Special Issue "Robotics and Automation Engineering"

A special issue of Robotics (ISSN 2218-6581).

Deadline for manuscript submissions: 28 February 2021.

Special Issue Editors

Dr. Ming Xie
Website
Guest Editor
Nanyang Technological University, Singapore City, Singapore
Interests: humanoid robotics (design, control, biped walking, mobile manipulation) and autonomous vehicles (perception, planning, and control)
Dr. Everett X. Wang
Website
Guest Editor
Guangdong University of Technology, Guangzhou, China
Interests: receiver and system design for global navigation satellite systems; transport models for advanced electron devices; modeling and control of robotic systems as well as deep learning in medical applications
Dr. Chun-Yi Su
Website
Guest Editor
Concordia University, Montréal, QC, Canada
Interests: control of nonlinear systems preceded by non-smooth nonlinearities; control of robotics and nonholonomic mechanical systems; mechatronics, fuzzy control techniques for nonlinear systems; control of metal cutting

Special Issue Information

Dear Colleagues,

The world’s civilization is rapidly evolving toward a massive deployment of automated machines in general and smart robots in particular. With the advance of various technologies such as sensing, planning, perception, control, machine intelligence, and cloud computing, future robots will be more skillful in handling all kinds of tasks in industry and society. Hence, it is important to keep track of the fast growth of theories and technologies which advance robotics and automation engineering.

In this Special Issue, we would like to select the best papers from the coming International Conference on Robotics and Automation Engineering, as well as to invite contributions of papers from authors outside the conference participants, in order to disseminate and share the recent progresses in the following areas:

  • Sensing and measurement in robotics and automation;
  • Planning and control in robotics and automation;
  • Modelling and simulation in robotics and automation;
  • Cognition and recognition in robotics and automation;
  • Grasping and manipulation in robotics and automation;
  • Wheeled and legged locomotion in robotics and automation;
  • Advanced robots for automated material handling;
  • Advanced robots for automated welding and painting;
  • Advanced robots for automated assembly;
  • Advanced robots for automated transportation;
  • Advanced robots for automated landing and unloading;
  • Key supporting technologies to robotics and automation

Dr. Ming Xie
Dr. Everett X. Wang
Dr. Chun-Yi Su
Guest Editors

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 papers will be 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. Robotics is an international peer-reviewed open access quarterly 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 1000 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
  • Automation
  • Control
  • Advanced robots

Published Papers (2 papers)

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Research

Open AccessArticle
Probabilistic Allocation of Specialized Robots on Targets Detected Using Deep Learning Networks
Robotics 2020, 9(3), 54; https://doi.org/10.3390/robotics9030054 - 16 Jul 2020
Abstract
Task allocation for specialized unmanned robotic agents is addressed in this paper. Based on the assumptions that each individual robotic agent possesses specialized capabilities and that targets representing the tasks to be performed in the surrounding environment impose specific requirements, the proposed approach [...] Read more.
Task allocation for specialized unmanned robotic agents is addressed in this paper. Based on the assumptions that each individual robotic agent possesses specialized capabilities and that targets representing the tasks to be performed in the surrounding environment impose specific requirements, the proposed approach computes task-agent fitting probabilities to efficiently match the available robotic agents with the detected targets. The framework is supported by a deep learning method with an object instance segmentation capability, Mask R-CNN, that is adapted to provide target object recognition and localization estimates from vision sensors mounted on the robotic agents. Experimental validation, for indoor search-and-rescue (SAR) scenarios, is conducted and results demonstrate the reliability and efficiency of the proposed approach. Full article
(This article belongs to the Special Issue Robotics and Automation Engineering)
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Open AccessArticle
Nonlinear Model Predictive Control for Mobile Robot Using Varying-Parameter Convergent Differential Neural Network
Robotics 2019, 8(3), 64; https://doi.org/10.3390/robotics8030064 - 31 Jul 2019
Cited by 8
Abstract
The mobile robot kinematic model is a nonlinear affine system, which is constrained by velocity and acceleration limits. Therefore, the traditional control methods may not solve the tracking problem because of the physical constraint. In this paper, we present the nonlinear model predictive [...] Read more.
The mobile robot kinematic model is a nonlinear affine system, which is constrained by velocity and acceleration limits. Therefore, the traditional control methods may not solve the tracking problem because of the physical constraint. In this paper, we present the nonlinear model predictive control (NMPC) algorithm to track the desired trajectory based on neural-dynamic optimization. In the proposed algorithm, the NMPC scheme utilizes a new neural network named the varying-parameter convergent differential neural network (VPCDNN) which is a Hopfifield-neural network structure with respect to the differential equation theory to solve the quadratic programming (QP) problem. The new network structure converges to the global optimal solution and it is more efficient than traditional numerical methods. In the simulation, we verify that the proposed method is able to successfully track reference trajectories with a two-wheel mobile robot. The experimental validation has been conducted in simulation and the results show that the proposed method is able to precisely track the trajectory maintaining a high robustness based on the VPCDNN solver. Full article
(This article belongs to the Special Issue Robotics and Automation Engineering)
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