Mathematics Methods of Robotics and Intelligent Systems

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E2: Control Theory and Mechanics".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 1912

Special Issue Editors


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Guest Editor
Department of Mechanical Engineering, National Pingtung University of Science and Technology, Pingtung 912301, Taiwan
Interests: AI applications; robot design; nonlinear optimal and robust control
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Systems and Naval Mechatronics Engineering, National Cheng Kung University, Tainan 70101, Taiwan
Interests: nonlinear optimal and robust control of nonlinear systems; intelligent robot design; nonlinear estimation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue on "Mathematics Methods of Robotics and Intelligent Systems" aims to bring together mathematical methods and advancements for applications of robotic and intelligent systems. The field of robotics and intelligent systems has witnessed remarkable advancements in recent years and has been propelled by the integration of sophisticated mathematical methods. This Special Issue aims to explore and highlight the pivotal role that mathematical approaches play in shaping the landscape of robotics and intelligent systems.

This Special Issue seeks to provide a comprehensive platform for researchers, academics, and practitioners to disseminate their latest findings and insights into the diverse applications of mathematical methods in the realm of robotics and intelligent systems. Contributions are invited across a spectrum of topics, including but not limited to mathematical modeling in control systems, path planning, motion control, machine learning, optimization methods, nonlinear control theory, advanced intelligent algorithms for robotic systems, deep learning, bio-inspired robotics, and cooperative control for multi-robot systems.

Topics of interest:

  • Mathematical modeling of robotic systems;
  • Optimization algorithms for path planning of robots;
  • Nonlinear control applications of intelligent systems;
  • Machine learning algorithms for robotic perception;
  • Computational geometry in robotics;
  • Mathematical approaches to sensor fusion in intelligent systems.

Dr. Yung-Hsiang Chen
Dr. Yung-Yue Chen
Guest Editors

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Keywords

  • nonlinear and hybrid robotic systems
  • distributed and multi-agent systems
  • multi-sensor fusion techniques and filtering
  • motion planning
  • optimal path design
  • swarm robotics
  • control theory
  • gaussian control
  • matrix control
  • H-infinity control
  • sliding mode control
  • multivariable control
  • neural control
  • nonlinear control
  • stochastic control
  • model predictive control
  • robust control
  • optimal control
  • adaptive control
  • time-varying control
  • fuzzy control
  • fractional-order control

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Published Papers (2 papers)

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23 pages, 6242 KiB  
Article
Numerical Approach for Trajectory Smoothing for LegUp Rehabilitation Parallel Robot
by Iosif Birlescu, Vlad Mihaly, Calin Vaida, Andrei Caprariu, Paul Tucan, Jose Machado and Doina Pisla
Mathematics 2025, 13(8), 1241; https://doi.org/10.3390/math13081241 - 9 Apr 2025
Viewed by 184
Abstract
Robotic-assisted motor rehabilitation has seen significant development over the past decade, driven by the distinct advantages that robots offer in this medical task. A key aspect of robotic-assisted rehabilitation is ensuring that the performed rehabilitation exercises are safely planned (i.e., without the risk [...] Read more.
Robotic-assisted motor rehabilitation has seen significant development over the past decade, driven by the distinct advantages that robots offer in this medical task. A key aspect of robotic-assisted rehabilitation is ensuring that the performed rehabilitation exercises are safely planned (i.e., without the risk of patient injury or anatomic joint strain). This paper presents a new numerical approach to trajectory planning for a LegUp parallel robot designed for lower limb rehabilitation. The proposed approach generates S-shaped motion profiles, also called S-curves, with precise control over all kinematic parameters, resulting in smooth acceleration and deceleration. This approach ensures the safety and effectiveness of rehabilitation exercises by minimizing strain on the patient’s anatomical joints. The mathematical models employed (numerical integration and differentiation) are well-established and computationally efficient for real-time implementation in the robot’s control hardware. Experimental tests using LegUp validate the effectiveness of the proposed trajectory-smoothing approach. Full article
(This article belongs to the Special Issue Mathematics Methods of Robotics and Intelligent Systems)
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25 pages, 19697 KiB  
Article
Control Design and Implementation of Autonomous Robotic Lawnmower
by Yung-Hsiang Chen
Mathematics 2024, 12(21), 3324; https://doi.org/10.3390/math12213324 - 23 Oct 2024
Cited by 2 | Viewed by 1129
Abstract
This paper presents the trajectory tracking control design and implementation of feedback linearization (FL) and robust feedback linearization (RFL), applicable to a robotic lawnmower with four mecanum driving wheels. The RFL control design additionally includes a robust control law. These two nonlinear control [...] Read more.
This paper presents the trajectory tracking control design and implementation of feedback linearization (FL) and robust feedback linearization (RFL), applicable to a robotic lawnmower with four mecanum driving wheels. The RFL control design additionally includes a robust control law. These two nonlinear control laws are developed to enable the controlled robotic lawnmower to accurately follow any specified trajectory. The simulation outcomes illustrate that the suggested control law based on RFL displays superior trajectory tracking accuracy and resilience compared to the FL control method in the case of a robotic lawnmower operating under demanding conditions. These conditions encompass environmental disturbances and uncertainties in modeling. The RFL control method also exhibits lower energy consumption compared to the FL control method. Finally, using the RFL controller derived from this study, the error in trajectory tracking in computer simulations and the actual mowing performance have demonstrated outstanding results. Full article
(This article belongs to the Special Issue Mathematics Methods of Robotics and Intelligent Systems)
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