Advanced Control and Optimization for Robotic Systems

A special issue of Robotics (ISSN 2218-6581). This special issue belongs to the section "Sensors and Control in Robotics".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 2461

Special Issue Editors


E-Mail Website
Guest Editor
School of Engineering, Lancaster University, Lancaster, UK
Interests: robotics and autonomous systems; cyber-physical systems; robotics for environmental monitoring; robotics for extreme environments; unmanned aerial vehicles; cooperative navigation and control; multi-agent systems; active noise and vibration control systems; system identification; adaptive control and signal processing
Special Issues, Collections and Topics in MDPI journals

E-Mail
Co-Guest Editor
Electrical Engineering Department, Islamic Azad University, Najafabad Branch, Isfahan, Iran
Interests: control systems; robotic and autonomous systems; robot navigation; nonlinear and adaptive control

Special Issue Information

Dear Colleagues,

Robotic systems are rapidly advancing in terms of complexity, autonomy, and versatility, requiring innovative control and optimization strategies to achieve high performance in real-world unstructured environments and safety critical applications. This Special Issue seeks original research and review articles that address the latest theoretical developments, computational methods, and practical implementations in advanced control and optimization for robotics.

We invite contributions that explore novel frameworks, algorithms, and applications that enhance the intelligence, adaptability, and robustness of robotic systems across diverse domains, such as industrial automation, service robotics, autonomous vehicles, underwater/aerial robots, and human–robot interaction.

Topics of interest include (but are not limited to) the following:

  • Nonlinear, adaptive, and robust control methods for robotics;
  • Learning-based and reinforcement learning control techniques;
  • Optimization-based control (MPC, convex/non-convex optimization, distributed optimization);
  • Cooperative control and coordination of multi-robot systems;
  • Control and estimation under uncertainty, disturbances, and communication constraints;
  • Observer-based design for robotics applications;
  • Visual servoing and sensor-based control strategies;
  • Intelligent control for human–robot collaboration and assistive robotics;
  • Energy-efficient and sustainable control strategies for robotic systems;
  • Safe and reliable control under physical and safety constraints;
  • Optimization in motion planning, trajectory generation, and navigation;
  • Hybrid approaches combining model-based and data-driven control;
  • Applications of fuzzy, neural, and intelligent systems in robotic optimization;
  • Benchmarking, simulation platforms, and experimental validation of advanced control algorithms.

This Special Issue will serve as a platform for bridging theory and practice, highlighting both cutting-edge methodologies and real-world case studies that demonstrate the transformative potential of advanced control and optimization in robotics.

Dr. Allahyar Montazeri
Guest Editor

Dr. Khoshnam Shojaei
Co-Guest Editor

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. Robotics 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 1800 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

  • nonlinear, adaptive, and robust control for robotics
  • learning-based and reinforcement learning control
  • optimization-based control
  • MPC
  • convex/non-convex optimization
  • distributed optimization
  • cooperative control
  • multi-robot systems
  • sensor-based control
  • sustainable control
  • safe and reliable control
  • motion planning
  • trajectory generation
  • data-driven control
  • model-based control

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

22 pages, 14360 KB  
Article
Kinematic Characterization of a Novel 4-DoF Parallel Mechanism with Modular Actuation
by Zoltán Forgó and Ferenc Tolvaly-Roșca
Robotics 2026, 15(1), 13; https://doi.org/10.3390/robotics15010013 - 1 Jan 2026
Viewed by 384
Abstract
The accelerating industrial demand for high-speed manipulation has necessitated the development of robotic architectures that effectively balance dynamic performance with workspace size. While serial SCARA robots offer large workspaces and parallel Delta robots provide high acceleration, existing architectures fail to combine these benefits [...] Read more.
The accelerating industrial demand for high-speed manipulation has necessitated the development of robotic architectures that effectively balance dynamic performance with workspace size. While serial SCARA robots offer large workspaces and parallel Delta robots provide high acceleration, existing architectures fail to combine these benefits effectively for specific four-degree-of-freedom (4-DoF) Schoenflies motion tasks. This study introduces and characterizes a novel 4-DoF parallel topology, having a symmetrical build-up, which is distinguished by its use of modular 2-DoF linear drive units. The research methodology entails the structural synthesis of the kinematic chain followed by kinematic analysis using vector algebra to derive closed-form inverse geometric models. Additionally, the Jacobian matrix is formulated to evaluate velocity transmission and systematically classify singular configurations, while the dexterity index is defined to assess the rotational capabilities of the mechanism. Numerical simulations of pick-and-place trajectory were also conducted, varying trajectory curvature to analyze kinematic behavior. The results demonstrate that the proposed modular architecture yields a highly symmetric and homogeneous workspace that can be scaled simply by adjusting the drive module lengths. Furthermore, the singularity and dexterity analyses reveal a substantial, singularity-free operational workspace, although tighter trajectory curvatures were found to impose higher velocity demands on the joints. In conclusion, the proposed mechanism successfully achieves the targeted Schoenflies motion, offering a solution for automated industrial tasks. Full article
(This article belongs to the Special Issue Advanced Control and Optimization for Robotic Systems)
Show Figures

Figure 1

29 pages, 166576 KB  
Article
A Decentralized Potential Field-Based Self-Organizing Control Framework for Trajectory, Formation, and Obstacle Avoidance of Fully Autonomous Swarm Robots
by Mohammed Abdel-Nasser, Sami El-Ferik, Ramy Rashad and Abdul-Wahid A. Saif
Robotics 2025, 14(12), 192; https://doi.org/10.3390/robotics14120192 - 18 Dec 2025
Viewed by 1425
Abstract
In this work, we propose a fully decentralized, self-organizing control framework for a swarm of autonomous ground mobile robots. The system integrates potential field-based mechanisms for simultaneous trajectory tracking, formation control, and obstacle avoidance, all based on local sensing and neighbor interactions without [...] Read more.
In this work, we propose a fully decentralized, self-organizing control framework for a swarm of autonomous ground mobile robots. The system integrates potential field-based mechanisms for simultaneous trajectory tracking, formation control, and obstacle avoidance, all based on local sensing and neighbor interactions without centralized coordination. Each robot autonomously computes attractive, repulsive, and formation forces to navigate toward target positions while maintaining inter-robot spacing and avoiding both static and dynamic obstacles. Inspired by biological swarm behavior, the controller emphasizes robustness, scalability, and flexibility. The proposed method has been successfully validated in the ARGoS simulator, which provides realistic physics, sensor modeling, and a robust environment that closely approximates real-world conditions. The system was tested with up to 15 robots and is designed to scale to larger swarms (e.g., 100 robots), demonstrating stable performance across a range of scenarios. Results obtained using ARGoS confirm the swarm’s ability to maintain formation, avoid collisions, and reach a predefined goal area within a configurable 1 m radius. This zone serves as a spatial convergence region suitable for multi-robot formation, even in the presence of unknown fixed obstacles and movable agents. The framework can seamlessly handle the addition or removal of swarm members without reconfiguration. Full article
(This article belongs to the Special Issue Advanced Control and Optimization for Robotic Systems)
Show Figures

Figure 1

Back to TopTop