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Machines
Special Issues
Recent Trends in Robot Motion Planning and Control
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Recent Trends in Robot Motion Planning and Control

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Robotics, Mechatronics and Intelligent Machines".

Deadline for manuscript submissions: closed (15 June 2023) | Viewed by 11581

Special Issue Editors

Dr. Meng Yuan

E-Mail Website
Guest Editor
Rehabilitation Research Institute of Singapore, Nanyang Technological University, Singapore, Singapore
Interests: control systems; motion control
Dr. Ye Wang

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, Australia
Interests: model predictive control; learning-based control
Dr. Lei Li

E-Mail Website
Guest Editor
Rehabilitation Research Institute of Singapore, Nanyang Technological University, Singapore, Singapore
Interests: robotics; mechatronics
Prof. Dr. Wei Tech Ang

E-Mail Website
Guest Editor
Rehabilitation Research Institute of Singapore, Nanyang Technological University, Singapore, Singapore
Interests: bio-robotics; bio-mechatronics; rehabilitation and assistive technology; control

Special Issue Information

Dear Colleagues,

The emergence of robots has brought great convenience to people’s lives in the fields of manufacturing, assembly, rehabilitation, assistance, and others. As an interplanetary topic, the blossoming of robots involves advances in electronics, mechatronics, control engineering etc. Motion planning and control problems are essential in robot development for achieving optimal movement and interaction with the environment and/or humans.  

This Special Issue intends to cover recent developments in robotics regarding motion planning and control that are related to dynamic modelling, path planning, machine learning, optimization, model-based control, robot navigation, human–machine interfaces etc., such that safe and reliable operation can be achieved.

Dr. Meng Yuan
Dr. Ye Wang
Dr. Lei Li
Prof. Dr. Wei Tech Ang
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 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. Machines is an international peer-reviewed open access monthly 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

  • robotics
  • motion planning
  • motion control
  • optimization-based control
  • learning-based control
  • human-machine interface
  • system modelling
  • nonlinear control

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Published Papers (3 papers)

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Research

21 pages, 11280 KiB  
Article
A Planning Framework for Robotic Insertion Tasks via Hydroelastic Contact Model
by Lin Yang, Mohammad Zaidi Ariffin, Baichuan Lou, Chen Lv and Domenico Campolo
Machines 2023, 11(7), 741; https://doi.org/10.3390/machines11070741 - 14 Jul 2023
Cited by 3 | Viewed by 4440
Abstract
Robotic contact-rich insertion tasks present a significant challenge for motion planning due to the complex force interaction between robots and objects. Although many learning-based methods have shown success in contact tasks, most methods need sampling or exploring to gather sufficient experimental data. However, [...] Read more.
Robotic contact-rich insertion tasks present a significant challenge for motion planning due to the complex force interaction between robots and objects. Although many learning-based methods have shown success in contact tasks, most methods need sampling or exploring to gather sufficient experimental data. However, it is both time-consuming and expensive to conduct real-world experiments repeatedly. On the other hand, while the virtual world enables low cost and fast computations by simulators, there still exists a huge sim-to-real gap due to the inaccurate point contact model. Although finite element analysis might generate accurate results for contact tasks, it is computationally expensive. As such, this study proposes a motion planning framework with bilevel optimization to leverage relatively accurate force information with fast computation time. This framework consists of Dynamic Movement Primitives (DMPs) used to parameterize motion trajectories, Black-Box Optimization (BBO), a derivative-free approach, integrated to improve contact-rich insertion policy with hydroelastic contact model, and simulated variability to account for visual uncertainty in the real world. The accuracy of the simulated model is then validated by comparing our contact results with a benchmark Peg-in-Hole task. Using these integrated DMPs and BBO with hydroelastic contact model, the motion trajectory generated in planning is capable of guiding the robot towards successful insertion with iterative refinement. Full article
(This article belongs to the Special Issue Recent Trends in Robot Motion Planning and Control)
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23 pages, 5107 KiB  
Article
A Single-Loop Framework for the Reliability-Based Control Co-Design Problem in the Dynamic System
by Qi Zhang, Yizhong Wu, Li Lu and Ping Qiao
Machines 2023, 11(2), 262; https://doi.org/10.3390/machines11020262 - 9 Feb 2023
Cited by 6 | Viewed by 1509
Abstract
When solving the control co-design (CCD) problem using the simultaneous strategy in a deterministic manner, the uncertainty stemming from the stochastic design variables is ignored, and might have a negative influence on the performance of the dynamic system. In attempting to overcome the [...] Read more.
When solving the control co-design (CCD) problem using the simultaneous strategy in a deterministic manner, the uncertainty stemming from the stochastic design variables is ignored, and might have a negative influence on the performance of the dynamic system. In attempting to overcome the undesirable effect of the uncertainty, this research investigates the reliability-based control co-design (RB-CCD) problem and presents a single-loop framework for RB-CCD based on the modified RB-CCD model and single-loop approach (SLA). Specifically, the modified model is deduced by introducing additional design variables and equality constraints (state equations and algebraic equality constraints) so as to transform the probabilistic constraints into inequality constraints. Meanwhile, to enhance the solution efficiency, SLA transforms the modified RB-CCD model into an equivalent single-loop deterministic CCD model by incorporating the approximate reliability information of the stochastic design variables into the deterministic optimization. Finally, a numerical example and an engineering example are implemented to verify the feasibility and effectiveness of the single-loop RB-CCD optimization framework. The results demonstrate that the suggested single-loop framework dramatically improves the reliability of the dynamic system, and significantly increases the solving efficiency without compromising accuracy. Full article
(This article belongs to the Special Issue Recent Trends in Robot Motion Planning and Control)
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14 pages, 1078 KiB  
Article
Adaptive Backlash Compensation for CNC Machining Applications
by Lu Gan, Liuping Wang and Fei Huang
Machines 2023, 11(2), 193; https://doi.org/10.3390/machines11020193 - 1 Feb 2023
Cited by 8 | Viewed by 4869
Abstract
The mechanical transmission employed inside the computer numerical control (CNC) machine electromechanical system usually has an inherent backlash. As a position-controlled system is commonly used for the electromechanical system, the backlash limits the performance of the motion control system due to its nonlinearity [...] Read more.
The mechanical transmission employed inside the computer numerical control (CNC) machine electromechanical system usually has an inherent backlash. As a position-controlled system is commonly used for the electromechanical system, the backlash limits the performance of the motion control system due to its nonlinearity and discontinuity. This paper proposes an effective method to adaptively detect and compensate for the backlash effect in real time, in which the end-effect load position of the CNC machine is estimated and controlled by the position-controlled servo system, in order to eliminate the influence of backlash on the contour path performance. The simulation results obtained from the model of a realistic CNC machine show the successful elimination of the error between the reference and the end-effector position and a significant improvement in the control system performance. Full article
(This article belongs to the Special Issue Recent Trends in Robot Motion Planning and Control)
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