Actuators and sensors for application in agricultural robots: A review

In recent years, with the rapid development of science and technology, agricultural robots have gradually begun to replace humans, to complete various agricultural operations, changing traditional agricultural production methods. Not only is the labor input reduced, but also the production efficiency can be improved, which invariably contributes to the development of smart agriculture. This paper reviews the core technologies used for agricultural robots in non-structural environments. In addition, we review the technological progress of drive systems, control strategies, end-effectors, robotic arms, environmental perception, and other related systems. This research shows that in a non-structured agricultural environment, using cameras and light detection and ranging (LiDAR), as well as ultrasonic and satellite navigation equipment, and by integrating sensing, transmission, control, and operation, different types of actuators can be innovatively designed and developed to drive the advance of agricultural robots, to meet the delicate and complex requirements of agricultural products as operational objects, such that better productivity and standardization of agriculture can be achieved. In summary, agricultural production is developing toward a data-driven, standardized, and unmanned approach, with smart agriculture supported by actuator-driven-based agricultural robots. This paper concludes with a summary of the main existing technologies and challenges in the development of actuators for applications in agricultural robots, and the outlook regarding the primary development directions of agricultural robots in the near future

Towards autonomous mapping in agriculture: A review of supportive technologies for ground robotics

This paper surveys the supportive technologies currently available for ground mobile robots used for autonomous mapping in agriculture. Unlike previous reviews, we describe state-of-the-art approaches and technologies aimed at extracting information from agricultural environments, not only for navigation purposes but especially for mapping and monitoring. The state-of-the-art platforms and sensors, the modern localization techniques, the navigation and path planning approaches, as well as the potentialities of artificial intelligence towards autonomous mapping in agriculture are analyzed. According to the findings of this review, many examples of recent mobile robots provide full navigation and autonomous mapping capability. Significant resources are currently devoted to this research area, in order to further improve mobile robot capabilities in this complex and challenging field

Localization, Navigation and Activity Planning for Wheeled Agricultural Robots – A Survey

Source at:https://fruct.org/publications/volume-32/fruct32/High cost, time intensive work, labor shortages and inefficient strategies have raised the need of employing mobile robotics to fully automate agricultural tasks and fulfil the requirements of precision agriculture. In order to perform an agricultural task, the mobile robot goes through a sequence of sub operations and integration of hardware and software systems. Starting with localization, an agricultural robot uses sensor systems to estimate its current position and orientation in field, employs algorithms to find optimal paths and reach target positions. It then uses techniques and models to perform feature recognition and finally executes the agricultural task through an end effector. This article, compiled through scrutinizing the current literature, is a step-by-step approach of the strategies and ways these sub-operations are performed and integrated together. An analysis has also been done on the limitations in each sub operation, available solutions, and the ongoing research focus

Robotic Crop Interaction in Agriculture for Soft Fruit Harvesting

Autonomous tree crop harvesting has been a seemingly attainable, but elusive, robotics goal for the past several decades. Limiting grower reliance on uncertain seasonal labour is an economic driver of this, but the ability of robotic systems to treat each plant individually also has environmental benefits, such as reduced emissions and fertiliser use. Over the same time period, effective grasping and manipulation (G&M) solutions to warehouse product handling, and more general robotic interaction, have been demonstrated. Despite research progress in general robotic interaction and harvesting of some specific crop types, a commercially successful robotic harvester has yet to be demonstrated. Most crop varieties, including soft-skinned fruit, have not yet been addressed. Soft fruit, such as plums, present problems for many of the techniques employed for their more robust relatives and require special focus when developing autonomous harvesters. Adapting existing robotics tools and techniques to new fruit types, including soft skinned varieties, is not well explored. This thesis aims to bridge that gap by examining the challenges of autonomous crop interaction for the harvesting of soft fruit. Aspects which are known to be challenging include mixed obstacle planning with both hard and soft obstacles present, poor outdoor sensing conditions, and the lack of proven picking motion strategies. Positioning an actuator for harvesting requires solving these problems and others specific to soft skinned fruit. Doing so effectively means addressing these in the sensing, planning and actuation areas of a robotic system. Such areas are also highly interdependent for grasping and manipulation tasks, so solutions need to be developed at the system level. In this thesis, soft robotics actuators, with simplifying assumptions about hard obstacle planes, are used to solve mixed obstacle planning. Persistent target tracking and filtering is used to overcome challenging object detection conditions, while multiple stages of object detection are applied to refine these initial position estimates. Several picking motions are developed and tested for plums, with varying degrees of effectiveness. These various techniques are integrated into a prototype system which is validated in lab testing and extensive field trials on a commercial plum crop. Key contributions of this thesis include I. The examination of grasping & manipulation tools, algorithms, techniques and challenges for harvesting soft skinned fruit II. Design, development and field-trial evaluation of a harvester prototype to validate these concepts in practice, with specific design studies of the gripper type, object detector architecture and picking motion for this III. Investigation of specific G&M module improvements including: o Application of the autocovariance least squares (ALS) method to noise covariance matrix estimation for visual servoing tasks, where both simulated and real experiments demonstrated a 30% improvement in state estimation error using this technique. o Theory and experimentation showing that a single range measurement is sufficient for disambiguating scene scale in monocular depth estimation for some datasets. o Preliminary investigations of stochastic object completion and sampling for grasping, active perception for visual servoing based harvesting, and multi-stage fruit localisation from RGB-Depth data. Several field trials were carried out with the plum harvesting prototype. Testing on an unmodified commercial plum crop, in all weather conditions, showed promising results with a harvest success rate of 42%. While a significant gap between prototype performance and commercial viability remains, the use of soft robotics with carefully chosen sensing and planning approaches allows for robust grasping & manipulation under challenging conditions, with both hard and soft obstacles

Augmented Perception for Agricultural Robots Navigation

[EN] Producing food in a sustainable way is becoming very challenging today due to the lack of skilled labor, the unaffordable costs of labor when available, and the limited returns for growers as a result of low produce prices demanded by big supermarket chains in contrast to ever-increasing costs of inputs such as fuel, chemicals, seeds, or water. Robotics emerges as a technological advance that can counterweight some of these challenges, mainly in industrialized countries. However, the deployment of autonomous machines in open environments exposed to uncertainty and harsh ambient conditions poses an important defiance to reliability and safety. Consequently, a deep parametrization of the working environment in real time is necessary to achieve autonomous navigation. This article proposes a navigation strategy for guiding a robot along vineyard rows for field monitoring. Given that global positioning cannot be granted permanently in any vineyard, the strategy is based on local perception, and results from fusing three complementary technologies: 3D vision, lidar, and ultrasonics. Several perception-based navigation algorithms were developed between 2015 and 2019. After their comparison in real environments and conditions, results showed that the augmented perception derived from combining these three technologies provides a consistent basis for outlining the intelligent behavior of agricultural robots operating within orchards.This work was supported by the European Union Research and Innovation Programs under Grant N. 737669 and Grant N. 610953. The associate editor coordinating the review of this article and approving it for publication was Dr. Oleg Sergiyenko.Rovira Más, F.; Sáiz Rubio, V.; Cuenca-Cuenca, A. (2021). Augmented Perception for Agricultural Robots Navigation. IEEE Sensors Journal. 21(10):11712-11727. https://doi.org/10.1109/JSEN.2020.3016081S1171211727211

Integrasjon av et minimalistisk sett av sensorer for kartlegging og lokalisering av landbruksroboter

Robots have recently become ubiquitous in many aspects of daily life. For in-house applications there is vacuuming, mopping and lawn-mowing robots. Swarms of robots have been used in Amazon warehouses for several years. Autonomous driving cars, despite being set back by several safety issues, are undeniably becoming the standard of the automobile industry. Not just being useful for commercial applications, robots can perform various tasks, such as inspecting hazardous sites, taking part in search-and-rescue missions. Regardless of end-user applications, autonomy plays a crucial role in modern robots. The essential capabilities required for autonomous operations are mapping, localization and navigation. The goal of this thesis is to develop a new approach to solve the problems of mapping, localization, and navigation for autonomous robots in agriculture. This type of environment poses some unique challenges such as repetitive patterns, large-scale sparse features environments, in comparison to other scenarios such as urban/cities, where the abundance of good features such as pavements, buildings, road lanes, traffic signs, etc., exists. In outdoor agricultural environments, a robot can rely on a Global Navigation Satellite System (GNSS) to determine its whereabouts. It is often limited to the robot's activities to accessible GNSS signal areas. It would fail for indoor environments. In this case, different types of exteroceptive sensors such as (RGB, Depth, Thermal) cameras, laser scanner, Light Detection and Ranging (LiDAR) and proprioceptive sensors such as Inertial Measurement Unit (IMU), wheel-encoders can be fused to better estimate the robot's states. Generic approaches of combining several different sensors often yield superior estimation results but they are not always optimal in terms of cost-effectiveness, high modularity, reusability, and interchangeability. For agricultural robots, it is equally important for being robust for long term operations as well as being cost-effective for mass production. We tackle this challenge by exploring and selectively using a handful of sensors such as RGB-D cameras, LiDAR and IMU for representative agricultural environments. The sensor fusion algorithms provide high precision and robustness for mapping and localization while at the same time assuring cost-effectiveness by employing only the necessary sensors for a task at hand. In this thesis, we extend the LiDAR mapping and localization methods for normal urban/city scenarios to cope with the agricultural environments where the presence of slopes, vegetation, trees render the traditional approaches to fail. Our mapping method substantially reduces the memory footprint for map storing, which is important for large-scale farms. We show how to handle the localization problem in dynamic growing strawberry polytunnels by using only a stereo visual-inertial (VI) and depth sensor to extract and track only invariant features. This eliminates the need for remapping to deal with dynamic scenes. Also, for a demonstration of the minimalistic requirement for autonomous agricultural robots, we show the ability to autonomously traverse between rows in a difficult environment of zigzag-liked polytunnel using only a laser scanner. Furthermore, we present an autonomous navigation capability by using only a camera without explicitly performing mapping or localization. Finally, our mapping and localization methods are generic and platform-agnostic, which can be applied to different types of agricultural robots. All contributions presented in this thesis have been tested and validated on real robots in real agricultural environments. All approaches have been published or submitted in peer-reviewed conference papers and journal articles.Roboter har nylig blitt standard i mange deler av hverdagen. I hjemmet har vi støvsuger-, vaske- og gressklippende roboter. Svermer med roboter har blitt brukt av Amazons varehus i mange år. Autonome selvkjørende biler, til tross for å ha vært satt tilbake av sikkerhetshensyn, er udiskutabelt på vei til å bli standarden innen bilbransjen. Roboter har mer nytte enn rent kommersielt bruk. Roboter kan utføre forskjellige oppgaver, som å inspisere farlige områder og delta i leteoppdrag. Uansett hva sluttbrukeren velger å gjøre, spiller autonomi en viktig rolle i moderne roboter. De essensielle egenskapene for autonome operasjoner i landbruket er kartlegging, lokalisering og navigering. Denne type miljø gir spesielle utfordringer som repetitive mønstre og storskala miljø med få landskapsdetaljer, sammenlignet med andre steder, som urbane-/bymiljø, hvor det finnes mange landskapsdetaljer som fortau, bygninger, trafikkfelt, trafikkskilt, etc. I utendørs jordbruksmiljø kan en robot bruke Global Navigation Satellite System (GNSS) til å navigere sine omgivelser. Dette begrenser robotens aktiviteter til områder med tilgjengelig GNSS signaler. Dette vil ikke fungere i miljøer innendørs. I ett slikt tilfelle vil reseptorer mot det eksterne miljø som (RGB-, dybde-, temperatur-) kameraer, laserskannere, «Light detection and Ranging» (LiDAR) og propriopsjonære detektorer som treghetssensorer (IMU) og hjulenkodere kunne brukes sammen for å bedre kunne estimere robotens tilstand. Generisk kombinering av forskjellige sensorer fører til overlegne estimeringsresultater, men er ofte suboptimale med hensyn på kostnadseffektivitet, moduleringingsgrad og utbyttbarhet. For landbruksroboter så er det like viktig med robusthet for lang tids bruk som kostnadseffektivitet for masseproduksjon. Vi taklet denne utfordringen med å utforske og selektivt velge en håndfull sensorer som RGB-D kameraer, LiDAR og IMU for representative landbruksmiljø. Algoritmen som kombinerer sensorsignalene gir en høy presisjonsgrad og robusthet for kartlegging og lokalisering, og gir samtidig kostnadseffektivitet med å bare bruke de nødvendige sensorene for oppgaven som skal utføres. I denne avhandlingen utvider vi en LiDAR kartlegging og lokaliseringsmetode normalt brukt i urbane/bymiljø til å takle landbruksmiljø, hvor hellinger, vegetasjon og trær gjør at tradisjonelle metoder mislykkes. Vår metode reduserer signifikant lagringsbehovet for kartlagring, noe som er viktig for storskala gårder. Vi viser hvordan lokaliseringsproblemet i dynamisk voksende jordbær-polytuneller kan løses ved å bruke en stereo visuel inertiel (VI) og en dybdesensor for å ekstrahere statiske objekter. Dette eliminerer behovet å kartlegge på nytt for å klare dynamiske scener. I tillegg demonstrerer vi de minimalistiske kravene for autonome jordbruksroboter. Vi viser robotens evne til å bevege seg autonomt mellom rader i ett vanskelig miljø med polytuneller i sikksakk-mønstre ved bruk av kun en laserskanner. Videre presenterer vi en autonom navigeringsevne ved bruk av kun ett kamera uten å eksplisitt kartlegge eller lokalisere. Til slutt viser vi at kartleggings- og lokaliseringsmetodene er generiske og platform-agnostiske, noe som kan brukes med flere typer jordbruksroboter. Alle bidrag presentert i denne avhandlingen har blitt testet og validert med ekte roboter i ekte landbruksmiljø. Alle forsøk har blitt publisert eller sendt til fagfellevurderte konferansepapirer og journalartikler

Flexible system of multiple RGB-D sensors for measuring and classifying fruits in agri-food Industry

The productivity of the agri-food sector experiences continuous and growing challenges that make the use of innovative technologies to maintain and even improve their competitiveness a priority. In this context, this paper presents the foundations and validation of a flexible and portable system capable of obtaining 3D measurements and classifying objects based on color and depth images taken from multiple Kinect v1 sensors. The developed system is applied to the selection and classification of fruits, a common activity in the agri-food industry. Being able to obtain complete and accurate information of the environment, as it integrates the depth information obtained from multiple sensors, this system is capable of self-location and self-calibration of the sensors to then start detecting, classifying and measuring fruits in real time. Unlike other systems that use specific set-up or need a previous calibration, it does not require a predetermined positioning of the sensors, so that it can be adapted to different scenarios. The characterization process considers: classification of fruits, estimation of its volume and the number of assets per each kind of fruit. A requirement for the system is that each sensor must partially share its field of view with at least another sensor. The sensors localize themselves by estimating the rotation and translation matrices that allow to transform the coordinate system of one sensor to the other. To achieve this, Iterative Closest Point (ICP) algorithm is used and subsequently validated with a 6 degree of freedom KUKA robotic arm. Also, a method is implemented to estimate the movement of objects based on the Kalman Filter. A relevant contribution of this work is the detailed analysis and propagation of the errors that affect both the proposed methods and hardware. To determine the performance of the proposed system the passage of different types of fruits on a conveyor belt is emulated by a mobile robot carrying a surface where the fruits were placed. Both the perimeter and volume are measured and classified according to the type of fruit. The system was able to distinguish and classify the 95% of fruits and to estimate their volume with a 85% of accuracy in worst cases (fruits whose shape is not symmetrical) and 94% of accuracy in best cases (fruits whose shape is more symmetrical), showing that the proposed approach can become a useful tool in the agri-food industry.This project has been supported by the National Commission for Science and Technology Research of Chile (Conicyt) under FONDECYT grant 1140575 and the Advanced Center of Electrical and Electronic Engineering - AC3E (CONICYT/FB0008)

Machine Learning Feature Extraction Based on Binary Pixel Quantification Using Low-Resolution Images for Application of Unmanned Ground Vehicles in Apple Orchards

Deep learning and machine learning (ML) technologies have been implemented in various applications, and various agriculture technologies are being developed based on image-based object recognition technology. We propose an orchard environment free space recognition technology suitable for developing small-scale agricultural unmanned ground vehicle (UGV) autonomous mobile equipment using a low-cost lightweight processor. We designed an algorithm to minimize the amount of input data to be processed by the ML algorithm through low-resolution grayscale images and image binarization. In addition, we propose an ML feature extraction method based on binary pixel quantification that can be applied to an ML classifier to detect free space for autonomous movement of UGVs from binary images. Here, the ML feature is extracted by detecting the local-lowest points in segments of a binarized image and by defining 33 variables, including local-lowest points, to detect the bottom of a tree trunk. We trained six ML models to select a suitable ML model for trunk bottom detection among various ML models, and we analyzed and compared the performance of the trained models. The ensemble model demonstrated the best performance, and a test was performed using this ML model to detect apple tree trunks from 100 new images. Experimental results indicate that it is possible to recognize free space in an apple orchard environment by learning using approximately 100 low-resolution grayscale images. © 2020 by the authors.1

Lidar-based Obstacle Detection and Recognition for Autonomous Agricultural Vehicles

Today, agricultural vehicles are available that can drive autonomously and follow exact route plans more precisely than human operators. Combined with advancements in precision agriculture, autonomous agricultural robots can reduce manual labor, improve workflow, and optimize yield. However, as of today, human operators are still required for monitoring the environment and acting upon potential obstacles in front of the vehicle. To eliminate this need, safety must be ensured by accurate and reliable obstacle detection and avoidance systems.In this thesis, lidar-based obstacle detection and recognition in agricultural environments has been investigated. A rotating multi-beam lidar generating 3D point clouds was used for point-wise classification of agricultural scenes, while multi-modal fusion with cameras and radar was used to increase performance and robustness. Two research perception platforms were presented and used for data acquisition. The proposed methods were all evaluated on recorded datasets that represented a wide range of realistic agricultural environments and included both static and dynamic obstacles.For 3D point cloud classification, two methods were proposed for handling density variations during feature extraction. One method outperformed a frequently used generic 3D feature descriptor, whereas the other method showed promising preliminary results using deep learning on 2D range images. For multi-modal fusion, four methods were proposed for combining lidar with color camera, thermal camera, and radar. Gradual improvements in classification accuracy were seen, as spatial, temporal, and multi-modal relationships were introduced in the models. Finally, occupancy grid mapping was used to fuse and map detections globally, and runtime obstacle detection was applied on mapped detections along the vehicle path, thus simulating an actual traversal.The proposed methods serve as a first step towards full autonomy for agricultural vehicles. The study has thus shown that recent advancements in autonomous driving can be transferred to the agricultural domain, when accurate distinctions are made between obstacles and processable vegetation. Future research in the domain has further been facilitated with the release of the multi-modal obstacle dataset, FieldSAFE

Autonomous Power Line Inspection with Drones via Perception-Aware MPC

Drones have the potential to revolutionize power line inspection by increasing productivity, reducing inspection time, improving data quality, and eliminating the risks for human operators. Current state-of-the-art systems for power line inspection have two shortcomings: (i) control is decoupled from perception and needs accurate information about the location of the power lines and masts; (ii) obstacle avoidance is decoupled from the power line tracking, which results in poor tracking in the vicinity of the power masts, and, consequently, in decreased data quality for visual inspection. In this work, we propose a model predictive controller (MPC) that overcomes these limitations by tightly coupling perception and action. Our controller generates commands that maximize the visibility of the power lines while, at the same time, safely avoiding the power masts. For power line detection, we propose a lightweight learning-based detector that is trained only on synthetic data and is able to transfer zero-shot to real-world power line images. We validate our system in simulation and real-world experiments on a mock-up power line infrastructure. We release our code and datasets to the public