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Machines
Special Issues
Design and Control of Agricultural Robots
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Design and Control of Agricultural Robots

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

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 3576

Special Issue Editor

Dr. Weixin Zhai

E-Mail Website
Guest Editor
Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications, Ministry of Agriculture and Rural Affairs, Beijing 100083, China
Interests: agricultural machinery; spatio-temporal big data

Special Issue Information

Dear Colleagues,

The Special Issue “Design and Control of Agricultural Robots” explores the field of robotics in agriculture, focusing on the design and control aspects of agricultural robots. It will delve into the various components involved in the design and control of agricultural robots, including hardware, sensing technologies, navigation systems, intelligent control algorithms, geographical information science, and agricultural applications of artificial intelligence and computational intelligence. It will highlight the potential benefits of deploying agricultural robots, such as increased efficiency, precision, and sustainability in farming operations. We encourage scholars to share their research aiming at the development of advanced robotic systems that can enhance agricultural practices and address challenges faced by modern agriculture.

Dr. Weixin Zhai
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.

Keywords

  • agricultural robot systems
  • sensing technologies
  • navigation and localization
  • robot manipulation and automation
  • intelligent control systems
  • human–robot interaction
  • field monitoring and data analysis
  • geographical information science
  • artificial intelligence

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Published Papers (2 papers)

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Research

15 pages, 12940 KiB  
Article
Design and Comparison of Two Maize Seeders Coupled with an Agricultural Robot
by Jesús A. López-Gómez, Joshua E. Patiño-Espejel, Noé Velázquez-López, David I. Sánchez-Chávez and Jelle Van Loon
Machines 2024, 12(12), 935; https://doi.org/10.3390/machines12120935 - 20 Dec 2024
Viewed by 1279
Abstract
In recent years, the development of robotic vehicles in agriculture has made it possible to reduce human intervention and fatigue in carrying out arduous or repetitive tasks, as well as helping to promote sustainable agriculture to address climate change. However, the great diversity [...] Read more.
In recent years, the development of robotic vehicles in agriculture has made it possible to reduce human intervention and fatigue in carrying out arduous or repetitive tasks, as well as helping to promote sustainable agriculture to address climate change. However, the great diversity of agricultural tasks and the varied production systems and crops demand a wide range of solutions that can be adapted to robotic vehicles as a power source. These alternatives must be affordable and user-friendly for some users, although more sophisticated solutions must also be developed for others, depending on their specific needs. For this, the present work focuses on the development of two maize seeders with different metering systems coupled to an agricultural robot. The first seeder has a conventional mechanically driven seed metering system with a drive wheel and chain gear, while the second one has an electronically driven metering system based on a DC motor and a digital encoder controlled by a microcontroller. Both seeders were coupled to a remote-controlled robotic vehicle and evaluated on real farmland. Seed distribution in the seed rows was contrasting; the results indicated that the mechanical system performed better in the field than the electronic system. For both seeders, the working capacity was approximately 0.135 ha/h at an average speed of 2.0 km/h. The proposed robot–seeder assembly could help farmers automate and reduce the workload associated with planting, as well as attract young people to the field. Full article
(This article belongs to the Special Issue Design and Control of Agricultural Robots)
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22 pages, 4565 KiB  
Article
Agricultural UAV Path Planning Based on a Differentiated Creative Search Algorithm with Multi-Strategy Improvement
by Jin Liu, Yong Lin, Xiang Zhang, Jibin Yin, Xiaoli Zhang, Yong Feng and Qian Qian
Machines 2024, 12(9), 591; https://doi.org/10.3390/machines12090591 - 26 Aug 2024
Cited by 2 | Viewed by 1243
Abstract
A differentiated creative search algorithm with multi-strategy improvement (MSDCS) is proposed for the path planning problem for agricultural UAVs under different complicated situations. First, the good point set and oppositional learning strategies are used to effectively improve the quality of population diversity; the [...] Read more.
A differentiated creative search algorithm with multi-strategy improvement (MSDCS) is proposed for the path planning problem for agricultural UAVs under different complicated situations. First, the good point set and oppositional learning strategies are used to effectively improve the quality of population diversity; the adaptive fitness–distance balance reset strategy is proposed to motivate the low performers to move closer to the region near the optimal solution and find the potential optimal solution; and the vertical and horizontal crossover strategy with random dimensions is proposed to improve the computational accuracy of the algorithm and the ability to jump out of the local optimum. Second, the MSDCS is compared to different algorithms using the IEEE_CEC2017 test set, which consists of 29 test functions. The results demonstrate that the MSDCS achieves the optimal value in 23 test functions, surpassing the comparison algorithms in terms of convergence accuracy, speed, and stability by at least one order of magnitude difference, and it is ranked No. 1 in terms of comprehensive performance. Finally, the enhanced algorithm was employed to address the issue of path planning for agricultural UAVs. The experimental results demonstrate that the MSDCS outperforms comparison algorithms in path planning across various contexts. Consequently, the MSDCS can generate optimal pathways that are both rational and safe for agricultural UAV operations. Full article
(This article belongs to the Special Issue Design and Control of Agricultural Robots)
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