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Nowadays solid waste is generated at an unprecedented rate due to rapid urbanization and
industrialization. In the developing countries, recycling of useful materials from solid waste such as wood,
plastic, glass and metal is severely constrained by limited door-to-door collection and poor means of waste
sorting. Indeed, recovery of useful material...
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Context 1
... switches detect any item through a variety of optical characteristics. Basically, they consist of a light-emitting element and a light-receiving element. When the emitted light is interrupted or reflected by the sensing object, the amount of light received is changed. The receiving element detects this change converting it to an electrical output. We have three types of optical sensors: through-beam, diffuse reflective, and retro- reflective. The system uses the through-beam type shown in Fig. 3. ...
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... is non-contact electronic proximity sensor, which is used for detecting the position of metal objects. The sensing range depends on the type of metal being tested. Ferrous metals, such as iron and steel, allow a longer sensing range, whereas nonferrous metals, like aluminum and copper, minimize the sensing range to 60% [11]. It consists of coils and electromagnets, which are part of an LC tuned circuit that activates the oscillator. A low frequency electromagnetic field (EMF) is generated by the coils and electromagnets and radiated from the sensor's sensing surface. When a metallic object enters the EMF, the eddy current will be transmitted inside the conductive object. By absorbing the energy from EMF, this eddy current has a retroactive effect on the proximity sensor and weakens the oscillatory amplitudes. This modifies the parameters of the internal circuit thus enabling to detect any metallic approaches and to control the output of the switches. The used inductive sensor is shown in Fig. ...
Context 3
... proximity sensors, unlike the inductive ones, produce electrostatic field, which permits the sensing of metals as well as nonmetallic materials for example glass, paper, and wood. They depend on the dielectric constant of the object. Therefore, it is easier to detect materials with larger dielectric constants. Capacitive proximity sensors generate an electrostatic field and their sensing surface is formed of two metal electrodes of an unwound capacitor. When an object is near the surface, it enters the electrostatic field of these electrodes, thus the capacitance is modified in the oscillator circuit. Therefore, the oscillator starts oscillating with different amplitudes depending on the distance between the sensor and the object. When the oscillator reaches specific threshold, the output state of the sensor changes. Finally, when the object moves away, the oscillator's amplitude decreases and the sensor is switched back to its original state. The used capacitive sensor is shown in Fig. ...
Citations
... These sensors were successfully adopted in constructive demolition waste, 105 End-of Life vehicle (ELV) plastic, 106 and extensive types of solid waste sorting processes. 107 Finally, a laser sensor, which depicts profiles of objects and gauges distance to surface of objects, provides height and outline information to a robot arm for effective Figure 4 presents the functions and combination of different sensors. ...
The increase in global population and improvement of living standards have stirred up a continuous increase in solid waste generation, while simple incineration and landfilling bring about serious environmental and health concerns. In order to improve resource recovery and mitigate pollution, noncontacting and nondestructive sensor-based waste sorting systems are applied to enhance solid waste classification. In recent years, in addition to the rapid development of computer hardware, especially improvements of GPU computing capacity, complicated and efficient classification algorithms have emerged and been widely used in industrial sectors. These advances allow computers to process signals from sensors more quickly and accurately and to classify matters automatically. This article introduces widely applied sensor-based technologies in solid waste sorting and analyzes applicable conditions for each specific method. The latest developed algorithms are critically compared with competitive counterparts. Successful practices are described, and findings are highlighted. Though spectroscopic-based and vision-based waste classifications have achieved high performance in accuracy and detection speed, challenges and future directions can still provide wide development opportunities. Concretely, these opportunities generally comprise classification of indistinct plastics, application of the latest object detection algorithms, appropriate data set formulating, and sensor combination for multiple sorting tasks within a single system.
... In this context, in addition to the aforementioned modules that consist of semi-automation with multiple robots, the components for the modules: multi-modal sensors, robustly applicable grippers, and integrated planners, are necessary. Improving the fusion systems of multiple sensor data [221], [222] and/or multiple features of sensors [86] is a promising approach to replace the customized sensing systems individually prepared according to different sorting workplaces. Modular [223] and/or multifunctional [224], [225] endeffectors can possibly be used for handling a large variety of waste. ...
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 and a sorting-target-aware previous study reference list allows users to find the desired system configuration, including the investigated components according to their purpose.
... Currently, employed methods rely on manual separation, classification, and transportation, which require a lot of manual labor and money, hence making it a cumbersome and expensive process [1]. Several techniques of automatic waste detection and sorting have been proposed, such as automated sorting [2][3][4][5]. Hence, a massive amount of potential lies in the domain of waste management techniques and improving them. ...
Garbage detection and disposal have become one of the major hassles in urban planning. Due to population influx in urban areas, the rate of garbage generation has increased exponentially along with garbage diversity. In this paper, we propose a hardware solution for garbage segregation at the base level based on deep learning architecture. The proposed deep-learning-based hardware solution SmartBin can segregate the garbage into biodegradable and non-biodegradable using Image classification through a Convolutional Neural Network System Architecture using a Real-time embedded system. Garbage detection via image classification aims for quick and efficient categorization of garbage present in the bin. However, this is an arduous task as garbage can be of any dimension, object, color, texture, unlike object detection of a particular entity where images of objects of that entity do share some similar characteristics and traits. The objective of this work is to compare the performance of various pre-trained Convolution Neural Network namely AlexNet, ResNet, VGG-16, and InceptionNet for garbage classification and test their working along with hardware components (PiCam, raspberry pi, infrared sensors, etc.) used for garbage detection in the bin. The InceptionNet Neural Network showed the best performance measure for the proposed model with an accuracy of 98.15% and a loss of 0.10 for the training set while it was 96.23% and 0.13 for the validation set.
... An additional advantage related to mechanical waste sorting is the reduced health risk for workers [7]. The option to combine sensors with different characterization principles [8] especially results in a better quality of the final product, higher product yield and improved valuable recovery [9], 1. ...
Sensor-based and robot sorting are key technologies in the extended value chain of many products such as packaging waste (glass, plastics) or building materials since these processes are significant contributors in reaching the EU recycling goals. Hence, technological developments and possibilities to improve these processes concerning data analytics are evaluated with an interview-based survey. The requirements to apply data analytics in sensor-based sorting are separated into different sections, i.e., data scope or consistency. The interviewed companies are divided into four categories: sorting machine manufacturers, sorting robot manufacturers, recycling plant operators, and sensor technology companies. This paper aims to give novel insights into the degree of implementation of data analytics in the Austrian waste management sector. As a result, maturity models are set up for these sections and evaluated for each of the interview partner categories. Interviewees expressed concerns regarding the implementation such as a perceived loss of control and, subsequently, a supposed inability to intervene. Nevertheless, further comments by the interviewees on the state of the waste management sector conveyed that data analytics in their processes would also be a significant step forward to achieve the European recycling goals.
... K. Chahine et al. [8] presented an automated waste sorting system that will sort the waste using PLC, inductive proximity sensors, capacitive proximity sensors, and photoelectric sensors. This system will sort 4 types of garbage. ...
Because of the economic development of industries, factories produce huge quantities of waste daily, of which some are reusable and some are non-usable. So, waste collection and segregation become relatively important for such industries. However, Bangladesh is far behind other countries in this arena. Since improper management of waste leads to considerable loss of industries, waste management becomes a big challenge for an industry in Bangladesh. The main objective of this project is to automate this waste management process with minimum human involvement to reduce the cost and save time. For this purpose, an automatic robot is built on an IoT platform using appropriate sensors, control units, and servo motors controlled by Arduino Uno. This robot collects the waste even from a nearly unreachable place and also, sorts them into metallic and non-metallic waste. We believe that the proposed robot is capable of meeting the strict requirement of low-cost as well as better performing units made by the local industries and hence, boost the use of waste management service of our country.
... In some studies a smart trash bin has been created which can provide information if the trash bin is full (Fadel, 2017), (Zavare et al., 2017), (Navghane, Killedar and Rohokale, 2016), but sorting has not been done for the type of waste included. Subsequent research has made waste sorting machines, but on machines made using conveyors (Wath and Ughade, 2019), (Jude et al., 2019), (Chaithanya et al., 2017), (Samreen et al., 2017), (Williams and Bentil, 2016), (Ranjitha et al., 2018), (Engineering, 2019), (Chahine and Ghazal, 2017). This is certainly not suitable for a small-scale trash bins because to make or buy it requires expensive costs. ...
... This machine uses an inductive proximity sensor to detect metal waste and a capacitive proximity sensor to detect plastic bottle waste. These sensors were chosen because they have been proven to be able to sort out different types of waste (Chahine and Ghazal, 2017), (Pushpa et al., 2015), (C, Badami and H, 2017). Then this machine is equipped with a PIR sensor to detect whether or not people are going to throw waste, thus making opening and closing the trash cans automatically. ...
... Chahine & Ghazal [17] designed and developed an automated waste sorting system with the use of an inductive proximity sensor, a capacitive proximity sensor and a photelectic sensor. Ang et al. [18] developed automated waste sorter in 2013, which had a mobile robot delivery system. ...
Waste management as well as sorting is a very crucial task to make the environment green and to ensure better (re)use of the resources. Bangladesh, because of its high density population, is facing enormous challenges to manage huge amount of wastes produced every day. So the purpose of this paper is to use the advancement of Information and Communication technology (ICT) to improve the waste management system and make lives better by providing a smarter way for waste sorting and management. In this paper, an intelligent system was proposed and developed for automatically sorting the waste to be used in context of Bangladesh. A light weighted experiment was carried out to evaluate the system performance. The experiment replicated with 11 objects (waste) of different size and types. The experimental results showed that the proposed system was reliable and achieved about 82% accuracy for the categorization of different kinds of waste.
Due to the current labor shortage, automation by robots has been expected in society. In manual sorting of garbage in recycling factories, there is a risk of injury due to sharp garbage, and robots are needed to replace them. In this paper, we propose an improved method of garbage sorting using thermal images. Previously, we classified three types of beverage container garbage from thermal images, but it could not cope with dense garbage. In this work, material classification is performed for each pixel of the thermal image, followed by clustering to correctly separate and classify garbage, even dense garbage. In the experiment, we collected thermal images of heated garbage on hot conveyor for classification, and verified the accuracy of the computed material and object maps, and compared them with previous work.
