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Comparison of semantic segmentation, classification and localization, object detection and instance segmentation (Li, Johnson and Yeung, 2017)
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Purpose
This research paper aims to create an environment which enables robots to learn from hu-mans by algorithms of Computer Vision and Machine Learning for object detection and gripping. The proposed concept transforms manual picking to highly automated picking performed by robots.
Methodology
After defining requirements for a robotic picking s...
Contexts in source publication
Context 1
... Segmentation, classification, localization and instance segmentation are other jobs working on images besides object detection. These tasks of Computer Vision are shown in Figure 1 outlining the differences of these. ...
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Recent advancements have led to a proliferation of machine learning systems used to assist humans in a wide range of tasks. However, we are still far from accurate, reliable, and resource-efficient operations of these systems. For robot perception, convolutional neural networks (CNNs) for object detection and pose estimation are recently coming int...
In the production process from green beans to coffee bean packages, the defective bean removal (or in short, defect removal) is one of most labor-consuming stages, and many companies investigate the automation of this stage for minimizing human efforts. In this paper, we propose a deep-learning-based defective bean inspection scheme (DL-DBIS), toge...
Citations
... One of the main tasks of robots is to pick objects in dynamic environments since the shape and position of the stored object can often change. Different cameras, algorithms, equipment, and lighting must be considered, complicating the task of training robots in the picking process (Rieder & Verbeet, 2019). During the picking process, it is necessary to determine the location of the object, which is possible by using recognition algorithms that process sensor or camera data (Thamer et al., 2018). ...
Intralogistics have made remarkable changes within its evolution. Still, the intralogistics picking process is considerable manual-intensive work with a high potential to automation. Industry 4.0 connotes artificial intelligent robot technologies which are used in the picking process. Two of these technologies are autonomous mobile robots and collaborative robots, which are emerging robot technologies. Although these robots are stand-alone technologies, they can be combined as one robot: an autonomous mobile cobot. This report aims to investigate the autonomous mobile cobot, what was the development behind it, how does it work, how it can be used in the intralogistics picking process, what are the main impact and benefits of combining the robots together, and what are the cost-effectiveness, return on investments, and risks that are implications of the new robot technology in the picking process. The report is done as a non-systematic literature review collecting data from research journals, online libraries, the internet, blogposts, and by interviewing industry experts and visiting a robot manufacturer to acquire knowledge on robots in the picking process. The study revealed that autonomous mobile cobots are still a new technology in the intralogistics picking process, and only a few of the robot manufacturers already produce these. Further, there is a lack of research done on autonomous mobile cobots and their effects on the picking process. The effects are gathered into the report based on research done on autonomous mobile robots and collaborative robots. Furthermore, autonomous mobile cobots are widely used in multiple industries and sectors, like in the military, agriculture, healthcare, and construction. These use cases can be looked at to benchmark the picking process. Given the demand for automation in the picking process and the benefits of autonomous mobile cobots, more research is required to support business decision-makers and make reliable investment decisions on implementing the robots in the picking process.
... Only few approaches consider how to transform order picking from manual handling to partwise or complete automation without redesigning the whole logistics infrastructure. Rieder and Verbeet [6] proposed a concept to introduce robots into a traditional rack picking environment and raise the percentage of robot picking by human-robot collaboration stepwise. To prove the approach's suitability for operational use a demonstrator was developed to display the human-robot collaboration as central element of the concept. ...
... The concept of a Collaborative Human-Robot Picking System enables the integration of robots into an existing picking system without investing much effort into warehouse infrastructure or process design [6]. In this concept, human pickers help to integrate robots starting at a rather low performance level to improve during operations especially in the process of detecting wanted items within the shelves. ...
... The Picking system consist of four phases shown in Figure 1. The interaction between the phases realizes the concept of a Feedback Loop [6] [7]. ...
This paper introduces a demonstrator for a Collaborative Human-Robot Picking application consisting of a robot in front of a rack picking known objects into a bin. The major part of the system demonstrates the execution of picking orders carried out by human pickers and picking robots in a common workspace. A further part includes the training of Convolutional Neural Networks for object detection. A central concept of the system is an Emergency Call enabling the robot to request help if problems occur during object detection or grasping. The main goals of this demonstrator are to evaluate the interaction between human pickers and robots and to test the performance of object detection.
... The extraction feature map pass through transform layer which responsible of organizing the feature map and creating YOLO v2 transform layer object to improves the network stability by determining location predictions [22], [38]. The YOLO v2 output layer is the last layer which responsible of determining and providing location of defined bound box for the target objects [39]. Moreover, this layers contain some properties such as loss function, anchor box and classes [40]. ...
The developing of deep learning systems that used for chronic diseases diagnosing is challenge. Furthermore, the localization and identification of objects like white blood cells (WBCs) in leukemia without preprocessing or traditional hand segmentation of cells is a challenging matter due to irregular and distorted of nucleus. This paper proposed a system for computer-aided detection depend completely on deep learning with three models computer-aided detection (CAD3) to detect and classify three types of WBC which is fundamentals of leukemia diagnosing. The system used modified you only look once (YOLO v2) algorithm and convolutional neural network (CNN). The proposed system trained and evaluated on dataset created and prepared specially for the addressed problem without any traditional segmentation or preprocessing on microscopic images. The study proved that dividing of addressed problem into sub-problems will achieve better performance and accuracy. Furthermore, the results show that the CAD3 achieved an average precision (AP) up to 96% in the detection of leukocytes and accuracy 94.3% in leukocytes classification. Moreover, the CAD3 gives report contain a complete information of WBC. Finally, the CAD3 proved its efficiency on the other dataset such as acute lymphoblastic leukemia image database (ALL-IBD1) and blood cell count dataset (BCCD). Keywords: CAD3 CNN Leukemia WBCs classification dataset WBCs detection dataset YOLO This is an open access article under the CC BY-SA license.
... Often automated picking is assigned to a specific area [7] and automated picking in a cooperative area is carried out only in a few solutions [8,9]. Rieder and Verbeet [10] describe a system learning from data gathered during the cooperation of humans and robots during picking. Picking systems are defined by their picking execution into human picking, robot picking and picker-less [11]. ...
The automation of intralogistic processes is a major trend, but order picking, one of the core and most cost-intensive tasks in this field, remains mostly manual due to the flexibility required during picking. Reacting to its hard physical and ergonomic strain, the automation of this process is however highly relevant. Robotic picking system would enable the automation of this process from a technical point of view, but the necessity for the system to evolve in time, due to dynamics of logistic environments, faces operations with new challenges that are hardly treated in literature. This unknown scares potential investors, hindering the application of technically feasible solutions. In this paper, a model for the evaluation of the additional cost of training of automated systems during operations is presented, that also considers the savings enabled by the system after its evolution. The proposed approach, that considers different parameters such as capacity, ergonomics and cost, is validated with a case study and discussed.
... Information is transferred to ODBC databases (Oracle, SQL, etc.). On the other hand, since products in the form of digital pallets can be uniquely identified, the relevant logistics tracking system and the anti-counterfeiting system of RFIDbased products contribute to security [6]. ...
This paper aims to define a hypothetical start-up named as Mobay Smart Warehouse Solutions creates connected robot solutions to warehouses by IoT, RFID and certain warehouse robots. The team examined similar start-ups and robot manufacturer and compared with conducted studies and rebuild a smart warehouse system aiming less labour force and increased efficiency. Based on assumptions made using sectoral data: value creation, value proposition, production costs, business canvas, service flow and data flow are summarised. The return of investment based on assumed production costs showed that Mobay Solutions could be a profitable business for small and medium scale warehouses at first.
... Therefore, the general concept is gradually developed using a process model with an evaluation framework up to a demonstrator. The concept of a feedback loop is the Figure 1: Feedback loop for improvement of object detection in a cooperative human-robot picking system, according to [47] Electronic copy available at: https://ssrn.com/abstract=3821401 foundation of this approach and describes a self-improving automated system in a cooperative environment. ...
... Therefore, human employees must support robots [46]. The concept of a feedback loop for improvement of object detection by Rieder and Verbeet [47] propose a concept for improvement of object detection during automated picking by a human-robot feedback loop as shown in Figure 1. This concept assumes already existing knowledge about the position of an object in a picture is sufficient for successful gripping by a robot [48]. ...
Picking as core process of intra logistics must cope with an increasing difficulty in acquisition of personnel and with continuously changing product ranges. These challenges can be tackled by partwise automated picking systems to create a cooperative working environment for human pickers and picking robots. However, performance of picking robots is determined significantly by their ability to detect objects. The presented approach enables a stepwise transformation from manual picking to highly automated picking processes by learning robots increasing their ability for successful object detection by a neural network. Its goals are to guarantee reliable order fulfilment by a feedback loop between humans and robots for error handling and to gather data for learning algorithms. A concept for a human-robot collaboration and a process model realizing the feedback loop are proposed. In addition, a quantitative framework to evaluate partwise automated picking systems is introduced. First results are provided by a demonstrator including an environment for image recording and an agent-based simulator. It is shown that the probability for a successful object detection can be improved by the proposed approach.
... Werner et al. (2017) describe the collaboration of humans and robots in a static assembly cell. Rieder and Verbeet (2019) present a process model to realize a cooperative picking system defining an Application-Phase and a Learning-Phase to ensure order fulfillment and continuous improvement of robots' ability for object detection. This model is extended by Verbeet et al. (2019) by an Adjustment-Phase and a Cooperation-Phase as well as by a conceptual picking system. ...
Picking is a core process of logistics. The challenge of acquiring personnel for operations and handling steadily changing product ranges can be tackled by partwise automated picking systems to create a cooperative working environment for human pickers and picking robots. This chapter is motivated to enable a stepwise transformation from manual picking to highly automated picking processes by cooperative and learning robots. The main goal is to guarantee reliable order fulfilment by implementation of a feedback-loop between humans and robots for error handling and to gather data for machine learning algorithms to increase the performance of object detection. In this chapter a concept for measurement and evaluation of system performance is introduced ensuring successful processing of picking orders and training of picking robots to improve their ability for object detection. It is based on the amount of picking orders, the picking capacity of humans and robots, and the probability for successful automated order picking considering the training effort during system design. The proposed concept can be used for overall capacity planning as well as for operational control of picking processes.
... as described by researchers(Kohl et al., 2019;Mester and Wahl, 2019;Schwäke et al., 2017;Rieder and Verbeet, 2019), presented paper is focused on technologies for replacement of the walking and searching part of Time distribution within order picking process (according tode Koster, Le-Duc and Roodbergen, 2007) ...
Purpose:New technical solutions in logistics change the ways of working within warehouses on differentlevels, from warehouse layouts and concepts of goods pick-ing to process planning and human resources. Thus, disrupting the previous practice in its core.Methodology: In order to evaluate the impact of the new technologies on the ware-house operations, the multiple case study approach was used. To gain a deeper un-derstanding of the changes within logistics processes, the results of the deep-dive analysis are summarized using morphologic box methodology.Findings:Presented solutions such as AutoStore, Kiva and CarryPick can lead to a substantial increase in the speed of order picking while staying very flexible and de-manding significantly less of expensive warehouse space. Still, the implementation of these technologies requires a systematic approach with clearly stated goals.Originality:In contrary to available papers which are concentrating on a single case study with application of one technology at one particular company, the presented paper analyses several solutions comparing them with each other. Additionally, the research evaluates the impact of the technologies on logistic processes and ware-house layouts. Thus, creating value for practitioners looking for solutions to optimize intralogistics.
... Werner et al. (2017) describe the collaboration of humans and robots in a static assembly cell. Rieder and Verbeet (2019) present a process model to realize a cooperative picking system defining an Application-Phase and a Learning-Phase to ensure order fulfillment and continuous improvement of robots' ability for object detection. This model is extended by Verbeet et al. (2019) by an Adjustment-Phase and a Cooperation-Phase as well as by a conceptual picking system. ...
Purpose: A picking system is presented ensuring order fulfilment and enabling trans-formation from manual to automated picking using a continuous learning process. It is based on Machine Learning for object detection and realized by a human-robot collaboration to meet requirements for flexibility and adaptability. A demonstrator is implemented to show cooperation and to evaluate the learning process. Methodology: The collaborative process, system architecture, and an approach for evaluation and workload balancing for order fulfilment and learning of robots during picking have already been introduced. However, a practical application is still miss-ing. A demonstrator is implemented using an agent-based architecture (JADEX) and a physical robot (UR5e) with a camera for object detection and first empirical data are evaluated. Findings: Single components of the demonstrator are already developed, but a pending task is to implement their interaction to analyze overall system perfor-mance. This work focuses on human-robot-interaction (Emergency Call), automated generation of images extended by feedback information, and training of algorithms for object detection. Requirements of human-machine interface, technical evalua-tion of image recording, and effort of algorithm training are discussed. Originality: Many approaches for automated picking assume a static range of ob-jects. However, this approach considers a changing range as well as a concept for transformation of manual to automated picking enabled by human-robot coopera-tion and automated image recording while enabling reliable order fulfilment.
... The following section explains this concept in detail. Rieder and Verbeet (2019) present an adaptive process model to realize a cooperative picking system containing an Application-Phase and a Learning-Phase. This model was extended by Verbeet et al. (2019) by an Adjustment-Phase and a Cooperation-Phase as well as by a conceptual picking system describing its components and their interactions. ...
Picking is a core process of logistics. The challenge of acquiring person-nel for operations and handling steadily changing product ranges can be tackled by partwise automated picking systems to create a cooperative working environment for human pickers and picking robots. This paper is motivated to enable a stepwise transformation from manual picking to highly automated picking processes by co-operative and learning robots. The main goal is to guarantee reliable order fulfilment by implementation of a feedback-loop between humans and robots for error han-dling and to gather data for machine learning algorithms to increase the performance of object detection. In this paper a concept for measurement and evaluation of sys-tem performance is introduced ensuring successful processing of picking orders and training of picking robots to improve their ability for object detection. It is based on the amount of picking orders, the picking capacity of humans and robots, and the probability for successful automated order picking considering the training effort during system design. The proposed concept can be used for overall capacity plan-ning as well as for operational control of picking processes.
