Automatic Romaine Heart Harvester

The Romaine Robotics Senior Design Team developed a romaine lettuce heart trimming system in partnership with a Salinas farm to address a growing labor shortage in the agricultural industry that is resulting in crops rotting in the field before they could be harvested. An automated trimmer can alleviate the most time consuming step in the cut-trim-bag harvesting process, increasing the yields of robotic cutters or the speed of existing laborer teams. Leveraging the Partner Farm’s existing trimmer architecture, which consists of a laborer loading lettuce into sprungloaded grippers that are rotated through vision and cutting systems by an indexer, the team redesigned geometry to improve the loading, gripping, and ejection stages of the system. Physical testing, hand calculations, and FEA were performed to understand acceptable grip strengths and cup design, and several wooden mockups were built to explore a new actuating linkage design for the indexer. The team manufactured, assembled, and performed verification testing on a full-size metal motorized prototype that can be incorporated with the Partner Farm’s existing cutting and vision systems. The prototype met all of the established requirements, and the farm has implemented the redesign onto their trimmer. Future work would include designing and implementing vision and cutting systems for the team’s metal prototype

Development of an intelligent self-learning product assembly system using visual identification

Thesis (Master of Engineering in Electrical Engineering) -- Central University of Technology, Free State, 2018Modern automation systems rely on fixed programming to carry out their production routines. These systems are effective for production outputs but do not allow any flexibility within the production routine. Effort is required to change the ongoing production routine through reprogramming, redesign or complete overhaul of the system to cater for new production outputs. These efforts require down time and result in a loss of revenue. If a completely automated flexible system is introduced into such a production line, the complete reprogramming process required to cater for new production needs could be automated without losing production time. Within this study, a real-time KUKA Robotic Control system is introduced. The KUKA Robotic Controller maintains its original programming methods with no reprogramming required when executing a new production assembly. This is achieved through manoeuvring the KUKA Robotic System in real-time to new destinations based on image-processing outputs and feedback. For demonstration purposes and proof of concept, the system learns a design presented to it by an end user and then reproduces this seen design based on the image-processing results in terms of location and orientation. Therefore, instead of reprogramming each new required position, the system takes over real-time control of the KUKA Robotic System and carries out the required steps autonomously. The benefit of such a system would be that the KUKA Robotic System would not require reprogramming to carry out new routines. It is controlled in a real-time environment to carry out new procedures based on external sensors (in this case, image-processing outputs). KUKA Robotic Sensor Interface (RSI) software is used to implement real-time control of the KUKA Robotic System and is explored extensively throughout this study

Development Of An Intelligent Self-Learning Product Assembly System Using Visual Identification

Modern automation systems rely on fixed programming to carry out their production routines. These systems are effective for production outputs but do not allow any flexibility within the production routine. Effort is required to change the ongoing production routine through reprogramming, redesign or complete overhaul of the system to cater for new production outputs. These efforts require down time and result in a loss of revenue. If a completely automated flexible system is introduced into such a production line, the complete reprogramming process required to cater for new production needs could be automated without losing production time. Within this study, a real-time KUKA Robotic Control system is introduced. The KUKA Robotic Controller maintains its original programming methods with no reprogramming required when executing a new production assembly. This is achieved through manoeuvring the KUKA Robotic System in real-time to new destinations based on image-processing outputs and feedback. For demonstration purposes and proof of concept, the system learns a design presented to it by an end user and then reproduces this seen design based on the image-processing results in terms of location and orientation. Therefore, instead of reprogramming each new required position, the system takes over real-time control of the KUKA Robotic System and carries out the required steps autonomously. The benefit of such a system would be that the KUKA Robotic System would not require reprogramming to carry out new routines. It is controlled in a real-time environment to carry out new procedures based on external sensors (in this case, image-processing outputs). KUKA Robotic Sensor Interface (RSI) software is used to implement real-time control of the KUKA Robotic System and is explored extensively throughout this study

THE METHODOLOGY FOR INTEGRATING ROBOTIC SYSTEMS IN UNDEGROUND MINING MACHINES

Roof bolting is a critical operation in ensuring the safety and stability of underground mines by securing the roof strata with bolts. The process involves moving and manipulating heavy tools while being vigilant about the safety of the area. During the installation of roof bolts, operators are exposed to hazardous conditions due to challenging working conditions in underground mines, extensive working hours, and demanding shift schedules leading to personnel fatigue and influencing operators to take shortcuts that may increase the risk of injuries and fatal accidents. The successful completion of roof bolting tasks depends heavily on operator judgment and experience to perform these tasks. To mitigate the occupational hazards inherent in roof bolting operations, a six-axis ABB IRB 1600 robotic arm was integrated into the roof bolter machine to imitate human functions during the roof bolting operation. The integration process involves selecting a suitable robot that can perform human activities and has the potential to handle the tasks at hand. The ultimate goal of implementing the robotic system into the roof bolter machine is to minimize human involvement during the roof bolting operation by converting the machine from manual operations to a partially automated roof bolter machine. The integration enhances the safety of personnel by moving humans away from the face where roof bolting takes place to a safe distance. The operator is then assigned a new role to control and supervise all the operational tasks of the automated roof bolting operation via a human-machine interface (HMI). During the laboratory testing of the automation process, the robotic arm cooperates with some novel specialized technologies to imitate human activities during roof bolting operations. The developed systems include the plate feeder, the bolt feeder, and the wrench. These systems were built to support automation and minimize human intervention during roof bolting operations. These components were linked to the Programmable Logic Controller (PLC) and controlled by the HMI touchpad. An HMI was developed for the operator to control and monitor the automated process away from the active face. This study establishes robust communication paths among all the components. The design communication network links the robotic arm and other components of the roof bolter machine, leading to a smooth and sequential roof bolting process. The EtherNet/IP protocol is used to pass messages between the components of the automated roof bolter machine through a Controller Area Network (CAN) bus device installed to enable communication using CAN protocols. Establishing a robust communication network between the components prevents collision and manages the movement of the robotic arm and other developed automated systems during the bolting process. The outcome of the study shows that the robotic arm has the potential to mimic human activities during the roof bolting operation by performing bolt grasping, holding, lifting, placing, and removal of drill steels during the roof bolting operations. As a result, humans can be moved away from hazardous areas to a safe location and control the roof bolting operation through an Human Machine Interface (HMI) touchpad. The HMI controls the bolting process with start and stop buttons from the subroutine of all the components to perform the roof bolting operation. These buttons enable the operator to stop the operation in the event of unsafe acts

Robotic Technologies for High-Throughput Plant Phenotyping: Contemporary Reviews and Future Perspectives

Phenotyping plants is an essential component of any effort to develop new crop varieties. As plant breeders seek to increase crop productivity and produce more food for the future, the amount of phenotype information they require will also increase. Traditional plant phenotyping relying on manual measurement is laborious, time-consuming, error-prone, and costly. Plant phenotyping robots have emerged as a high-throughput technology to measure morphological, chemical and physiological properties of large number of plants. Several robotic systems have been developed to fulfill different phenotyping missions. In particular, robotic phenotyping has the potential to enable efficient monitoring of changes in plant traits over time in both controlled environments and in the field. The operation of these robots can be challenging as a result of the dynamic nature of plants and the agricultural environments. Here we discuss developments in phenotyping robots, and the challenges which have been overcome and others which remain outstanding. In addition, some perspective applications of the phenotyping robots are also presented. We optimistically anticipate that autonomous and robotic systems will make great leaps forward in the next 10 years to advance the plant phenotyping research into a new era

Desenvolvimento de um veículo inovador para armazéns automáticos

Intralogistics is increasingly a matter of research and development as a form of optimization, automation, integration and management of the flow of materials and information that circulate within a business unit. With a strong connection to material handling equipment and automation solutions, intralogistics has proved to be one of the main factors responsible for something that is already happening: a fourth industrial revolution where it is possible to convert warehouses and manufacturing units into intelligent environments where the entire process can be controlled and supervised through a single system. It became necessary to develop more and more innovative and efficient solutions to the constant diversity of challenges proposed by the market. In this sense, it was proposed to develop something innovative within the area of Automated Storage and Retrieval Systems (AS/RS), a technology increasingly sought after by today's manufacturing plants. As such, the goal was to improve the most emergent AS/RS in recent years: the Pallet/Box Shuttle AS/RS. In order to achieve the proposed objective, it was necessary to analyze all the existing solutions in the market and, principally, to find the main points to be improved and the direction to follow in order to innovate an already advanced solution. The results show a robotized solution where it was possible to increase the automation of the operations in the storage systems and improve the responsiveness of the system, taking this solution to a new level.A intralogística é cada vez mais uma área de investigação e desenvolvimento como uma forma de otimização, automação, integração e gestão do fluxo de materiais e informações que circulam dentro de uma unidade de negócios. Com uma forte ligação com equipamentos de manipulação de materiais e soluções de automação, a intralogística provou ser um dos principais fatores responsáveis por algo que já está a acontecer: uma quarta revolução industrial, onde é possível converter armazéns e unidades fabris em ambientes inteligentes, onde todo o processo pode ser controlado e supervisionado através de um único sistema. Tornou-se necessário desenvolver soluções cada vez mais inovadoras e eficientes para a constante diversidade de desafios propostos pelo mercado. Nesse sentido, propôs-se desenvolver algo inovador dentro da área dos Armazéns Automáticos, uma solução cada vez mais procurada pelas unidades fabris de hoje. Como tal, estabeleceu-se o objetivo de melhorar o tipo de Armazém Automático mais emergente dos últimos anos: o Armazém Automático com Veículos Satélite para Caixas ou Paletes. Para alcançar o objetivo proposto, foi necessário analisar todas as soluções existentes no mercado e, principalmente, encontrar os principais pontos a serem aprimorados e definir a direção a seguir para se inovar uma solução já avançada. Os resultados obtidos apresentam uma solução robotizada onde foi possível aumentar a automatização das operações dos sistemas de armazenamento e melhorar a capacidade de resposta do sistema, levando esta solução para um novo patamar

A ROS-based software architecture for a versatile collaborative dual-armed autonomous mobile robot for the manufacturing industry

The industrial context is changing rapidly due to advancements in technology fueled by the Internet and Information Technology. The fourth industrial revolution counts integration, flexibility, and optimization as its fundamental pillars, and, in this context, Human-Robot Collaboration has become a crucial factor for manufacturing sustainability in Europe. Collaborative robots are appealing to many companies due to their low installation and running costs and high degree of flexibility, making them ideal for reshoring production facilities with a short return on investment. The ROSSINI European project aims to implement a true Human-Robot Collaboration by designing, developing, and demonstrating a modular and scalable platform for integrating human-centred robotic technologies in industrial production environments. The project focuses on safety concerns related to introducing a cobot in a shared working area and aims to lay the groundwork for a new working paradigm at the industrial level. The need for a software architecture suitable to the robotic platform employed in one of three use cases selected to deploy and test the new technology was the main trigger of this Thesis. The chosen application consists of the automatic loading and unloading of raw-material reels to an automatic packaging machine through an Autonomous Mobile Robot composed of an Autonomous Guided Vehicle, two collaborative manipulators, and an eye-on-hand vision system for performing tasks in a partially unstructured environment. The results obtained during the ROSSINI use case development were later used in the SENECA project, which addresses the need for robot-driven automatic cleaning of pharmaceutical bins in a very specific industrial context. The inherent versatility of mobile collaborative robots is evident from their deployment in the two projects with few hardware and software adjustments. The positive impact of Human-Robot Collaboration on diverse production lines is a motivation for future investments in research on this increasingly popular field by the industry

Autonomous dishwasher loading from cluttered trays using pre-trained deep neural networks

Abstract: Autonomous dishwasher loading is a benchmark problem in robotics that highlights the challenges of robotic perception, planning, and manipulation in an unstructured environment. Current approaches resort to a specialized solution, however, these technologies are not viable in a domestic setting. Learning‐based solutions seem promising for a general purpose solutions; however, they require large amounts of catered data to be applied in real‐world scenarios. This article presents a novel learning‐based solution without a training phase using pre‐trained object detection networks. By developing a perception, planning, and manipulation framework around an off‐the‐shelf object detection network, we are able to develop robust pick‐and‐place solutions that are easy to develop and general purpose requiring only a RGB feedback and a pinch gripper. Analysis of a real‐world canteen tray data is first performed and used for developing our in‐lab experimental setup. Our results obtained from real‐world scenarios indicate that such approaches are highly desirable for plug‐and‐play domestic applications with limited calibration. All the associated data and code of this work are shared in a public repository