Preliminary technology utilization assessment of the robotic fruit harvester

The results of an analysis whose purpose was to examine the history and progress of mechanical fruit harvesting, to determine the significance of a robotic fruit tree harvester and to assess the available market for such a product are summarized. Background information that can be used in determining the benefit of a proof of principle demonstration is provided. Such a demonstration could be a major step toward the transfer of this NASA technology

End Effector for Robotic Strawberry Picker Final Design Review

In this report, we have outlined the background of the problem and need for a solution to an automated form of strawberry harvesting. The report includes our research findings, defines the scope and objectives for this project, and documents our complete design process. Also included is our final, completed prototype, and a description of the manufacturing, design verification and testing process. Also included is our conclusions and recommendations for further improvement on future iterations

Concept studie autonoom oogsten van appels en peren

Bij de oogst van appels en peren is in een korte periode veel arbeid nodig. Het vraagt een forse inspanning om voldoende kwalitatief juiste arbeid te krijgen op het juiste moment tegen de juiste prijs, de kosten voor arbeid zijn hoog. Voor NFO en het PT hebben initiatief genomen om te komen tot robotisering van deze handeling te automatiseren. Doelstelling van dit project is één of meerdere concepten te ontwerpen voor het automatisch oogsten van appels en peren, waarbij kritische deelfuncties op eerste haalbaarheid zijn getoetst om de drempel te verlagen om een vervolg te starten. Aansluitend is het doel ontwikkelingstraject te starten dat binnen een afzienbare periode zal leiden tot implementatie. In het rapport zijn enkele perspectiefvolle concepten uitgewerkt. Verschillende deelfuncties zijn ontworpen en getest op haalbaarheid zoals: het sensorconcept globaal (waar zitten de appels); het sensorconcept lokaal (Is de appel voldoende rijp); manipulatie en end-effector en logistiek en buffering. Voor de verschillende functies zijn goede oplossingen voorhanden. De studie betreft met name een papieren studie. In een vervolg zal moeten blijken of het ook daadwerkelijk technisch haalbaar is. De focus in een vervolg dient te liggen bij de plukhandeling zelf en de daarbij horende sensoriek (de ogen, de arm en het handje). Het afvoeren van de geoogste appels, het bufferen en het transport door de boomgaard lijkt niet het probleem te zijn. Aan het einde heeft een herijking van de businesscase plaatsgevonden. De NFO/PT acht het project voldoende perspectiefvol om te starten met een initiatief voor verdere implementatie als vervolg op de conceptontwikkeling

Lessons learnt from field trials of a robotic sweet pepper harvester for protected cropping systems

In this paper, we present the lessons learnt during the development of a new robotic harvester (Harvey) that can autonomously harvest sweet pepper (capsicum) in protected cropping environments. Robotic harvesting offers an attractive potential solution to reducing labour costs while enabling more regular and selective harvesting, optimising crop quality, scheduling and therefore profit. Our approach combines effective vision algorithms with a novel end-effector design to enable successful harvesting of sweet peppers. We demonstrate a simple and effective vision-based algorithm for crop detection, a grasp selection method, and a novel end-effector design for harvesting. To reduce complexity of motion planning and to minimise occlusions we focus on picking sweet peppers in a protected cropping environment where plants are grown on planar trellis structures. Initial field trials in protected cropping environments, with two cultivars, demonstrate the efficacy of this approach. The results show that the robot harvester can successfully detect, grasp, and detach crop from the plant within a real protected cropping system. The novel contributions of this work have resulted in significant and encouraging improvements in sweet pepper picking success rates compared with the state-of-the-art. Future work will look at detecting sweet pepper peduncles and improving the total harvesting cycle time for each sweet pepper. The methods presented in this paper provide steps towards the goal of fully autonomous and reliable crop picking systems that will revolutionise the horticulture industry by reducing labour costs, maximising the quality of produce, and ultimately improving the sustainability of farming enterprises

Proof-of-concept modular robot platform for cauliflower harvesting

This paper presents a proof-of-concept platform for demonstrating robotic harvesting of summer-varieties of cauliflower, and early tests performed under laboratory conditions. The platform is designed to be modular and has two dexterous robotic arms with variable-stiffness technology. The bi-manual configuration enables the separation of grasping and cutting behaviours into separate robot manipulators. By exploiting the passive compliance of the variable-stiffness arms, the system can operate with both grasping and cutting tool close to the ground. Multiple 3D vision cameras are used to track the cauliflowers in real-time, and to attempt to assess the maturity. Early experiments with the platform in the laboratory highlight the potential and challenges of the platform

The Use of Agricultural Robots in Orchard Management

Book chapter that summarizes recent research on agricultural robotics in orchard management, including Robotic pruning, Robotic thinning, Robotic spraying, Robotic harvesting, Robotic fruit transportation, and future trends.Comment: 22 page

Mechanization in the cider apple orchard to reduce labor time

While fresh-eating, dessert apples dominate the apple market of Washington State, the over 30-fold national increase in volumetric cider production over the past 15 years indicates there is growing demand for specialty cider apples, or apple cultivars with positive cider-making attributes such as high polyphenol content and titratable acidity. In a commercial cider orchard, the labor costs associated with thinning, pruning, and harvest comprise a majority of the annual variable production costs once the orchard is in full production; thus, reducing the labor time to perform these tasks has the potential to increase the appeal and profitability of growing cider apples. Further, when cider apples are pressed into juice soon after harvest, cosmetic damage and bruising of the fruit during harvest has minimal impact on juice quality, indicating a strong potential for harvest mechanization. This study separately examines summer mechanical hedging for pruning and shaping orchard rows, as well as mechanized over-the-row harvest (using a modified Oxbo-Korvan 930 harvester) in a multi-cultivar cider apple orchard. Mechanical hedging both sides of the orchard row occurred at a rate of 2.1-3.5 seconds per meter across 5 years. Across 16 cultivars and 3 years, mechanical harvest occurred at a rate of 2.7-3.1 seconds per meter, and the average capture efficiency was 82% with no significant differences among cultivars. Based on these results, a combination of mechanical pruning and harvest could significantly lower labor time, and thus the total production costs, associated with growing cider apples

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