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Autonomous Agricultural Robots

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

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 6288

Special Issue Editors

Centre for Automation and Robotics, CSIC-UPM, Arganda del Rey, 28500 Madrid, Spain
Interests: artificial perception; computer vision; evolutionary algorithms; spatial knowledge representation; spatial reasoning for decision support systems; distributed systems; collective intelligence; collective robotics; precision farming
1. Centre for Automation and Robotics, CSIC-UPM, Arganda del Rey, 28500 Madrid, Spain
2. Centre for Electrical, Mechanical and Industrial Research and Innovation (CINEMI), Universidad Tecnologica de Panama, 0819-07289 Panama City, Panama
Interests: robotics; automation; control; manipulators
Centre for Automation and Robotics, CSIC-UPM, Arganda del Rey, 28500 Madrid, Spain
Interests: robotics; autonomous navigation; 3D reconstruction

Special Issue Information

Dear Colleagues,

There is no doubt that robotics applied to agriculture is key to improving the productivity of crop fields and product quality. Robotics for agriculture includes all those systems of perception, reasoning and action in the agricultural field, with the aim of performing specific tasks in the crop, in an autonomous, semi-autonomous or tele-operated way. These robotic systems increase precision, accuracy and efficiency in the use of inputs during crop growth, favoring increased productivity and agricultural sustainability, thus contributing to sustainable development objectives.

Despite the advances made in recent decades, there are still challenges that are becoming important lines of research. Important examples are autonomous navigation of robots in environments with high uncertainty and variability, early detection of pests and their local treatment, coordination of multiple heterogeneous robots to complete different agricultural tasks, etc. which must be addressed to achieve complete automation in agriculture.

This Special Issue aims to bring together the most recent advances in robotics applied to agriculture and its supporting technologies.

We would like to invite the academic and industrial research community to submit original research and review articles to this Special Issue of Sensors (Impact Factor = 3.576).

The scope of this Special Issue includes, but is not limited to, the following topics:

  • Robotics for pruning, thinning, harvesting, mowing, spraying, weed removal and any other agricultural application
  • Aerial and ground robotic platforms in agriculture
  • Autonomous navigation of robotics vehicles in unstructured farming environments
  • Manipulators in agricultural applications
  • Use of exoskeletons in agriculture
  • Fruit and flower detection and recognition
  • Early pest detection and treatment
  • Machine learning and artificial intelligence integrated in agricultural machinery
  • Multi-robot systems
  • Robotics for greenhouse
  • Robotics for vertical farming
  • Robotics for urban farming
  • Human–robot interaction in farming
  • Soft robots for farming

Dr. Angela Ribeiro
Dr. Héctor Montes
Dr. Jose Maria Bengochea-Guevara
Dr. Dionisio Andújar
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. 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

  • aerial robotics
  • ground robotics
  • manipulators
  • exoskeletons
  • pest detection and treatment
  • fruit and flower detection
  • multi-robot systems
  • greenhouse
  • vertical farming
  • urban farming
  • human–robot interaction
  • soft robots

Published Papers (3 papers)

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Research

17 pages, 6503 KiB  
Article
Development of Path Generation and Algorithm for Autonomous Combine Harvester Using Dual GPS Antenna
by Kyuho Lee, Hyohyuk Choi and Junghun Kim
Sensors 2023, 23(10), 4944; https://doi.org/10.3390/s23104944 - 21 May 2023
Viewed by 1582
Abstract
Research on autonomous driving technology is actively underway to solve the facing problems in the agricultural field. Combine harvesters used in East Asian countries, including Korea, are tracked-type vehicles. The steering control system of the tracked vehicle has different characteristics from the wheeled [...] Read more.
Research on autonomous driving technology is actively underway to solve the facing problems in the agricultural field. Combine harvesters used in East Asian countries, including Korea, are tracked-type vehicles. The steering control system of the tracked vehicle has different characteristics from the wheeled vehicle used in the agricultural tractor. In this paper, a dual GPS antenna-based autonomous driving system and path tracking algorithm were developed for a robot combine harvester. An α-turn-type work path generation algorithm and a path tracking algorithm were developed. The developed system and algorithm were verified through experiments using actual combine harvesters. The experiment consisted of an experiment with harvesting work and an experiment without harvesting work. In the experiment without harvesting work, an error of 0.052 m occurred during working driving and 0.207 m during turning driving. In the experiment where the harvesting work was carried out, an error of 0.038 m occurred during work driving and 0.195 m during turning driving. As a result of comparing the non-work area and driving time to the results of manual driving, the self-driving experiment with harvesting work showed an efficiency of 76.7%. Full article
(This article belongs to the Special Issue Autonomous Agricultural Robots)
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17 pages, 5147 KiB  
Article
Development of a Moving Baseline RTK/Motion Sensor-Integrated Positioning-Based Autonomous Driving Algorithm for a Speed Sprayer
by Joong-hee Han, Chi-ho Park and Young Yoon Jang
Sensors 2022, 22(24), 9881; https://doi.org/10.3390/s22249881 - 15 Dec 2022
Cited by 3 | Viewed by 1693
Abstract
To address problems such as pesticide poisoning and accidents during pest control work and to enable efficient work in this area, the development of a competitively prices speed sprayer with autonomous driving is required. Accordingly, in order to contribute to developing the commercialization [...] Read more.
To address problems such as pesticide poisoning and accidents during pest control work and to enable efficient work in this area, the development of a competitively prices speed sprayer with autonomous driving is required. Accordingly, in order to contribute to developing the commercialization of a low-cost autonomous driving speed sprayer, we developed a positioning algorithm and an autonomous driving-based spraying algorithm by using two low-cost global navigation satellite system (GNSS) modules and a low-cost motion sensor. In order to provide stable navigation solutions from the autonomous driving hardware despite disturbances from the electromagnetic field generated by the spraying device, the proposed positioning algorithm, a moving baseline (MB) real-time kinematic (RTK)/motion sensor-integrated positioning algorithm, was developed using a loosely coupled extended Kalman filter. To compare the yaw estimation performance provided by the MB RTK positioning technique, yaw was calculated by post-processing with two types of positioning algorithms: the MB RTK/motion sensor-integrated positioning algorithm and the GNSS RTK/motion sensor-integrated positioning algorithm. In the static test, the precision of the yaw provided by the MB RTK/motion sensor-integrated positioning algorithm was 0.14°, but with the GNSS RTK/motion sensor-integrated positioning algorithm, the precision of the yaw was 4.53°. The static test results confirmed that the proposed positioning algorithm using the yaw provided by the MB RTK positioning technique based on two GNSS modules for measurement, precisely estimated the yaw even when the spray engine was operating. To perform autonomous driving and spraying, an autonomous driving-based spraying algorithm was developed using the MB RTK/motion sensor-integrated positioning algorithm. As a result of two performance tests based on the proposed algorithm in an orchard, autonomous driving and spraying were stably performed according to the set autonomous driving route and spraying method, and the root mean square (RMS) of the path-following error was 0.06 m. Full article
(This article belongs to the Special Issue Autonomous Agricultural Robots)
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15 pages, 54788 KiB  
Article
Evaluation of a CNN-Based Modular Precision Sprayer in Broadcast-Seeded Field
by Paolo Rommel Sanchez and Hong Zhang
Sensors 2022, 22(24), 9723; https://doi.org/10.3390/s22249723 - 12 Dec 2022
Cited by 2 | Viewed by 1813
Abstract
In recent years, machine vision systems (MVS) with convolutional neural networks (CNN) for precision spraying have been increasingly investigated due to their robust performance in plant detection. However, the high computational requirement of CNNs makes them slow to be adopted in field operations, [...] Read more.
In recent years, machine vision systems (MVS) with convolutional neural networks (CNN) for precision spraying have been increasingly investigated due to their robust performance in plant detection. However, the high computational requirement of CNNs makes them slow to be adopted in field operations, especially in unstructured working environments such as broadcast-seeded fields. In this study, we developed a modular precision sprayer by distributing the high computational load of CNN among parallel low-cost and low-power vision computing devices. The sprayer utilized a custom precision spraying algorithm based on SSD-MobileNetV1 running on a Jetson Nano 4 GB. The model achieved 76% mAP0.5 at 19 fps for weed and soybean detection in a broadcast-seeded field. Further, the sprayer targeted all weed samples and exhibited up to 48.89% spray volume reduction with a typical walking speed up to 3.0 km/h, which was three times faster than similar systems with known targeting performance. With these results, the study demonstrated that CNN-based precision spraying in a complex broadcast-seeded field can achieve increased velocity at high accuracy without needing powerful and expensive computational hardware using modular designs. Full article
(This article belongs to the Special Issue Autonomous Agricultural Robots)
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