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Advanced Robotic Manipulators and Control Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensors and Robotics".

Deadline for manuscript submissions: 1 December 2024 | Viewed by 1821

Special Issue Editors


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Guest Editor
School of Automation, Beijing Institute of Technology, Beijing 100081, China
Interests: Intelligent & Robotic Systems; Motion Control; Speed Consensus Control; Flexible Gait Transition Control

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Guest Editor
School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China
Interests: wheel-leg robot motion control; robot intelligent perception and image processing; unmanned aerial vehicle (UAV) mobile takeover through heterogeneous intelligent agent collaboration; multi channel electro-hydraulic servo system drive and control

Special Issue Information

Dear Colleagues,

In recent years, robotics has witnessed remarkable advancements, transforming various industries and aspects of our daily lives. From manufacturing and healthcare to transportation and exploration, robots play a pivotal role in enhancing efficiency, safety, and precision. The integration of cutting-edge technologies, such as artificial intelligence, machine learning, and sensor networks, has fueled the rapid evolution of robotics. As a result, there is a growing need to explore and disseminate research related to advanced robotics and their control mechanisms. These sophisticated machines, which are equipped with state-of-the-art sensors, actuators, and artificial intelligence, have the capability to perform complex tasks either autonomously or in collaboration with humans. The development and application of advanced robotics have far-reaching implications across various domains. Advanced robotics builds upon the foundation of traditional robotics, integrating breakthroughs in computer science, materials science, and engineering. It encompasses a wide range of robotic systems, including industrial robots, medical robots, drones, and autonomous vehicles. These robots exhibit enhanced capabilities such as perception, decision-making, and adaptability. Advanced robotics, which is reshaping industries, economies, and our daily lives, signifies a paradigm shift in technology. As research progresses, we anticipate witnessing even more remarkable applications and innovations in the coming years.

This Special Issue aims to address several research areas, including advanced robot design, system dynamics, intelligent perception and decision-making, and robot control, by considering these areas. Through the promotion of interdisciplinary collaboration and the exchange of knowledge, our goal is to expedite the adoption of advanced robotics solutions across various fields. This involves a detailed exploration of case studies that highlight the latest advancements in robotics and their applications for control.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Robot design and kinematics
  • Motion control strategies
  • Multi-agent collaborative control
  • Reinforcement learning for robotics
  • Robot vision and perception
  • Simultaneous localization and mapping (SLAM)
  • Robot motion planning and navigation
  • Velocity profile planning
  • Terrain detection and adaptation
  • Docking and target interaction control
  • Heterogeneous and cross-domain control
  • Data-driven control
  • Learning-based control and its application in robots
  • Cyber–physical systems
  • Rehabilitation robots, prosthetics, and exoskeleton robots
  • Medical and surgical robots, biomimetic robots
  • Robot perception and environmental adaptability
  • Human–machine interaction and collaboration

Prof. Dr. Shoukun Wang
Dr. Zhihua Chen
Guest Editors

Manuscript Submission Information

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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. Sensors 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

  • robotics motion control
  • multi-agent reinforcement learning
  • medical image processing
  • motion planning
  • velocity profile planning
  • heterogeneous agent
  • data-driven
  • cyber-physical systems

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Published Papers (2 papers)

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Research

20 pages, 8922 KiB  
Article
Prediction and Elimination of Physiological Tremor During Control of Teleoperated Robot Based on Deep Learning
by Juntao Chen, Zhiqing Zhang, Wei Guan, Xinxin Cao and Ke Liang
Sensors 2024, 24(22), 7359; https://doi.org/10.3390/s24227359 - 18 Nov 2024
Viewed by 368
Abstract
Currently, teleoperated robots, with the operator’s input, can fully perceive unknown factors in a complex environment and have strong environmental interaction and perception abilities. However, physiological tremors in the human hand can seriously affect the accuracy of processes that require high-precision control. Therefore, [...] Read more.
Currently, teleoperated robots, with the operator’s input, can fully perceive unknown factors in a complex environment and have strong environmental interaction and perception abilities. However, physiological tremors in the human hand can seriously affect the accuracy of processes that require high-precision control. Therefore, this paper proposes an EEMD-IWOA-LSTM model, which can decompose the physiological tremor of the hand into several intrinsic modal components (IMF) by using the EEMD decomposition strategy and convert the complex nonlinear and non-stationary physiological tremor curve of the human hand into multiple simple sequences. An LSTM neural network is used to build a prediction model for each (IMF) component, and an IWOA is proposed to optimize the model, thereby improving the prediction accuracy of the physiological tremor and eliminating it. At the same time, the prediction results of this model are compared with those of different models, and the results of EEMD-IWOA-LSTM presented in this study show obvious superior performance. In the two examples, the MSE of the prediction model proposed are 0.1148 and 0.00623, respectively. The defibrillation model proposed in this study can effectively eliminate the physiological tremor of the human hand during teleoperation and improve the control accuracy of the robot during teleoperation. Full article
(This article belongs to the Special Issue Advanced Robotic Manipulators and Control Applications)
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17 pages, 6860 KiB  
Article
Online Flow Measurement of Liquid Metal Solutions Based on Impact Force Sequences: Modeling Analysis, Simulation, and Validation of Experimental Results
by Qiguang Li, Xiru Zheng, Yu He, Fangmin Xu, Yulin Zhuang, Bingji Zeng and Bofang Duan
Sensors 2024, 24(14), 4553; https://doi.org/10.3390/s24144553 - 14 Jul 2024
Viewed by 794
Abstract
Aiming at the existing high-temperature liquid metal flow online accurate measurement by the metal melt characteristics, installation space, and high-temperature environment adaptability limitations, this paper innovatively puts forward a soft measurement method based on the impact force generated in the fluid flow process [...] Read more.
Aiming at the existing high-temperature liquid metal flow online accurate measurement by the metal melt characteristics, installation space, and high-temperature environment adaptability limitations, this paper innovatively puts forward a soft measurement method based on the impact force generated in the fluid flow process as an observational variable series. Fluid mechanics theory and simulation software are used to analyze and verify the feasibility of the impact force as an observable variable to measure the flow rate, followed by the construction of the CNN-LSTM-CNN-Double (CLCD) flow measurement model of impact force and flow rate based on the parameters of the learning rate and the number of training times, and finally the construction of a test platform for the flow measurement, and the validity of the method is verified through actual operation. Full article
(This article belongs to the Special Issue Advanced Robotic Manipulators and Control Applications)
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