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Modern Trends in Computation and Control in Autonomous Robotics Systems
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Modern Trends in Computation and Control in Autonomous Robotics 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 October 2025 | Viewed by 1186

Special Issue Editor

Dr. Yujiong Liu

E-Mail Website
Guest Editor
Robotics and Mechatronics Lab, Virginia Tech College of Engineering, Blacksburg, VA 24061, USA
Interests: robot control

Special Issue Information

Dear Colleagues,

As our society rapidly evolves into the robot era, autonomous robot technologies become one of the key technologies to enable robots to seamlessly integrate into human society. This Special Issue, “Modern Trends in Computation and Control in Autonomous Robotics Systems”, aims to address the fundamental computation and control challenges faced by the rapidly evolving autonomous robotics systems and expects to have profound impacts on both the applications and the research of such systems.

The primary goal of this Special Issue is to explore and disseminate novel ideas and research in the diverse aspects of autonomous robotics systems, with a focus on the computational and control aspects of such systems. The computational aspect mainly covers the topics related to the algorithms that enable the autonomous robotic system to navigate, perceive, make decisions, and interact with its environment, while the control aspect mainly covers the theories behind these algorithms. Authors are cordially invited to submit original and unpublished research articles. All submissions will be peer-reviewed based on their originality, technical quality, and relevance to the Special Issue. The topics of interest for this Special Issue include, but are not limited to:

  • Classic and emerging control theories and practices for autonomous robotic systems;
  • Computational methods for sensor data fusion and interpretation;
  • Machine learning techniques for object detection and recognition;
  • Simultaneous localization and mapping (SLAM) algorithms and frameworks;
  • Path planning algorithms and optimization techniques;
  • Adaptive and learning-based navigation strategies;
  • AI-driven decision-making and planning in robotics;
  • Computational approaches for ensuring safety in autonomous systems;
  • Verification and validation of autonomous robotic algorithms.

Dr. Yujiong Liu
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 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. Mathematics 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 2600 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

  • dynamics and control
  • optimization
  • sensing and perception
  • localization and mapping
  • path planning
  • navigation
  • decision-making
  • intelligent control
  • machine learning and AI

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Published Papers (2 papers)

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21 pages, 8166 KiB  
Article
Reinforcement Learning-Based Control for Robotic Flexible Element Disassembly
by Benjamín Tapia Sal Paz, Gorka Sorrosal, Aitziber Mancisidor, Carlos Calleja and Itziar Cabanes
Mathematics 2025, 13(7), 1120; https://doi.org/10.3390/math13071120 - 28 Mar 2025
Viewed by 313
Abstract
Disassembly plays a vital role in sustainable manufacturing and recycling processes, facilitating the recovery and reuse of valuable components. However, automating disassembly, especially for flexible elements such as cables and rubber seals, poses significant challenges due to their nonlinear behavior and dynamic properties. [...] Read more.
Disassembly plays a vital role in sustainable manufacturing and recycling processes, facilitating the recovery and reuse of valuable components. However, automating disassembly, especially for flexible elements such as cables and rubber seals, poses significant challenges due to their nonlinear behavior and dynamic properties. Traditional control systems struggle to handle these tasks efficiently, requiring adaptable solutions that can operate in unstructured environments that provide online adaptation. This paper presents a reinforcement learning (RL)-based control strategy for the robotic disassembly of flexible elements. The proposed method focuses on low-level control, in which the precise manipulation of the robot is essential to minimize force and avoid damage during extraction. An adaptive reward function is tailored to account for varying material properties, ensuring robust performance across different operational scenarios. The RL-based approach is evaluated in a simulation using soft actor–critic (SAC), deep deterministic policy gradient (DDPG), and proximal policy optimization (PPO) algorithms, benchmarking their effectiveness in dynamic environments. The experimental results indicate the satisfactory performance of the robot under operational conditions, achieving an adequate success rate and force minimization. Notably, there is at least a 20% reduction in force compared to traditional planning methods. The adaptive reward function further enhances the ability of the robotic system to generalize across a range of flexible element disassembly tasks, making it a promising solution for real-world applications. Full article
(This article belongs to the Special Issue Modern Trends in Computation and Control in Autonomous Robotics Systems)
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20 pages, 1989 KiB  
Article
Hybrid A*-Guided Model Predictive Path Integral Control for Robust Navigation in Rough Terrains
by Joonyeol Yang , Minhyeong Kang , Seulchan Lee and Sanghyun Kim
Mathematics 2025, 13(5), 810; https://doi.org/10.3390/math13050810 - 28 Feb 2025
Viewed by 723
Abstract
Navigating rough terrains requires a robust path planning algorithm that accounts for the physical properties of the environment to maintain stability and ensure safety. This article proposes the Hybrid A*-guided Model Predictive Path Integral (MPPI) algorithm augmented with traversability estimation to address the [...] Read more.
Navigating rough terrains requires a robust path planning algorithm that accounts for the physical properties of the environment to maintain stability and ensure safety. This article proposes the Hybrid A*-guided Model Predictive Path Integral (MPPI) algorithm augmented with traversability estimation to address the challenges of path planning on uneven terrains. The traversability estimation process quantifies surface characteristics, such as slope and roughness to identify traversable regions. Using this information, the Hybrid A* algorithm computes paths that minimize surface irregularities and prioritize regions with lower gradients, thereby enhancing stability and reducing dynamic disturbances. These computed paths are then used to define the mean control input for the MPPI algorithm, which performs localized optimization while adhering to the terrain-aware trajectory. By integrating terrain-aware guidance through the Hybrid A* algorithm with the MPPI, the proposed methodology automates the selection of the appropriate mean control input and enhances control performance by explicitly incorporating terrain properties into the planning process. Experimental results demonstrate the ability of the algorithm to navigate complex terrains with reduced roll and pitch motions, contributing to improved stability and performance. Full article
(This article belongs to the Special Issue Modern Trends in Computation and Control in Autonomous Robotics Systems)
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