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Machines
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Advances in AI-Powered Human–Machine-Augmented Intelligence
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Advances in AI-Powered Human–Machine-Augmented Intelligence

A Special Issue of Machines (ISSN 2075-1702) belonging to the section "Robotics, Mechatronics and Intelligent Machines".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 3853

Editors

Prof. Dr. Yunbo Zhao

E-Mail Website
Guest Editor
Department of Automation, University of Science and Technology of China, Hefei, China
Interests: human–machine intelligence; smart manufacturing
Dr. Pengfei Li

E-Mail Website
Guest Editor
Department of Automation, University of Science and Technology of China, Hefei, China
Interests: human–machine intelligence; model predictive control

Special Issue Information

Dear Colleagues,

In recent decades, the rapid advancement of artificial intelligence (AI) technologies has revolutionized the interaction between humans and AI-driven machines, particularly at the higher levels of intelligence such as autonomy, control, and decision making. This evolution has not only unlocked unprecedented opportunities for novel forms of intelligence but also introduced grand challenges that demand urgent exploration and innovation.

To address these transformative developments, this Special Issue aims to serve as an international forum for academics, engineers, and researchers to present their latest theoretical or applied works in the emerging field of AI-powered human–machine-augmented intelligence. We invite submissions of innovative research that explores, but is not limited to, the following themes:

  • Human and Machine Autonomy: Mechanisms and frameworks for autonomous decision making in collaborative systems.
  • Human–Machine Mutual Trust: Strategies to establish and maintain trust in human–AI interactions.
  • Traded- and Shared-Control Methodologies: Novel approaches to dynamic control allocation between humans and machines.
  • Applications in Real-World Systems: Practical implementations of human–machine intelligence across domains such as healthcare, infrastructure, or industrial automation.

The Special Issue emphasizes interdisciplinary integration, welcoming contributions from fields including cognitive sciences, automation and control systems, artificial intelligence, human–machine systems, and related disciplines. We particularly encourage submissions that offer novel insights into this emerging area or propose innovative methodologies and techniques to tackle its unique challenges.

Prof. Dr. Yunbo Zhao
Dr. Pengfei 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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly 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–machine intelligence
  • human trust
  • intention inference
  • autonomy
  • shared control
  • traded control

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

26 pages, 3203 KB  
Article
LiDAR-Aided Human–Machine Shared Control Optimization for Unknown Complex Environments via Model Predictive Control and Deep Reinforcement Learning
by Zhiao Cheng, Qianqian Zhang and Zerui Li
Machines 2026, 14(9), 1081; https://doi.org/10.3390/machines14091081 (registering DOI) - 19 Sep 2026
Abstract
Intelligent navigation of mobile robots in unknown environments has become a key enabling technology for service and logistics applications. However, in unstructured scenarios, perception noise and environmental uncertainty can significantly degrade system performance, making it a major challenge to balance safety and efficiency. [...] Read more.
Intelligent navigation of mobile robots in unknown environments has become a key enabling technology for service and logistics applications. However, in unstructured scenarios, perception noise and environmental uncertainty can significantly degrade system performance, making it a major challenge to balance safety and efficiency. In this work, we propose a Human–Machine Shared Control method with Model Predictive Control constraints (HMSC). HMSC establishes a confidence-driven human–machine shared control mechanism that maximizes collaborative efficiency by dynamically assessing the reliability of agent decisions to regulate control weights. Simultaneously, the method introduces a composite confidence evaluation model which, by fusing epistemic uncertainty with geometric feasibility from lightweight LiDAR measurements, achieves a robust quantification of policy risk. To ensure safe execution, we develop a multi-trajectory prediction mechanism which, after validating kinematic constraints, minimally intervenes to safely adjust control commands. We conducted Gazebo-based simulation experiments on obstacle avoidance and target navigation using a LiDAR-equipped mobile robot model and validated the rationality of the confidence model. The results demonstrate that the proposed shared control strategy, which combines confidence assessment with deterministic safety boundaries, significantly improves the success rate and robustness of the system in uncertain environments. Full article
(This article belongs to the Special Issue Advances in AI-Powered Human–Machine-Augmented Intelligence)
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25 pages, 15848 KB  
Article
A Human-in-the-Loop Framework for Outage Scheduling of Power Grids via LLM-RL Coordination
by Zhenhuan Ding, Jin Lv, Wei Tang, Kai Lv, Xun Mao and Qianqian Zhang
Machines 2026, 14(8), 860; https://doi.org/10.3390/machines14080860 - 30 Jul 2026
Viewed by 455
Abstract
As the operation modes and maintenance management requirements of power grids become increasingly complex, the optimization of monthly outage maintenance schedules faces challenges such as difficulty in multi-department coordination, high reliance on manual experience, and insufficient adaptability to dynamic demands. To address these [...] Read more.
As the operation modes and maintenance management requirements of power grids become increasingly complex, the optimization of monthly outage maintenance schedules faces challenges such as difficulty in multi-department coordination, high reliance on manual experience, and insufficient adaptability to dynamic demands. To address these issues, this paper proposes a collaborative optimization method for monthly outage schedules of power grids based on a large language model and reinforcement learning. The method uses the large language model to parse natural language requirements raised in balance meetings, converting requirements such as fixed maintenance dates, duration adjustments, and forbidden maintenance periods into structured constraint parameters. Subsequently, the small reinforcement learning model based on Dueling Deep Q-Network (Dueling DQN) re-optimizes the outage schedule according to the updated environment parameters, achieving a collaborative solution from meeting requirement parsing to schedule generation. Case study results on the IEEE 39-bus system show that the proposed method can integrate the dynamic requirements arising from multiple rounds of balance meetings and perform adaptive optimization of the outage schedule under different constraint conditions. Experimental results on the IEEE 39-bus system demonstrate that the proposed framework effectively optimizes monthly outage schedules. Compared with the initial schedule, the proposed method reduces monthly voltage violations by 31 times and decreases active power loss by 35.88 MWh. Full article
(This article belongs to the Special Issue Advances in AI-Powered Human–Machine-Augmented Intelligence)
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23 pages, 5275 KB  
Article
Enhancing Human–Machine Collaboration: A Trust-Aware Trajectory Planning Framework for Assistive Aerial Teleoperation
by Qianzheng Zhuang, Kangjie Huang, Xiaoran Jin, Pengfei Li, Yunbo Zhao and Yu Kang
Machines 2025, 13(9), 876; https://doi.org/10.3390/machines13090876 - 20 Sep 2025
Cited by 2 | Viewed by 2628
Abstract
Human–machine collaboration in assistive aerial teleoperation is frequently compromised by trust imbalances, which arise from the vehicle’s complex dynamics and the operator’s constrained perceptual feedback. We introduce a novel framework that enhances collaboration by dynamically integrating a model of human trust into the [...] Read more.
Human–machine collaboration in assistive aerial teleoperation is frequently compromised by trust imbalances, which arise from the vehicle’s complex dynamics and the operator’s constrained perceptual feedback. We introduce a novel framework that enhances collaboration by dynamically integrating a model of human trust into the unmanned aerial vehicle’s trajectory planning. We first propose a Machine-Performance-Dependent trust model, specifically tailored for aerial teleoperation, that quantifies trust based on real-time safety and visibility metrics. This model then informs a trust-aware trajectory planning algorithm, which generates smooth and adaptive trajectories that continuously align with the operator’s trust level and intent inferred from control inputs. Extensive simulations conducted in diverse forest environments validate our approach. The results demonstrate that our method achieves task efficiency comparable to that of a trust-unaware baseline while significantly reducing operator workload and improving trajectory smoothness, achieving reductions of up to 23.2% and 43.2%, respectively, in challenging dense environments. By embedding trust dynamics directly into the trajectory optimization loop, this work pioneers a more intuitive, efficient, and resilient paradigm for assistive aerial teleoperation. Full article
(This article belongs to the Special Issue Advances in AI-Powered Human–Machine-Augmented Intelligence)
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