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Machines
Special Issues
Control Engineering and Artificial Intelligence
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Control Engineering and Artificial Intelligence

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Automation and Control Systems".

Deadline for manuscript submissions: 15 February 2026 | Viewed by 396

Special Issue Editors

Prof. Dr. Dan Zhang

E-Mail Website
Guest Editor
Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Interests: synthesis and optimization of manipulator mechanisms; generalized parallel mechanisms research; reconfigurable robots
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zhengtao Ding

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, The University of Manchester, Manchester M13 9PL, UK
Interests: nonlinear and adaptive control theory and their applications, network-based control, distributed optimization and distributed learning, with applications to power systems and robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

CCEAI 2026 conference (https://www.cceai.org/) is the premier interdisciplinary forum for the presentation of new advances and research results in the fields of Control Engineering and Artificial Intelligence. The conference will bring together leading academic scientists, researchers and scholars in the domain of interest from around the world.

Prof. Dr. Dan Zhang
Prof. Dr. Zhengtao Ding
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-blind 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

  • control engineering
  • artificial intelligence
  • intelligent automation
  • automated guided vehicles
  • factory modeling and automation
  • flexible manufacturing systems
  • robotics
  • mechatronics

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Published Papers (1 paper)

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Research

18 pages, 14158 KB  
Article
Vision-Based Perception and Execution Decision-Making for Fruit Picking Robots Using Generative AI Models
by Yunhe Zhou, Chunjiang Yu, Jiaming Zhang, Yuanhang Liu, Jiangming Kan, Xiangjun Zou, Kang Zhang, Hanyan Liang, Sheng Zhang and Fengyun Wu
Machines 2026, 14(1), 117; https://doi.org/10.3390/machines14010117 - 19 Jan 2026
Viewed by 168
Abstract
At present, fruit picking mainly relies on manual operation. Taking the litchi (litchi chinensis Sonn.)-picking robot as an example, visual perception is often affected by illumination variations, low recognition accuracy, complex maturity judgment, and occlusion, which lead to inaccurate fruit localization. This study [...] Read more.
At present, fruit picking mainly relies on manual operation. Taking the litchi (litchi chinensis Sonn.)-picking robot as an example, visual perception is often affected by illumination variations, low recognition accuracy, complex maturity judgment, and occlusion, which lead to inaccurate fruit localization. This study aims to establish an embodied perception mechanism based on “perception-reasoning-execution” to enhance the visual perception and decision-making capability of the robot in complex orchard environments. First, a Y-LitchiC instance segmentation method is proposed to achieve high-precision segmentation of litchi clusters. Second, a generative artificial intelligence model is introduced to intelligently assess fruit maturity and occlusion, providing auxiliary support for automatic picking. Based on the auxiliary judgments provided by the generative AI model, two types of dynamic harvesting decisions are formulated for subsequent operations. For unoccluded main fruit-bearing branches, a skeleton thinning algorithm is applied within the segmented region to extract the skeleton line, and the midpoint of the skeleton is used to perform the first type of localization and harvesting decision. In contrast, for main fruit-bearing branches occluded by leaves, threshold-based segmentation combined with maximum connected component extraction is employed to obtain the target region, followed by skeleton thinning, thereby completing the second type of dynamic picking decision. Experimental results show that the Y-LitchiC model improves the mean average precision (mAP) by 1.6% compared with the YOLOv11s-seg model, achieving higher accuracy in litchi cluster segmentation and recognition. The generative artificial intelligence model provides higher-level reasoning and decision-making capabilities for automatic picking. Overall, the proposed embodied perception mechanism and dynamic picking strategies effectively enhance the autonomous perception and decision-making of the picking robot in complex orchard environments, providing a reliable theoretical basis and technical support for accurate fruit localization and precision picking. Full article
(This article belongs to the Special Issue Control Engineering and Artificial Intelligence)
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