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Machines
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Advanced Control and Robotic System in Path Planning
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Advanced Control and Robotic System in Path Planning

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Automation and Control Systems".

Deadline for manuscript submissions: closed (16 January 2024) | Viewed by 5914

Special Issue Editor

Dr. Augusto Luis Ballardini

E-Mail Website
Guest Editor
Alcalá de Henares Automation Department, Polytechnic School, Universidad de Alcalá, 28871 Madrid, Spain
Interests: vehicle localization; lidar localization; digital maps; computer vision; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

I am pleased to invite you to contribute to this Special Issue on the subject of control and path planning in autonomous systems.

In recent years, the scientific community’s interest in control and path-planning systems has been renewed considering the increased success of machine learning algorithms. Indeed, deep learning (DL) technologies are nowadays used to replace, assist, and enhance state-of-the-art approaches. This Special Issues aims to collate novel efficient techniques in the context of the path planning and control of autonomous robot systems for both single-robot and multi-robot systems with a special emphasis on current DL trends.

We also aim to create the opportunity to provide an overview of the latest developments in these fields, including expertise from both researchers and practitioners. Review papers are also welcome.

Topics of interest include, but are not limited to, the following:

  • Control in automation, mechatronics, and robotics;
  • Applications of deep reinforcement learning on control systems;
  • Application of machine vision techniques for path planning;
  • Application of the latest DNN techniques for control and path planning;
  • Intelligent and adaptive path planning for autonomous systems;
  • Related topics involving path planning and control;
  • Modern advanced control systems with applications;
  • Optimized control for autonomous systems;
  • Other machine learning approaches in path planning and control applications.

Dr. Augusto Luis Ballardini
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. 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.

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

25 pages, 8098 KiB  
Article
A Multi-Objective Trajectory Planning Method of the Dual-Arm Robot for Cabin Docking Based on the Modified Cuckoo Search Algorithm
by Ronghua Liu and Feng Pan
Machines 2024, 12(1), 64; https://doi.org/10.3390/machines12010064 - 16 Jan 2024
Cited by 1 | Viewed by 1338
Abstract
During the assembly of mechanical systems, the dual-arm robot is always used for cabin docking. In order to ensure the accuracy and reliability of cabin docking, a multi-objective trajectory planning method for the dual-arm robot was proposed. A kinematic model of the dual-arm [...] Read more.
During the assembly of mechanical systems, the dual-arm robot is always used for cabin docking. In order to ensure the accuracy and reliability of cabin docking, a multi-objective trajectory planning method for the dual-arm robot was proposed. A kinematic model of the dual-arm robot was constructed based on the Denavit–Hartenberg (D-H) method firstly. Then, in the Cartesian space, the end trajectory of the dual-arm robot was confirmed by the fifth-order B-spline curve. On the basis of a traditional multi-objective cuckoo search algorithm, a modified cuckoo algorithm was built using the improved initial population generation method and the step size. The total consumption time and joint impact were selected as the objective functions, the overall optimal solution for the modified cuckoo algorithm was obtained using the normalized evaluation method. The optimal trajectory planning was achieved. Finally, the feasibility and effectiveness of the trajectory planning method were verified with the experiments. Full article
(This article belongs to the Special Issue Advanced Control and Robotic System in Path Planning)
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17 pages, 995 KiB  
Article
Observer-Based Control of a Microrobot Navigating within a 3D Blood Vessel along a Trajectory Delivered by a Joystick Device
by Meziane Larbi, El-Hadi Guechi, Ahmed Maidi and Karim Belharet
Machines 2023, 11(7), 738; https://doi.org/10.3390/machines11070738 - 13 Jul 2023
Cited by 1 | Viewed by 1493
Abstract
In this paper, an observer-based state feedback control strategy for trajectory tracking of a magnetic microrobot navigating within a 3D blood vessel is proposed. The desired trajectory to be followed by the microrobot is generated by an operator using a joystick device. To [...] Read more.
In this paper, an observer-based state feedback control strategy for trajectory tracking of a magnetic microrobot navigating within a 3D blood vessel is proposed. The desired trajectory to be followed by the microrobot is generated by an operator using a joystick device. To deal with the significant effect of both external disturbances and parametric uncertainties, often encountered in biological environments, a state feedback stabilization, that enforces the output tracking despite any environmental disturbances, is developed. Then, for the purpose of implementation, a state observer is developed to recover the whole state from the measured position of the microrobot. The state feedback and observer gains are determined separately by solving a set of linear matrix inequalities derived in the framework of Lyapunov stability theory. Simulation runs are performed to demonstrate the performance of the proposed control strategy. Full article
(This article belongs to the Special Issue Advanced Control and Robotic System in Path Planning)
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21 pages, 21624 KiB  
Article
Field Complete Coverage Path Planning Based on Improved Genetic Algorithm for Transplanting Robot
by Xizhi Wu, Jinqiang Bai, Fengqi Hao, Guanghe Cheng, Yongwei Tang and Xiuhua Li
Machines 2023, 11(6), 659; https://doi.org/10.3390/machines11060659 - 19 Jun 2023
Cited by 12 | Viewed by 2384
Abstract
The Complete Coverage Path Planning (CCPP) is a key technology in the field of agricultural robots, and has great significance for improving the efficiency and quality of tillage, fertilization, harvesting, and other agricultural robot operations, as well as reducing the operation energy consumption. [...] Read more.
The Complete Coverage Path Planning (CCPP) is a key technology in the field of agricultural robots, and has great significance for improving the efficiency and quality of tillage, fertilization, harvesting, and other agricultural robot operations, as well as reducing the operation energy consumption. The traditional boustrophedon- or heuristic-search-algorithm-based CCPP methods, when coping with the field with irregular boundaries, obstacles, and other complex environments, still face many problems and challenges, such as large repeated work areas, multiple turns or U-turns, low operation efficiency, and prone to local optimum. In order to solve the above problems, an improved-genetic-algorithm-based CCPP method was proposed in this paper, the proposed method innovatively extends the traditional genetic algorithm’s chromosomes and single-point mutation into chromosome pairs and multi-point mutation, and proposed a multi-objective equilibrium fitness function. The simulation and experimental results on simple regular fields showed that the proposed improved-genetic-algorithm-based CCPP method achieved the comparable performance with the traditional boustrophedon-based CCPP method. However, on the complex irregular fields, the proposed CCPP method reduces 38.54% of repeated operation area and 35.00% of number of U-turns, and can save 7.82% of energy consumption on average. This proved that the proposed CCPP method has a strong adaptive capacity to the environment, and has practical application value in improving the efficiency and quality of agricultural machinery operations, and reducing the energy consumption. Full article
(This article belongs to the Special Issue Advanced Control and Robotic System in Path Planning)
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