Classification of recyclables using laser-induced breakdown spectroscopy for waste management

Jull, Harrisson; Bier, James Michael; Künnemeyer, Rainer; Schaare, P.

doi:10.1080/00387010.2018.1466806

Cited by 20 publications

(11 citation statements)

References 34 publications

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“…1) Measurement Principle: The waste category classification approaches are categorized into contact-based, i.e., those having active contact with target objects [102], [108], and nocontact-based approaches [116], [117], [118], [119], [120], [121]. Furthermore, deep learning (DL)-based algorithms employing RGB and RGB-depth (RGBD) sensors have been used to detect and segment individual waste items from a densely cluttered pile [4], [78], [109], [122], [123], [124], [125], [126], [127], [128], [129], [130], [131], [132], [133], [134], [135], [136].…”

Section: B Sensors and Recognitionmentioning

confidence: 99%

Challenges for Future Robotic Sorters of Mixed Industrial Waste: A Survey

Kiyokawa

Takamatsu

Koyanaka

2024

IEEE Trans. Automat. Sci. Eng.

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To achieve recycling of mixed industrial waste toward an advanced sustainable society, waste sorting automation through robots is crucial and urgent. For this purpose, a robot is required to recognize the category, shape, pose, and condition of different waste items and manipulate them according to the category to be sorted. This survey considers three potential difficulties in the sorting automation: 1) End-effector: to robustly grasp and manipulate different waste items with dirt and deformations; 2) Sensor: to recognize the category, shape, and pose of existing objects to be manipulated and the wet and dirty conditions of their surfaces; and 3) Planner: to generate feasible and efficient sequences and trajectories. This survey includes 76 references to studies related to automatic waste sorting and 159 references to worldwide waste recycling attempts. This pioneering investigation reveals the possibility and limitations of conventional systems; thus, providing insights on open issues and potential technologies to achieve a robot-incorporated sorter for the chaotic mixed waste is one of its contributions. This paper further presents a system design policy for readers and discusses future advanced sorters, thereby contributing to the field of robotics and automation.Note to Practitioners-Most automated sorting systems operate for limited target waste items. This study is motivated by the automation of mixed industrial waste treatment facilities using advanced robotic sorters. Emerging advances and increasing functionalities of robot system components will widen system applicability and increase use cases in the chaotic mixed industrial waste domain. This paper surveys the research conducted to date, discusses open issues and potential approaches, and presents user guides that provide practitioners with a system design policy. The user guides created according to the strengths and weaknesses of each system configuration provide future researchers and developers with a useful a priori design policy that has been thus far validated on efficiency, quality, productivity, and reliability. A question-and-answer style guide Manuscript

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Section: B Sensors and Recognitionmentioning

confidence: 99%

Challenges for Future Robotic Sorters of Mixed Industrial Waste: A Survey

Kiyokawa

Takamatsu

Koyanaka

2024

IEEE Trans. Automat. Sci. Eng.

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“…LIBS is already widely utilized for plastic sorting analyzers. The classification and identification of various types of waste plastics is the most common usage, consisting of different plastic objects; for instance, household applications, toys, electrical cables, containers, landmine casings, and various types of e-waste [ 45 ]. Furthermore, LIBS has received much attention for several plastic compounds such as plastic-based films, plastic-bonded explosives, and bio-plastic [ 46 ].…”

Section: Waste Plastic Recycling and Technologymentioning

confidence: 99%

Current Prospects for Plastic Waste Treatment

et al. 2022

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The excessive amount of global plastic produced over the past century, together with poor waste management, has raised concerns about environmental sustainability. Plastic recycling has become a practical approach for diminishing plastic waste and maintaining sustainability among plastic waste management methods. Chemical and mechanical recycling are the typical approaches to recycling plastic waste, with a simple process, low cost, environmentally friendly process, and potential profitability. Several plastic materials, such as polypropylene, polystyrene, polyvinyl chloride, high-density polyethylene, low-density polyethylene, and polyurethanes, can be recycled with chemical and mechanical recycling approaches. Nevertheless, due to plastic waste’s varying physical and chemical properties, plastic waste separation becomes a challenge. Hence, a reliable and effective plastic waste separation technology is critical for increasing plastic waste’s value and recycling rate. Integrating recycling and plastic waste separation technologies would be an efficient method for reducing the accumulation of environmental contaminants produced by plastic waste, especially in industrial uses. This review addresses recent advances in plastic waste recycling technology, mainly with chemical recycling. The article also discusses the current recycling technology for various plastic materials.

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“…Therefore, the feasibility of push-and-drop has remained untested until now, notwithstanding that such manipulations using robotic hands are reasonable methods of agile manipulation. Conventional automatic sorting systems are based on different types of sensors (e.g., optical [28]- [30] and thermal techniques [31], [32]). Mao et al [33] proposed a classifier using a convolutional neural network to classify an RGB object image that included one waste item.…”

Section: A Robotic Waste Sortermentioning

confidence: 99%

Robotic Waste Sorter With Agile Manipulation and Quickly Trainable Detector

Kiyokawa¹,

Katayama

Tatsuta

et al. 2021

IEEE Access

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Owing to human labor shortages, the automation of labor-intensive manual waste-sorting is needed. The goal of automating waste-sorting is to replace the human role of robust detection and agile manipulation of waste items with robots. To achieve this, we propose three methods. First, we provide a combined manipulation method using graspless push-and-drop and pick-and-release manipulation. Second, we provide a robotic system that can automatically collect object images to quickly train a deep neuralnetwork model. Third, we provide a method to mitigate the differences in the appearance of target objects from two scenes: one for dataset collection and the other for waste sorting in a recycling factory. If differences exist, the performance of a trained waste detector may decrease. We address differences in illumination and background by applying object scaling, histogram matching with histogram equalization, and background synthesis to the source target-object images. Via experiments in an indoor experimental workplace for waste-sorting, we confirm that the proposed methods enable quick collection of the training image sets for three classes of waste items (i.e., aluminum can, glass bottle, and plastic bottle) and detection with higher performance than the methods that do not consider the differences. We also confirm that the proposed method enables the robot quickly manipulate the objects.

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Classification of recyclables using laser-induced breakdown spectroscopy for waste management

Cited by 20 publications

References 34 publications

Challenges for Future Robotic Sorters of Mixed Industrial Waste: A Survey

Challenges for Future Robotic Sorters of Mixed Industrial Waste: A Survey

Current Prospects for Plastic Waste Treatment

Robotic Waste Sorter With Agile Manipulation and Quickly Trainable Detector

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