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Electronics
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Advanced Control and Motion Planning Algorithms for Smart Robotic Systems
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Advanced Control and Motion Planning Algorithms for Smart Robotic Systems

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

Deadline for manuscript submissions: 16 June 2025 | Viewed by 582

Special Issue Editors

Dr. Ho Tho

E-Mail Website
Guest Editor
Department of System Safety, Nagaoka University of Technology, Nagaoka 940-2137, Japan
Interests: telerobotics; vibration control for flexible robots
Prof. Dr. Takanori Miyoshi

E-Mail Website
Guest Editor
Department of System Safety, Nagaoka University of Technology, Nagaoka 940-2137, Japan
Interests: telerobotics; haptic systems; power assist systems

Special Issue Information

Dear Colleagues,

Smart robotic systems necessitate smart controllers and motion planners. This Special Issue on "Advanced Control and Motion Planning Algorithms for Smart Robotic Systems" is a venue for academic and industrial researchers to showcase cutting-edge research on intelligent control strategies and motion planning techniques that enhance the autonomy, precision, and adaptability of robotic systems. We invite researchers to submit high-quality contributions that address the challenges in modern robotics, focusing on innovative algorithms, real-time control, and optimization techniques. Contributions can span theoretical analyses, simulations, and experimental validations of innovative smart robotic systems. The practicability of controllers/algorithms should be demonstrated on experimental apparatuses, or, at least, real-world conditions must be used in simulations. In addition, comparisons with state-of-the-art studies are strongly expected.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Advanced control designs for robotic systems.
  • Motion planning and trajectory optimization.
  • Multi-robot coordination and swarm robotics.
  • Leaning-based control strategies and adaptive algorithms.
  • Applications of artificial intelligence (AI) in robotics.
  • Human–robot interaction and collaboration.
  • Telerobotics and teleoperation.
  • Innovative robotic systems in industrial automation, aerospace, medical, and service fields.

We look forward to receiving your contributions.

Dr. Ho Tho
Prof. Dr. Takanori Miyoshi
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

  • smart robotic systems
  • advanced control algorithms
  • motion planning
  • trajectory optimization
  • adaptive algorithms
  • multi-robot coordination
  • machine learning in robotics
  • human–robot interaction
  • teleoperation
  • telerobotics

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Published Papers (1 paper)

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Research

27 pages, 1907 KiB  
Article
Neural-Driven Constructive Heuristic for 2D Robotic Bin Packing Problem
by Mariusz Kaleta and Tomasz Śliwiński
Electronics 2025, 14(10), 1956; https://doi.org/10.3390/electronics14101956 - 11 May 2025
Viewed by 365
Abstract
This study addresses the two-dimensional weakly homogeneous Bin Packing Problem (2D-BPP) in the context of robotic packing, where items must be arranged in a manner feasible for robotic manipulation. Traditional heuristics for this NP-hard problem often lack adaptability across diverse datasets, while metaheuristics [...] Read more.
This study addresses the two-dimensional weakly homogeneous Bin Packing Problem (2D-BPP) in the context of robotic packing, where items must be arranged in a manner feasible for robotic manipulation. Traditional heuristics for this NP-hard problem often lack adaptability across diverse datasets, while metaheuristics typically suffer from slow convergence. To overcome these limitations, we propose a novel neural-driven constructive heuristic. The method employs a population of simple feed-forward neural networks, which are trained using black-box optimization via the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). The resulting neural network dynamically scores candidate placements within the constructive heuristic. Unlike conventional heuristics, the approach adapts to instance-specific characteristics without relying on predefined rules. Evaluated on datasets generated by 2DCPackGen and real-world logistic scenarios, the proposed method consistently outperforms benchmark heuristics such as MaxRects and Skyline, reducing the average number of bins required across various item types and demand ranges. The most significant improvements occur in complex instances, with up to 86% of 2DCPackGen cases yielding superior results. This heuristic offers a flexible and extremely fast, data-driven solution to the algorithm selection problem, demonstrating robustness and potential for broader application in combinatorial optimization while avoiding the scalability issues of reinforcement learning-based methods. Full article
(This article belongs to the Special Issue Advanced Control and Motion Planning Algorithms for Smart Robotic Systems)
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