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Applied Sciences
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Robotics and AI: Planning, Control, and Applications
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Robotics and AI: Planning, Control, and Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 523

Editors

Dr. Yi Fang

E-Mail Website
Guest Editor
School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: industrial robots; motion planning and control; multi-objective intelligent optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Yuxin Sun

E-Mail Website
Guest Editor
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: electromechanical system dynamics modeling and control; high-precision and high-speed CNC equipment and control; signal processing and deep learning algorithms; robotic intelligent manufacturing
Special Issues, Collections and Topics in MDPI journals
Dr. Dongfang Liu

E-Mail Website
Guest Editor
Department of Computer Engineering, Kate Gleason College of Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA
Interests: artificial intelligence and robotics; vision-language intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of robotics has been closely tied to advances in artificial intelligence. Techniques such as machine learning, deep reinforcement learning, and computer vision now allow robots to handle tasks that once required fully manual programming or were simply not feasible. At the same time, classical problems in robotics, including building accurate models, generating efficient motion plans, and designing controllers that work reliably when conditions change, remain far from solved and continue to attract significant research attention.

Recent years have seen growing interest in combining AI methods with established approaches in modeling, planning, and control. For example, learning-based methods have been used to compensate for unmodeled dynamics, data-driven planners have been developed for high-dimensional configuration spaces, and hybrid control architectures have been proposed to improve adaptability in real-world settings. These efforts span multiple disciplines, from mechanical engineering and control theory to computer science.

This Special Issue aims to bring together contributions that address the interplay between robotics and AI, with a focus on practical modeling, planning, and control problems. Topics of interest include, but are not limited to, the following:

  • Kinematic and dynamic modeling of robotic systems;
  • AI-based perception, learning, and decision-making;
  • Motion planning and trajectory optimization;
  • Advanced control strategies for robotic applications;
  • Sim-to-real transfer and digital twins;
  • Multi-robot coordination;
  • Human–robot interaction and collaboration;
  • Applications involving industrial, medical, mobile, aerial, and service robots.

We welcome both theoretical and experimental work.

Dr. Yi Fang
Dr. Yuxin Sun
Dr. Dongfang Liu
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. Applied Sciences 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

  • robotics
  • artificial intelligence
  • kinematic and dynamic modeling
  • motion planning
  • trajectory optimization
  • robot control
  • machine learning
  • deep reinforcement learning
  • sim-to-real transfer
  • multi-robot coordination
  • human–robot interaction

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Published Papers (1 paper)

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Research

31 pages, 11223 KB  
Article
An Improved A*-Based Path-Planning Framework for Facility Agricultural Robots
by Ziqiang Yang, Chunyan Zhang, Tao Yu and Zhen Xu
Appl. Sci. 2026, 16(12), 6138; https://doi.org/10.3390/app16126138 - 17 Jun 2026
Viewed by 285
Abstract
Facility agricultural robots operating in greenhouse environments often encounter narrow passages, dense obstacle distributions, and frequent path-direction changes, which increase the difficulty of achieving efficient and smooth autonomous navigation. Conventional A* algorithms usually suffer from redundant node expansion, dense turning-point distributions, and insufficient [...] Read more.
Facility agricultural robots operating in greenhouse environments often encounter narrow passages, dense obstacle distributions, and frequent path-direction changes, which increase the difficulty of achieving efficient and smooth autonomous navigation. Conventional A* algorithms usually suffer from redundant node expansion, dense turning-point distributions, and insufficient path continuity under such constrained conditions. To address these issues, this study proposes an improved A*-based path-planning framework that integrates adaptive heuristic weighting, dynamic corner correction, and Bézier-curve-based path smoothing. Rather than introducing an entirely new planning paradigm, the proposed method coordinates several existing optimization strategies within a unified framework to improve search efficiency, path regularity, and path continuity for facility agricultural scenarios. The adaptive heuristic weighting strategy dynamically adjusts the contribution of the heuristic term according to the relative distance between the current node and the target node, thereby improving global search guidance while reducing unnecessary exploration. Dynamic corner correction is introduced to suppress zigzag path structures and reduce redundant turning nodes in obstacle-dense regions, while Bézier-curve-based smoothing is employed to improve path continuity and compatibility with the kinematic characteristics of agricultural mobile robots. Simulation experiments were conducted on grid maps and greenhouse-like environments with different obstacle distributions, and comparative evaluations were performed against Dijkstra, RRT, and conventional A* algorithms. Under representative simulation scenarios, the proposed framework reduced the number of turning points by up to 53.7% and decreased computation time by approximately 19.4% compared with the conventional A* algorithm, based on the average results of repeated trials under identical conditions. In addition, physical platform experiments on a ROS2-based agricultural robot demonstrated that the planned trajectories maintained relatively stable navigation performance and smoother directional transitions in constrained greenhouse-like environments. The results indicate that the proposed framework achieves a more balanced trade-off between computational efficiency, path compactness, and path smoothness than the benchmark methods considered in this study. Nevertheless, the current validation remains limited to structured or semi-structured greenhouse environments under static obstacle conditions. Future work will focus on improving adaptability to dynamic agricultural scenarios and integrating the framework with real-time perception and motion-control systems for practical greenhouse deployment. Full article
(This article belongs to the Special Issue Robotics and AI: Planning, Control, and Applications)
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