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Electronics
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Advances in Kinematic Planning and Dynamic Control of Intelligent Robots
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Advances in Kinematic Planning and Dynamic Control of Intelligent Robots

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

Deadline for manuscript submissions: 15 December 2025 | Viewed by 825

Special Issue Editors

Dr. Ke Shao

E-Mail Website
Guest Editor
School of Civil Aviation, Northwestern Polytechnical University, Xi’an 710072, China
Interests: non-linear control; sliding mode control; intelligent robots; high-precision control; vehicle dynamics and control
Special Issues, Collections and Topics in MDPI journals
Dr. Bin Lan

E-Mail
Guest Editor
Jianghuai Advance Technology Center, Hefei 230000, China
Interests: state estimation and locomotion planning of humanoid robot

Special Issue Information

Dear Colleagues,

The field of robotics is undergoing a paradigm shift driven by advancements in computational algorithms, sensor technologies, and adaptive control systems. Kinematic planning and dynamic control lie at the heart of enabling robots to perform complex tasks in unstructured environments, from industrial automation and autonomous navigation to medical robotics and human–robot collaborations. These foundational disciplines ensure precise motion execution, stability in dynamic interactions, and adaptability to real-world uncertainties.

This Special Issue aims to consolidate cutting-edge research on the integration of kinematics and dynamics in order to address challenges such as real-time motion planning, multi-degree-of-freedom optimization, robustness in dynamic environments, and energy-efficient control. By bridging theoretical models with practical implementations, this Special Issue seeks to advance robotic systems toward higher autonomy, safety, and versatility.

The scope of this Special Issue aligns with the Electronics journal’s focus on innovative technologies and computational methodologies. We invite original research and review articles that explore novel approaches, algorithms, and applications. In this Special Issue, original research articles and reviews are welcome. Research areas include, but are not limited to, the following:

  • Kinematic path planning for multi-joint and mobile robots.
  • Dynamic modeling and control of robotic manipulators and legged systems.
  • Real-time trajectory optimization under environmental constraints.
  • Machine learning-driven motion planning for adaptive robotics.
  • Collision avoidance in dynamic or cluttered environments.
  • Human–robot interaction with emphasis on safety and compliance.
  • Sensor fusion for enhanced localization and control accuracy.
  • Energy-efficient actuation and torque optimization.
  • Multi-robot coordination and swarm dynamics.
  • Simulation frameworks for validating kinematic and dynamic models.

We look forward to receiving your contributions.

Dr. Ke Shao
Dr. Bin Lan
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

  • robotic kinematics
  • dynamic control
  • motion planning
  • trajectory optimization
  • autonomous robotics
  • sensor fusion
  • real-time systems
  • multi-robot coordination
  • adaptive algorithms
  • human–robot interaction

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Published Papers (2 papers)

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Research

22 pages, 12768 KB  
Article
Multi-Agent Coverage Path Planning Using Graph-Adapted K-Means in Road Network Digital Twin
by Haeseong Lee and Myungho Lee
Electronics 2025, 14(19), 3921; https://doi.org/10.3390/electronics14193921 - 1 Oct 2025
Viewed by 304
Abstract
In this paper, we research multi-robot coverage path planning (MCPP), which generates paths for agents to visit all target areas or points. This problem is common in various fields, such as agriculture, rescue, 3D scanning, and data collection. Algorithms to solve MCPP are [...] Read more.
In this paper, we research multi-robot coverage path planning (MCPP), which generates paths for agents to visit all target areas or points. This problem is common in various fields, such as agriculture, rescue, 3D scanning, and data collection. Algorithms to solve MCPP are generally categorized into online and offline methods. Online methods work in an unknown area, while offline methods generate a path for the known. Recently, offline MCPP has been researched through various approaches, such as graph clustering, DARP, genetic algorithms, and deep learning models. However, many previous algorithms can only be applied on grid-like environments. Therefore, this study introduces an offline MCPP algorithm that applies graph-adapted K-means and spanning tree coverage for robust operation in non-grid-structure maps such as road networks. To achieve this, we modify a cost function based on the travel distance by adjusting the referenced clustering algorithm. Moreover, we apply bipartite graph matching to reflect the initial positions of agents. We also introduce a cluster-level graph to alleviate local minima during clustering updates. We compare the proposed algorithm with existing methods in a grid environment to validate its stability, and evaluation on a road network digital twin validates its robustness across most environments. Full article
(This article belongs to the Special Issue Advances in Kinematic Planning and Dynamic Control of Intelligent Robots)
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23 pages, 4283 KB  
Article
Quaternion-Based Velocity Scheduling for Robotic Systems
by Tzu-Yuan Huang, Jun Loong Wong and Ming-Yang Cheng
Electronics 2025, 14(19), 3869; https://doi.org/10.3390/electronics14193869 - 29 Sep 2025
Viewed by 254
Abstract
Finding the time-optimal parameterization of a given path subject to kinodynamic constraints is a critical topic in many robotic applications. However, designing a real-time motion planning algorithm for specified trajectories subject to physical constraints is challenging due to the high nonlinearity in robotic [...] Read more.
Finding the time-optimal parameterization of a given path subject to kinodynamic constraints is a critical topic in many robotic applications. However, designing a real-time motion planning algorithm for specified trajectories subject to physical constraints is challenging due to the high nonlinearity in robotic systems. Additionally, moving along a given path may include three types of motion—pure translation, pure orientation, and composite motion—which will further complicate finding the best solution in these applications. To cope with this difficulty, this paper proposes a complete, real-time quaternion-based velocity scheduling algorithm (QBVSA) that takes physical constraints such as joint velocity, joint acceleration, and joint torque into account. The proposed QBVSA is designed to efficiently handle various types of motion subject to physical constraints in real-time. The completeness of the proposed QBVSA is proved mathematically. By exploiting the idea of the initial velocity limit, the search for switching points—which is essential to the conventional numerical integration method—is not required in the proposed approach. Simulations and experiments are performed to validate the proposed motion planning approach. Full article
(This article belongs to the Special Issue Advances in Kinematic Planning and Dynamic Control of Intelligent Robots)
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