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A special issue of Robotics (ISSN 2218-6581). This special issue belongs to the section "Agricultural and Field Robotics".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 43939

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Dr. Giulio Reina

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Department of Mechanics, Mathematics, and Management, Politecnico di Bari, 70126 Bari, BA, Italy
Interests: vehicle dynamics and control; terramechanics; advanced driving assistance systems (ADAS) for automotive and agriculture industry towards self-driving vehicles; mobile robotics for planetary exploration

Special Issue Information

Dear Colleagues,

To meet the rising food demand of a world population predicted to reach 9.8 billion in 2050 while guaranteeing environmental sustainability, it is critical to improve crop production by introducing new technologies and artificial intelligence to accelerate the current transition towards the Agriculture 4.0 paradigm. Impressive progress has been achieved in agricultural robotics, with many applications having been demonstrated, including automated fruit harvesting, pruning, crop phenotyping and monitoring, weed control, selective spraying of pesticides and fertilisers, and more.

Nevertheless, new challenges need to be addressed in many areas of robotics, such as motion planning and control, manipulation, learning, perception, and locomotion to further improve the capabilities and the autonomy of farmer robots in challenging agricultural settings both in open field and greenhouse conditions. An underlying theme in agricultural robotics is the interdisciplinary nature that covers both robotics and natural sciences.

This Special Issue aims to present new innovative approaches in agricultural robotics and artificial intelligence. We solicit original contributions from both researchers and practitioners reporting on ideas and approaches to enable robotic systems in agriculture. Attention will be given to fostering the connection between robotics and plant sciences for solving real-world problems. Of particular interest are papers in which the research combines design and analysis and involves an investigation into disruptive technologies such as artificial intelligence and the Internet of Things, as well as the study of innovative locomotion systems, including passive/active articulated suspension systems for wheeled robots, articulated tracked vehicles, legged robots, and hybrid, reconfigurable, and bio-inspired architectures.

Dr. Giulio Reina
Guest Editor

Manuscript Submission Information

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Published Papers (13 papers)

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Editorial

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3 pages, 133 KiB

Open AccessEditorial

Robotics and AI for Precision Agriculture

by Giulio Reina

Robotics 2024, 13(4), 64; https://doi.org/10.3390/robotics13040064 - 20 Apr 2024

Cited by 1 | Viewed by 5553

Abstract

To meet the rising food demand of a world population predicted to reach 9 [...] Full article

(This article belongs to the Special Issue Robotics and AI for Precision Agriculture)

Research

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20 pages, 46648 KiB

Open AccessArticle

Generating a Dataset for Semantic Segmentation of Vine Trunks in Vineyards Using Semi-Supervised Learning and Object Detection

by Petar Slaviček, Ivan Hrabar and Zdenko Kovačić

Robotics 2024, 13(2), 20; https://doi.org/10.3390/robotics13020020 - 23 Jan 2024

Cited by 1 | Viewed by 2412

Abstract

This article describes an experimentally tested approach using semi-supervised learning for generating new datasets for semantic segmentation of vine trunks with very little human-annotated data, resulting in significant savings in time and resources. The creation of such datasets is a crucial step towards the development of autonomous robots for vineyard maintenance. In order for a mobile robot platform to perform a vineyard maintenance task, such as suckering, a semantically segmented view of the vine trunks is required. The robot must recognize the shape and position of the vine trunks and adapt its movements and actions accordingly. Starting with vine trunk recognition and ending with semi-supervised training for semantic segmentation, we have shown that the need for human annotation, which is usually a time-consuming and expensive process, can be significantly reduced if a dataset for object (vine trunk) detection is available. In this study, we generated about 35,000 images with semantic segmentation of vine trunks using only 300 images annotated by a human. This method eliminates about 99% of the time that would be required to manually annotate the entire dataset. Based on the evaluated dataset, we compared different semantic segmentation model architectures to determine the most suitable one for applications with mobile robots. A balance between accuracy, speed, and memory requirements was determined. The model with the best balance achieved a validation accuracy of 81% and a processing time of only 5 ms. The results of this work, obtained during experiments in a vineyard on karst, show the potential of intelligent annotation of data, reducing the time required for labeling and thus paving the way for further innovations in machine learning. Full article

(This article belongs to the Special Issue Robotics and AI for Precision Agriculture)

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24 pages, 10233 KiB

Open AccessEditor’s ChoiceArticle

An Efficient Guiding Manager for Ground Mobile Robots in Agriculture

by Luis Emmi, Roemi Fernández and Pablo Gonzalez-de-Santos

Robotics 2024, 13(1), 6; https://doi.org/10.3390/robotics13010006 - 26 Dec 2023

Cited by 5 | Viewed by 2878

Abstract

Mobile robots have become increasingly important across various sectors and are now essential in agriculture due to their ability to navigate effectively and precisely in crop fields. Navigation involves the integration of several technologies, including robotics, control theory, computer vision, and artificial intelligence, among others. Challenges in robot navigation, particularly in agriculture, include mapping, localization, path planning, obstacle detection, and guiding control. Accurate mapping, localization, and obstacle detection are crucial for efficient navigation, while guiding the robotic system is essential to execute tasks accurately and for the safety of crops and the robot itself. Therefore, this study introduces a Guiding Manager for autonomous mobile robots specialized for laser-based weeding tools in agriculture. The focus is on the robot’s tracking, which combines a lateral controller, a spiral controller, and a linear speed controller to adjust to the different types of trajectories that are commonly followed in agricultural environments, such as straight lines and curves. The controllers have demonstrated their usefulness in different real work environments at different nominal speeds, validated on a tracked mobile platform with a width of about 1.48 m, in complex and varying field conditions including loose soil, stones, and humidity. The lateral controller presented an average absolute lateral error of approximately 0.076 m and an angular error of about 0.0418 rad, while the spiral controller presented an average absolute lateral error of about 0.12 m and an angular error of about 0.0103 rad, with a horizontal accuracy of about ±0.015 m and an angular accuracy of about ±0.009 rad, demonstrating its effectiveness in real farm tests. Full article

(This article belongs to the Special Issue Robotics and AI for Precision Agriculture)

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17 pages, 4938 KiB

Open AccessArticle

Robot Learning by Demonstration with Dynamic Parameterization of the Orientation: An Application to Agricultural Activities

by Clemente Lauretti, Christian Tamantini, Hilario Tomè and Loredana Zollo

Robotics 2023, 12(6), 166; https://doi.org/10.3390/robotics12060166 - 7 Dec 2023

Cited by 4 | Viewed by 1986

Abstract

This work proposes a Learning by Demonstration framework based on Dynamic Movement Primitives (DMPs) that could be effectively adopted to plan complex activities in robotics such as the ones to be performed in agricultural domains and avoid orientation discontinuity during motion learning. The [...] Read more.

S O (3)

into an element of the tangent space

s o (3)

and vice versa. Moreover, it includes a dynamic parameterization for the tangent space elements to manage the discontinuity of the logarithmic map. The proposed approach was tested on the Tiago robot during the fulfillment of four agricultural activities, such as digging, seeding, irrigation and harvesting. The obtained results were compared to the one achieved by using the original formulation of the DMPs and demonstrated the high capability of the proposed method to manage orientation discontinuity (the success rate was 100 % for all the tested poses). Full article

(This article belongs to the Special Issue Robotics and AI for Precision Agriculture)

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16 pages, 40065 KiB

Open AccessArticle

AgroCableBot: Reconfigurable Cable-Driven Parallel Robot for Greenhouse or Urban Farming Automation

by Andrés García-Vanegas, María J. García-Bonilla, Manuel G. Forero, Fernando J. Castillo-García and Antonio Gonzalez-Rodriguez

Robotics 2023, 12(6), 165; https://doi.org/10.3390/robotics12060165 - 1 Dec 2023

Cited by 1 | Viewed by 2851

Abstract

In this paper, a Cable-Driven Parallel Robot developed to automate repetitive and essential tasks in crop production in greenhouse and urban garden environments is introduced. The robot has a suspended configuration with five degrees-of-freedom, composed of a fixed platform (frame) and a moving platform known as the end-effector. To generate its movements and operations, eight cables are used, which move through eight pulley systems and are controlled by four winches. In addition, the robot is equipped with a seedbed that houses potted plants. Unlike conventional suspended cable robots, this robot incorporates four moving pulley systems in the frame, which significantly increases its workspace. The development of this type of robot requires precise control of the end-effector pose, which includes both the position and orientation of the robot extremity. To achieve this control, analysis is performed in two fundamental aspects: kinematic analysis and dynamic analysis. In addition, an analysis of the effective workspace of the robot is carried out, taking into account the distribution of tensions in the cables. The aim of this analysis is to verify the increase of the working area, which is useful to cover a larger crop area. The robot has been validated through simulations, where possible trajectories that the robot could follow depending on the tasks to be performed in the crop are presented. This work supports the feasibility of using this type of robotic systems to automate specific agricultural processes, such as sowing, irrigation, and crop inspection. This contribution aims to improve crop quality, reduce the consumption of critical resources such as water and fertilizers, and establish them as technological tools in the field of modern agriculture. Full article

(This article belongs to the Special Issue Robotics and AI for Precision Agriculture)

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21 pages, 6754 KiB

Open AccessArticle

Cooperative Grape Harvesting Using Heterogeneous Autonomous Robots

by Chris Lytridis, Christos Bazinas, Ioannis Kalathas, George Siavalas, Christos Tsakmakis, Theodoros Spirantis, Eftichia Badeka, Theodore Pachidis and Vassilis G. Kaburlasos

Robotics 2023, 12(6), 147; https://doi.org/10.3390/robotics12060147 - 28 Oct 2023

Cited by 5 | Viewed by 3266

Abstract

The development of agricultural robots is an increasingly popular research field aiming at addressing the widespread labor shortages in the farming industry and the ever-increasing food production demands. In many cases, multiple cooperating robots can be deployed in order to reduce task duration, perform an operation not possible with a single robot, or perform an operation more effectively. Building on previous results, this application paper deals with a cooperation strategy that allows two heterogeneous robots to cooperatively carry out grape harvesting, and its implementation is demonstrated. More specifically, the cooperative grape harvesting task involves two heterogeneous robots, where one robot (i.e., the expert) is assigned the grape harvesting task, whereas the second robot (i.e., the helper) is tasked with supporting the harvesting task by carrying the harvested grapes. The proposed cooperative harvesting methodology ensures safe and effective interactions between the robots. Field experiments have been conducted in order firstly to validate the effectiveness of the coordinated navigation algorithm and secondly to demonstrate the proposed cooperative harvesting method. The paper reports on the conclusions drawn from the field experiments, and recommendations for future enhancements are made. The potential of sophisticated as well as explainable decision-making based on logic for enhancing the cooperation of autonomous robots in agricultural applications is discussed in the context of mathematical lattice theory. Full article

(This article belongs to the Special Issue Robotics and AI for Precision Agriculture)

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29 pages, 6579 KiB

Open AccessArticle

Non-Prehensile Manipulation Actions and Visual 6D Pose Estimation for Fruit Grasping Based on Tactile Sensing

by Marco Costanzo, Marco De Simone, Sara Federico and Ciro Natale

Robotics 2023, 12(4), 92; https://doi.org/10.3390/robotics12040092 - 25 Jun 2023

Cited by 3 | Viewed by 2652

Abstract

Robotic manipulation in cluttered environments is one of the challenges roboticists are currently facing. When the objects to handle are delicate fresh fruits, grasping is even more challenging. Detecting and localizing fruits with the accuracy necessary to grasp them is very difficult due to the large variability in the aspect and dimensions of each item. This paper proposes a solution that exploits a state-of-the-art neural network and a novel enhanced 6D pose estimation method that integrates the depth map with the neural network output. Even with an accurate localization, grasping fruits with a suitable force to avoid slippage and damage at the same time is another challenge. This work solves this issue by resorting to a grasp controller based on tactile sensing. Depending on the specific application scenario, grasping a fruit might be impossible without colliding with other objects or other fruits. Therefore, a non-prehensile manipulation action is here proposed to push items hindering the grasp of a detected fruit. The pushing from an initial location to a target one is performed by a model predictive controller taking into account the unavoidable delay in the perception and computing pipeline of the robotic system. Experiments with real fresh fruits demonstrate that the overall proposed approach allows a robot to successfully grasp apples in various situations. Full article

(This article belongs to the Special Issue Robotics and AI for Precision Agriculture)

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16 pages, 5225 KiB

Open AccessArticle

Path Following for an Omnidirectional Robot Using a Non-Linear Model Predictive Controller for Intelligent Warehouses

by Rocco Galati and Giacomo Mantriota

Robotics 2023, 12(3), 78; https://doi.org/10.3390/robotics12030078 - 29 May 2023

Cited by 8 | Viewed by 2166

Abstract

This paper presents results coming from a non-linear model predictive controller used to generate optimized trajectories specifically for an omnidirectional robot equipped with a spraying unit to mark on the floor the perimeter of dangerous areas or to move large palletized goods inside warehouses. Results on different trajectories and with moving obstacles are provided along with considerations on the controller performance. Full article

(This article belongs to the Special Issue Robotics and AI for Precision Agriculture)

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13 pages, 9391 KiB

Open AccessArticle

Tractor-Robot Cooperation: A Heterogeneous Leader-Follower Approach

by El Houssein Chouaib Harik

Robotics 2023, 12(2), 57; https://doi.org/10.3390/robotics12020057 - 6 Apr 2023

Cited by 6 | Viewed by 2405

Abstract

In this paper, we investigated the idea of including mobile robots as complementary machinery to tractors in an agricultural context. The main idea is not to replace the human farmer, but to augment his/her capabilities by deploying mobile robots as assistants in field operations. The scheme is based on a leader–follower approach. The manned tractor is used as a leader, which will be taken as a reference point for a follower. The follower then takes the position of the leader as a target, and follows it in an autonomous manner. This will allow the farmer to multiply the working width by the number of mobile robots deployed during field operations. In this paper, we present a detailed description of the system, the theoretical aspect that allows the robot to autonomously follow the tractor, in addition to the different experimental steps that allowed us to test the system in the field to assess the robustness of the proposed scheme. Full article

(This article belongs to the Special Issue Robotics and AI for Precision Agriculture)

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14 pages, 5533 KiB

Open AccessArticle

Design and Prototyping of an Interchangeable and Underactuated Tool for Automatic Harvesting

by Giuseppe Quaglia, Luigi Tagliavini, Giovanni Colucci, Ardit Vorfi, Andrea Botta and Lorenzo Baglieri

Robotics 2022, 11(6), 145; https://doi.org/10.3390/robotics11060145 - 6 Dec 2022

Cited by 5 | Viewed by 2580

Abstract

In the field of precision agriculture, the automation of sampling and harvesting operations plays a central role to expand the possible application scenarios. Within this context, this work presents the design and prototyping of a novel underactuated tool for the harvesting of autonomous grapevines. The device is conceived to be one of several tools that could be automatically grasped by a robotic manipulator. As a use case, the presented tool is customized for the gripper of the robotic arm mounted on the rover Agri.Q, a service robot conceived for agriculture automation, but it can be easily adapted to other robotic arm grippers. In this work, first, the requirements for such a device are defined, then the functional design is presented, and a dimensionless analysis is performed to guide the dimensioning of the device. Later, the executive design is carried out, while the results of a preliminary experimental validation test are illustrated at the end of the paper. Full article

(This article belongs to the Special Issue Robotics and AI for Precision Agriculture)

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23 pages, 14528 KiB

Open AccessArticle

Edge AI-Based Tree Trunk Detection for Forestry Monitoring Robotics

by Daniel Queirós da Silva, Filipe Neves dos Santos, Vítor Filipe, Armando Jorge Sousa and Paulo Moura Oliveira

Robotics 2022, 11(6), 136; https://doi.org/10.3390/robotics11060136 - 27 Nov 2022

Cited by 10 | Viewed by 4197

Abstract

Object identification, such as tree trunk detection, is fundamental for forest robotics. Intelligent vision systems are of paramount importance in order to improve robotic perception, thus enhancing the autonomy of forest robots. To that purpose, this paper presents three contributions: an open dataset of 5325 annotated forest images; a tree trunk detection Edge AI benchmark between 13 deep learning models evaluated on four edge-devices (CPU, TPU, GPU and VPU); and a tree trunk mapping experiment using an OAK-D as a sensing device. The results showed that YOLOR was the most reliable trunk detector, achieving a maximum F1 score around 90% while maintaining high scores for different confidence levels; in terms of inference time, YOLOv4 Tiny was the fastest model, attaining 1.93 ms on the GPU. YOLOv7 Tiny presented the best trade-off between detection accuracy and speed, with average inference times under 4 ms on the GPU considering different input resolutions and at the same time achieving an F1 score similar to YOLOR. This work will enable the development of advanced artificial vision systems for robotics in forestry monitoring operations. Full article

(This article belongs to the Special Issue Robotics and AI for Precision Agriculture)

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Review

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60 pages, 28632 KiB

Open AccessReview

Sensing and Artificial Perception for Robots in Precision Forestry: A Survey

by João Filipe Ferreira, David Portugal, Maria Eduarda Andrada, Pedro Machado, Rui P. Rocha and Paulo Peixoto

Robotics 2023, 12(5), 139; https://doi.org/10.3390/robotics12050139 - 5 Oct 2023

Cited by 12 | Viewed by 4889

Abstract

Artificial perception for robots operating in outdoor natural environments, including forest scenarios, has been the object of a substantial amount of research for decades. Regardless, this has proven to be one of the most difficult research areas in robotics and has yet to be robustly solved. This happens namely due to difficulties in dealing with environmental conditions (trees and relief, weather conditions, dust, smoke, etc.), the visual homogeneity of natural landscapes as opposed to the diversity of natural obstacles to be avoided, and the effect of vibrations or external forces such as wind, among other technical challenges. Consequently, we propose a new survey, describing the current state of the art in artificial perception and sensing for robots in precision forestry. Our goal is to provide a detailed literature review of the past few decades of active research in this field. With this review, we attempted to provide valuable insights into the current scientific outlook and identify necessary advancements in the area. We have found that the introduction of robotics in precision forestry imposes very significant scientific and technological problems in artificial sensing and perception, making this a particularly challenging field with an impact on economics, society, technology, and standards. Based on this analysis, we put forward a roadmap to address the outstanding challenges in its respective scientific and technological landscape, namely the lack of training data for perception models, open software frameworks, robust solutions for multi-robot teams, end-user involvement, use case scenarios, computational resource planning, management solutions to satisfy real-time operation constraints, and systematic field testing. We argue that following this roadmap will allow for robotics in precision forestry to fulfil its considerable potential. Full article

(This article belongs to the Special Issue Robotics and AI for Precision Agriculture)

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37 pages, 27143 KiB

Open AccessReview

Watch the Next Step: A Comprehensive Survey of Stair-Climbing Vehicles

by Antonio Pappalettera, Francesco Bottiglione, Giacomo Mantriota and Giulio Reina

Robotics 2023, 12(3), 74; https://doi.org/10.3390/robotics12030074 - 18 May 2023

Cited by 5 | Viewed by 3409

Abstract

Stair climbing is one of the most challenging tasks for vehicles, especially when transporting people and heavy loads. Although many solutions have been proposed and demonstrated in practice, it is necessary to further improve their climbing ability and safety. This paper presents a systematic review of the scientific and engineering stair climbing literature, providing brief descriptions of the mechanism and method of operation and highlighting the advantages and disadvantages of different types of climbing platform. To quantitatively evaluate the system performance, various metrics are presented that consider allowable payload, maximum climbing speed, maximum crossable slope, transport ability and their combinations. Using these metrics, it is possible to compare vehicles with different locomotion modes and properties, allowing researchers and practitioners to gain in-depth knowledge of stair-climbing vehicles and choose the best category for transporting people and heavy loads up a flight of stairs. Full article

(This article belongs to the Special Issue Robotics and AI for Precision Agriculture)

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