Autonomous Fruit Harvester with Machine Vision

This study presents an autonomous fruit harvester with a machine vision capable of detecting and picking or cutting an orange fruit from a tree. The system of is composed of a six-degrees of freedom (6-DOF) robotic arm mounted on a four-wheeled electric kart. The kart uses ZED stereo camera for depth estimation of a target. It can also be used to detect trees using the green detection algorithm. Image processing is done using Microsoft Visual Studio and OpenCV library. The x & y coordinates and distance of the tree are passed on to Arduino microcontroller as inputs to motor control of the wheels. When the kart is less than 65cm to the tree, the kart stops and the robotic arm system takes over to search and harvest orange fruits. The robotic arm has a webcam and ultrasonic sensor attached at its end-effector. The webcam is used for orange fruit detection while ultrasonic sensor is used to provide feedback on the distance of the orange fruit to end-effector. Multiple fruit harvesting is successfully done. The success rate of harvesting and putting fruit into the basket is 80% and 85% for the gripper end-effector and cutter end-effector respectively

Robotic Harvesting of Fruiting Vegetables: A Simulation Approach in V-REP, ROS and MATLAB

In modern agriculture, there is a high demand to move from tedious manual harvesting to a continuously automated operation. This chapter reports on designing a simulation and control platform in V-REP, ROS, and MATLAB for experimenting with sensors and manipulators in robotic harvesting of sweet pepper. The objective was to provide a completely simulated environment for improvement of visual servoing task through easy testing and debugging of control algorithms with zero damage risk to the real robot and to the actual equipment. A simulated workspace, including an exact replica of different robot manipulators, sensing mechanisms, and sweet pepper plant, and fruit system was created in V-REP. Image moment method visual servoing with eye-in-hand configuration was implemented in MATLAB, and was tested on four robotic platforms including Fanuc LR Mate 200iD, NOVABOT, multiple linear actuators, and multiple SCARA arms. Data from simulation experiments were used as inputs of the control algorithm in MATLAB, whose outputs were sent back to the simulated workspace and to the actual robots. ROS was used for exchanging data between the simulated environment and the real workspace via its publish-and-subscribe architecture. Results provided a framework for experimenting with different sensing and acting scenarios, and verified the performance functionality of the simulator

Design og styring av smarte robotsystemer for applikasjoner innen biovitenskap: biologisk prøvetaking og jordbærhøsting

This thesis aims to contribute knowledge to support fully automation in life-science applications, which includes design, development, control and integration of robotic systems for sample preparation and strawberry harvesting, and is divided into two parts. Part I shows the development of robotic systems for the preparation of fungal samples for Fourier transform infrared (FTIR) spectroscopy. The first step in this part developed a fully automated robot for homogenization of fungal samples using ultrasonication. The platform was constructed with a modified inexpensive 3D printer, equipped with a camera to distinguish sample wells and blank wells. Machine vision was also used to quantify the fungi homogenization process using model fitting, suggesting that homogeneity level to ultrasonication time can be well fitted with exponential decay equations. Moreover, a feedback control strategy was proposed that used the standard deviation of local homogeneity values to determine the ultrasonication termination time. The second step extended the first step to develop a fully automated robot for the whole process preparation of fungal samples for FTIR spectroscopy by adding a newly designed centrifuge and liquid-handling module for sample washing, concentration and spotting. The new system used machine vision with deep learning to identify the labware settings, which frees the users from inputting the labware information manually. Part II of the thesis deals with robotic strawberry harvesting. This part can be further divided into three stages. i) The first stage designed a novel cable-driven gripper with sensing capabilities, which has high tolerance to positional errors and can reduce picking time with a storage container. The gripper uses fingers to form a closed space that can open to capture a fruit and close to push the stem to the cutting area. Equipped with internal sensors, the gripper is able to control a robotic arm to correct for positional errors introduced by the vision system, improving the robustness. The gripper and a detection method based on color thresholding were integrated into a complete system for strawberry harvesting. ii) The second stage introduced the improvements and updates to the first stage where the main focus was to address the challenges in unstructured environment by introducing a light-adaptive color thresholding method for vision and a novel obstacle-separation algorithm for manipulation. At this stage, the new fully integrated strawberry-harvesting system with dual-manipulator was capable of picking strawberries continuously in polytunnels. The main scientific contribution of this stage is the novel obstacle-separation path-planning algorithm, which is fundamentally different from traditional path planning where obstacles are typically avoided. The algorithm uses the gripper to push aside surrounding obstacles from an entrance, thus clearing the way for it to swallow the target strawberry. Improvements were also made to the gripper, the arm, and the control. iii) The third stage improved the obstacle-separation method by introducing a zig-zag push for both horizontal and upward directions and a novel dragging operation to separate upper obstacles from the target. The zig-zag push can help the gripper capture a target since the generated shaking motion can break the static contact force between the target and obstacles. The dragging operation is able to address the issue of mis-capturing obstacles located above the target, in which the gripper drags the target to a place with fewer obstacles and then pushes back to move the obstacles aside for further detachment. The separation paths are determined by the number and distribution of obstacles based on the downsampled point cloud in the region of interest.Denne avhandlingen tar sikte på å bidra med kunnskap om automatisering og robotisering av applikasjoner innen livsvitenskap. Avhandlingen er todelt, og tar for seg design, utvikling, styring og integrering av robotsystemer for prøvetaking og jordbærhøsting. Del I omhandler utvikling av robotsystemer til bruk under forberedelse av sopprøver for Fourier-transform infrarød (FTIR) spektroskopi. I første stadium av denne delen ble det utviklet en helautomatisert robot for homogenisering av sopprøver ved bruk av ultralyd-sonikering. Plattformen ble konstruert ved å modifisere en billig 3D-printer og utstyre den med et kamera for å kunne skille prøvebrønner fra kontrollbrønner. Maskinsyn ble også tatt i bruk for å estimere soppens homogeniseringsprosess ved hjelp av matematisk modellering, noe som viste at homogenitetsnivået faller eksponensielt med tiden. Videre ble det foreslått en strategi for regulering i lukker sløyfe som brukte standardavviket for lokale homogenitetsverdier til å bestemme avslutningstidspunkt for sonikeringen. I neste stadium ble den første plattformen videreutviklet til en helautomatisert robot for hele prosessen som forbereder prøver av sopprøver for FTIR-spektroskopi. Dette ble gjort ved å legge til en nyutviklet sentrifuge- og væskehåndteringsmodul for vasking, konsentrering og spotting av prøver. Det nye systemet brukte maskinsyn med dyp læring for å identifisere innstillingene for laboratorieutstyr, noe som gjør at brukerne slipper å registrere innstillingene manuelt.Norwegian University of Life SciencespublishedVersio

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

Biologically inspired robotic perception-action for soft fruit harvesting in vertical growing environments

Multiple interlinked factors like demographics, migration patterns, and economics are presently leading to the critical shortage of labour available for low-skilled, physically demanding tasks like soft fruit harvesting. This paper presents a biomimetic robotic solution covering the full ‘Perception-Action’ loop targeting harvesting of strawberries in a state-of-the-art vertical growing environment. The novelty emerges from both dealing with crop/environment variance as well as configuring the robot action system to deal with a range of runtime task constraints. Unlike the commonly used deep neural networks, the proposed perception system uses conditional Generative Adversarial Networks to identify the ripe fruit using synthetic data. The network can effectively train the synthetic data using the image-to-image translation concept, thereby avoiding the tedious work of collecting and labelling the real dataset. Once the harvest-ready fruit is localised using point cloud data generated by a stereo camera, our platform’s action system can coordinate the arm to reach/cut the stem using the Passive Motion Paradigm framework inspired by studies on neural control of movement in the brain. Results from field trials for strawberry detection, reaching/cutting the stem of the fruit, and extension to analysing complex canopy structures/bimanual coordination (searching/picking) are presented. While this article focuses on strawberry harvesting, ongoing research towards adaptation of the architecture to other crops such as tomatoes and sweet peppers is briefly described

A field-tested robotic harvesting system for iceberg lettuce.

Agriculture provides an unique opportunity for the development of robotic systems; robots must be developed which can operate in harsh conditions and in highly uncertain and unknown environments. One particular challenge is performing manipulation for autonomous robotic harvesting. This paper describes recent and current work to automate the harvesting of iceberg lettuce. Unlike many other produce, iceberg is challenging to harvest as the crop is easily damaged by handling and is very hard to detect visually. A platform called Vegebot has been developed to enable the iterative development and field testing of the solution, which comprises of a vision system, custom end effector and software. To address the harvesting challenges posed by iceberg lettuce a bespoke vision and learning system has been developed which uses two integrated convolutional neural networks to achieve classification and localization. A custom end effector has been developed to allow damage free harvesting. To allow this end effector to achieve repeatable and consistent harvesting, a control method using force feedback allows detection of the ground. The system has been tested in the field, with experimental evidence gained which demonstrates the success of the vision system to localize and classify the lettuce, and the full integrated system to harvest lettuce. This study demonstrates how existing state-of-the art vision approaches can be applied to agricultural robotics, and mechanical systems can be developed which leverage the environmental constraints imposed in such environments

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

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

REVIEW OF ROBOTIC TECHNOLOGY FOR STRAWBERRY PRODUCTION

With an increasing world population in need of food and a limited amount of land for cultivation, higher efficiency in agricultural production, especially fruits and vegetables, is increasingly required. The success of agricultural production in the marketplace depends on its quality and cost. The cost of labor for crop production, harvesting, and post-harvesting operations is a major portion of the overall production cost, especially for specialty crops such as strawberry. As a result, a multitude of automation technologies involving semi-autonomous and autonomous robots have been utilized, with an aim of minimizing labor costs and operation time to achieve a considerable improvement in farming efficiency and economic performance. Research and technologies for weed control, harvesting, hauling, sorting, grading, and/or packing have been generally reviewed for fruits and vegetables, yet no review has been conducted thus far specifically for robotic technology being used in strawberry production. In this article, studies on strawberry robotics and their associated automation technologies are reviewed in terms of mechanical subsystems (e.g., traveling unit, handling unit, storage unit) and electronic subsystems (e.g., sensors, computer, communication, and control). Additionally, robotic technologies being used in different stages in strawberry production operations are reviewed. The robot designs for strawberry management are also categorized in terms of purpose and environment

ABC: Adaptive, Biomimetic, Configurable Robots for Smart Farms - From Cereal Phenotyping to Soft Fruit Harvesting

Currently, numerous factors, such as demographics, migration patterns, and economics, are leading to the critical labour shortage in low-skilled and physically demanding parts of agriculture. Thus, robotics can be developed for the agricultural sector to address these shortages. This study aims to develop an adaptive, biomimetic, and configurable modular robotics architecture that can be applied to multiple tasks (e.g., phenotyping, cutting, and picking), various crop varieties (e.g., wheat, strawberry, and tomato) and growing conditions. These robotic solutions cover the entire perception–action–decision-making loop targeting the phenotyping of cereals and harvesting fruits in a natural environment. The primary contributions of this thesis are as follows. a) A high-throughput method for imaging field-grown wheat in three dimensions, along with an accompanying unsupervised measuring method for obtaining individual wheat spike data are presented. The unsupervised method analyses the 3D point cloud of each trial plot, containing hundreds of wheat spikes, and calculates the average size of the wheat spike and total spike volume per plot. Experimental results reveal that the proposed algorithm can effectively identify spikes from wheat crops and individual spikes. b) Unlike cereal, soft fruit is typically harvested by manual selection and picking. To enable robotic harvesting, the initial perception system uses conditional generative adversarial networks to identify ripe fruits using synthetic data. To determine whether the strawberry is surrounded by obstacles, a cluster complexity-based perception system is further developed to classify the harvesting complexity of ripe strawberries. c) Once the harvest-ready fruit is localised using point cloud data generated by a stereo camera, the platform’s action system can coordinate the arm to reach/cut the stem using the passive motion paradigm framework, as inspired by studies on neural control of movement in the brain. Results from field trials for strawberry detection, reaching/cutting the stem of the fruit with a mean error of less than 3 mm, and extension to analysing complex canopy structures/bimanual coordination (searching/picking) are presented. Although this thesis focuses on strawberry harvesting, ongoing research is heading toward adapting the architecture to other crops. The agricultural food industry remains a labour-intensive sector with a low margin, and cost- and time-efficiency business model. The concepts presented herein can serve as a reference for future agricultural robots that are adaptive, biomimetic, and configurable