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Applied Sciences
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Recent Advances in Autonomous Systems and Robotics, 2nd Edition
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Recent Advances in Autonomous Systems and Robotics, 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: closed (30 October 2024) | Viewed by 2305

Special Issue Editor

Prof. Dr. Xinhua Liu

E-Mail Website
Guest Editor
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China
Interests: intelligence manufacturing and control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

To cope with non-programmed or non-preset situations, autonomous technology can use multi-source sensors and complex software to make systems with limited or no communication endure for long periods, and these systems can automatically adjust to an unknown environment, independently complete tasks, and maintain good performance. Autonomous systems and robotics are interdisciplinary fields involving real-time detection, information processing, comprehensive analysis, intelligent judgment, robust control, etc. With the continuous improvement of technical complexity, the possibility of system failure, vulnerability, and overall loss of function will also increase. Most applications still require the combination of human and autonomous systems to complete different tasks, so intelligent and unmanned systems are still challenging in current research.

The aim of this Special Issue is to celebrate the recent advances in autonomous systems and robotics and to promote the exchange and development of modern technologies, methods, and theories. We welcome authors to submit original research papers, perspectives, reviews, and mini-reviews. Areas to be covered in this Special Issue may include, but are not limited to, machine vision; machine learning and deep learning; artificial intelligence technology; fault detection and diagnosis; and intelligent robots.

Prof. Dr. Xinhua 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. Applied Sciences 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

  • machine learning
  • artificial intelligence
  • intelligent manufacturing
  • theoretical model
  • data processing
  • performance optimization
  • experimental analysis
  • robotics

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Published Papers (3 papers)

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Research

17 pages, 8636 KiB  
Article
Image-Based Tactile Deformation Simulation and Pose Estimation for Robot Skill Learning
by Chenfeng Fu, Longnan Li, Yuan Gao, Weiwei Wan, Kensuke Harada, Zhenyu Lu and Chenguang Yang
Appl. Sci. 2025, 15(3), 1099; https://doi.org/10.3390/app15031099 - 22 Jan 2025
Abstract
The TacTip is a cost-effective, 3D-printed optical tactile sensor commonly used in deep learning and reinforcement learning for robotic manipulation. However, its specialized structure, which combines soft materials of varying hardnesses, makes it challenging to simulate the distribution of numerous printed markers on [...] Read more.
The TacTip is a cost-effective, 3D-printed optical tactile sensor commonly used in deep learning and reinforcement learning for robotic manipulation. However, its specialized structure, which combines soft materials of varying hardnesses, makes it challenging to simulate the distribution of numerous printed markers on pins. This paper aims to create an interpretable, AI-applicable simulation of the deformation of TacTip under varying pressures and interactions with different objects, addressing the black-box nature of learning and simulation in haptic manipulation. The research focuses on simulating the TacTip sensor’s shape using a fully tunable, chain-based mathematical model, refined through comparisons with real-world measurements. We integrated the WRS system with our theoretical model to evaluate its effectiveness in object pose estimation. The results demonstrated that the prediction accuracy for all markers across a variety of contact scenarios exceeded 92%. Full article
(This article belongs to the Special Issue Recent Advances in Autonomous Systems and Robotics, 2nd Edition)
20 pages, 1618 KiB  
Article
Learning-Based Model Predictive Control for Legged Robots with Battery–Supercapacitor Hybrid Energy Storage System
by Boyu Shu, Zhiwu Huang, Wanwan Ren, Yue Wu and Heng Li
Appl. Sci. 2025, 15(1), 382; https://doi.org/10.3390/app15010382 - 3 Jan 2025
Viewed by 410
Abstract
Electrically driven legged robots have become popular in recent years. However, the development of reliable energy supply systems and effective energy management strategies for legged robots with dramatically varying power requirements still needs to be explored. This article proposes a learning-based model predictive [...] Read more.
Electrically driven legged robots have become popular in recent years. However, the development of reliable energy supply systems and effective energy management strategies for legged robots with dramatically varying power requirements still needs to be explored. This article proposes a learning-based model predictive control (MPC) energy management strategy for legged robots with battery–supercapacitor hybrid energy storage systems containing a power prediction unit and an MPC with learning-based adaptive weights. Firstly, the mathematical model of the legged robot is established, and a dual-layer long short-term memory network is constructed to predict the load power demand, providing the model and measurable disturbance for the MPC. Secondly, a multi-objective optimization objective function is established for the MPC-based energy management strategy. Three normalized terms, battery capacity loss, battery power fluctuation, and supercapacitor state-of-charge regulation, are balanced in the objective function. Finally, a deep learning algorithm is proposed to adaptively adjust the three weighting factors to meet the diverse operation conditions. Hardware-in-the-loop experimental implementations demonstrate that the proposed method can improve the kinematic performance of the legged robot by maintaining the supercapacitor state of charge at a relatively high level and reducing the battery capacity loss by 12.7% compared with the conventional MPC method. Full article
(This article belongs to the Special Issue Recent Advances in Autonomous Systems and Robotics, 2nd Edition)
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15 pages, 8494 KiB  
Article
Study on L-Bending Springback of 45 Steel Leather Cutting Tool Coupled with Local Induction Heating
by Yuan Cheng, Heran Geng, Chao Cao, Abul Fazal M. Arif, Xinhua Liu and Junfeng Yuan
Appl. Sci. 2024, 14(14), 6253; https://doi.org/10.3390/app14146253 - 18 Jul 2024
Viewed by 793
Abstract
Springback error is a major obstacle in L-bending sheets via cold working. Although thermal processing can effectively reduce the springback phenomenon, it is challenging to heat the sheet globally due to the influence of working conditions. Therefore, this study applied local induction heating [...] Read more.
Springback error is a major obstacle in L-bending sheets via cold working. Although thermal processing can effectively reduce the springback phenomenon, it is challenging to heat the sheet globally due to the influence of working conditions. Therefore, this study applied local induction heating to reduce the springback of 45 steel sheets and investigated the effects of bending parameters on springback behavior. Initially, a thermodynamic coupling model and a static model were created utilizing the VOCE hardening model and the von Mises yield criterion in the ANSYS workbench 2022 R2 software. The springback behavior and stress distribution of the sheets were then investigated under different temperatures (room temperature and 800 °C) and bending angles (15°, 30°, 45°, 60°, 75°, and 90°). Simultaneously, the experiments were performed to investigate springback behavior and guarantee the accuracy of the model. The results indicate that the springback reached a minimum value at the 45° bending angle at room temperature while increasing with the increasing bending angle under 800 °C local heating. The springback under local heating can be decreased by 75.2% and the error of the bending angle can improve by 2–7° compared with samples processed in room temperature. Full article
(This article belongs to the Special Issue Recent Advances in Autonomous Systems and Robotics, 2nd Edition)
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