmanned platforms and the [...] Read more.
With the development of intelligent technology, multi-agent systems have been widely applied in military and civilian fields. Compared to a single platform, multi-agent systems can complete more dangerous, difficult, and heavy tasks. However, due to the limited autonomy of unmanned platforms and the regulatory needs of personnel, multi-agent systems cooperating with manned platforms to perform tasks have been more widely promoted at this stage of development. This paper addresses a differential game method for cooperative decision-making of a multi-agent system cooperating with the manned platform for the target-pursuit problem. The manned platform pursues the target according to a certain trajectory, and its state can be obtained by the multi-agent system. Firstly, for the case that the target moves with a fixed trajectory, the target-pursuit problem in a manned–unmanned environment is viewed in the form of game based on a communication graph among agents. Secondly, strategies of all agents are proposed while maintaining their group cohesion. A set of coupled differential equations is solved to implement strategy calculation. Compared to purely unmanned systems, the strategies combine the advantages of the manned platform and add a reference item, which can achieve team cohesion relatively quickly. Furthermore, a brief analysis is made on the scenarios where the target is in another case or adopts other strategies. Finally, comparative simulations have verified the effectiveness and synergy of the strategy. Full article
(This article belongs to the Special Issue New Technologies and Applications of Human-Robot Intelligence)
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18 pages, 3722 KiB  
Article
Multi-USV Dynamic Navigation and Target Capture: A Guided Multi-Agent Reinforcement Learning Approach
by Sulemana Nantogma, Shangyan Zhang, Xuewei Yu, Xuyang An and Yang Xu
Electronics 2023, 12(7), 1523; https://doi.org/10.3390/electronics12071523 - 23 Mar 2023
Viewed by 1483
Abstract
Autonomous unmanned systems have become an attractive vehicle for a myriad of military and civilian applications. This can be partly attributed to their ability to bring payloads for utility, sensing, and other uses for various applications autonomously. However, a key challenge in realizing [...] Read more.
Autonomous unmanned systems have become an attractive vehicle for a myriad of military and civilian applications. This can be partly attributed to their ability to bring payloads for utility, sensing, and other uses for various applications autonomously. However, a key challenge in realizing autonomous unmanned systems is the ability to perform complex group missions, which require coordination and collaboration among multiple platforms. This paper presents a cooperative navigating task approach that enables multiple unmanned surface vehicles (multi-USV) to autonomously capture a maneuvering target while avoiding both static and dynamic obstacles. The approach adopts a hybrid multi-agent deep reinforcement learning framework that leverages heuristic mechanisms to guide the group mission learning of the vehicles. Specifically, the proposed framework consists of two stages. In the first stage, navigation subgoal sets are generated based on expert knowledge, and a goal selection heuristic model based on the immune network model is used to select navigation targets during training. Next, the selected goals’ executions are learned using actor-critic proximal policy optimization. The simulation results with multi-USV target capture show that the proposed approach is capable of abstracting and guiding the unmanned vehicle group coordination learning and achieving a generally optimized mission execution. Full article
(This article belongs to the Special Issue New Technologies and Applications of Human-Robot Intelligence)
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17 pages, 1185 KiB  
Article
Adaptive Non-Singular Fast Terminal Sliding Mode Trajectory Tracking Control for Robot Manipulators
by Qiyao Yang, Xiangfeng Ma, Wei Wang and Dongliang Peng
Electronics 2022, 11(22), 3672; https://doi.org/10.3390/electronics11223672 - 10 Nov 2022
Cited by 4 | Viewed by 1310
Abstract
In order to improve the efficiency of human–robot interaction (HRI), it is necessary to carry out research on precise control of the manipulator. In this paper, an adaptive non-singular fast terminal sliding mode control scheme is proposed for robot manipulators to solve the [...] Read more.
In order to improve the efficiency of human–robot interaction (HRI), it is necessary to carry out research on precise control of the manipulator. In this paper, an adaptive non-singular fast terminal sliding mode control scheme is proposed for robot manipulators to solve the trajectory tracking problem with model uncertainty and external disturbances. At first, a novel non-singular fast terminal sliding mode surface is proposed, and by introducing an auxiliary function, the singularity problem caused by the inverse of the error-related matrix could be avoided in the controller design process. Then, the controller is developed by using Lyapunov synthesis. A robust adaptive strategy is used to deal with lumped uncertainty, with an adaptive update law designed to compensate for the upper bound of lumped uncertainty whose upper bound is prior unknown. Finally, a two-link robot manipulators as a simulation example is given to illustrate the effectiveness of the proposed scheme. Compared with other similar algorithms, the proposed adaptive non-singular fast terminal sliding mode control scheme has higher efficiency and smaller computational complexity for the reason that no piecewise continuous function is needed to be constructed during the controller design. Full article
(This article belongs to the Special Issue New Technologies and Applications of Human-Robot Intelligence)
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