Aspects concerning the optimal development of robotic systems architecture for waste sorting tasks

Hancu, Olimpiu; Rad, Ciprian; Lapusan, Ciprian; Brişan, Cornel

doi:10.1088/1757-899x/444/5/052029

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…Abu-Qdais et al compares traditional machine learning models like Random Forest and Support Vector Machine with deep learning CNN. Results indicate superior performance of CNN, and introduces JONET, a deep learning model based on DenseNet 201 for waste classification [20]. Hancu et al discuss aspects related to the selection of robotic system architecture for sorting solid recyclable waste streams.…”

Section: Related Workmentioning

confidence: 99%

Recyclable plastic waste segregation with deep learning based hand-eye coordination

Sundaralingam,

Ramanathan

2024

Environ. Res. Commun.

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Plastic waste management is a critical concern in municipal solid waste management systems worldwide. Despite the efforts of waste management personnel to segregate waste manually, the existing challenges persist. In municipal waste facilities, individuals responsible for waste segregation face numerous obstacles. Consequently, a significant amount of plastic waste ends up in landfills, exacerbating the plastic waste problem. To overcome these challenges, this research focuses on developing an automated system capable of categorizing plastic waste based on its visual characteristics. The trained model exhibits high precision in identifying various types of plastic waste, including PET, HDPE, PVC, LDPE, PP, and PS. Specifically, the model achieves an Average Precision of 0.917 and an Average Recall of 0.801. Moreover, the model maintains a good balance between precision and recall. In real-time operation, an overhead camera locates the positions of both the waste items and the gripper. By calculating the positional difference between the waste and the gripper, the system achieves a higher level of segregation accuracy, resembling human-like hand-eye coordination. The proposed system offers a solution to the challenges faced in MSW facilities, where the timely segregation of waste is crucial. By automating the plastic waste categorization process, the system can significantly improve waste management practices, leading to a more sustainable approach to plastic waste disposal and recycling.

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Section: Related Workmentioning

confidence: 99%

Recyclable plastic waste segregation with deep learning based hand-eye coordination

Sundaralingam,

Ramanathan

2024

Environ. Res. Commun.

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“…This is why, an Automated Solid Waste Selection System would overcome the drawbacks of not recycling the solid waste. The concept of the system has been presented in the papers [8]- [10]. In order to design the conveyor and the robotic system as optimal as possible, it has been considered that the required workspace of the robots has to be as wide as the conveyor the other dimensions (length, and height) are user imposed (as presented in the Fig.…”

Section: Robot To Be Optimizedmentioning

confidence: 99%

Performance Comparison in the Optimisation of a Parallel Robot Using Particle Swarm Optimisation

Boantă¹,

Brişan²

2020

IJMO

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Parallel robots have many industrial applications due to their well-known advantages as high operational speeds, stiffness and accelerations. One the other hand, their workspace is reduced compared to the size of the elements of the robot. Frequently, the design of parallel robots implies a large amount of variables and nonlinear equations. This is why, a human designer generally applies optimisation algorithms in order to obtain specific properties of the robot. If the number of variables involved in the optimisation is too high, the required computational times may be extremely increased, aspect that for some applications is unacceptable. This is why, the aim of this paper is to analyse the performance comparison in terms of efficiency and computational times of an optimisation problem with several numbers of variables included in the optimisation. The variable define the geometrical characteristics of a parallel robot used for a solid waste selection system. Also, the optimisation problem is implemented using a heuristic algorithm, namely the Particle Swarm Optimization.

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“…The waste is taken from the moving conveyor and placed in a specific waste container, based on prior information received by and Image Acquisition System (see the Fig. 3) as presented in the concept from [3] and [4] (this aspect being beyond the scope of this paper).…”

Section: Robot To Be Optimizedmentioning

confidence: 99%

“…The concept of the Automated Solid Waste Selection System has been presented extensively in the papers [3] and [4] and is composed by a ramified Transportation System (TS) , such as set of conveyors that transport the unsorted waste (UW) from the loading point up to the unloading point, a waste Image Recognition and Sensorial System (IRS) , that identifies the type of waste that passes beneath, and a waste selective Robot Disposal System (RDS), that extracts a specific waste from the conveyor and places it in a specific container. If necessary, a more detailed description of the Automated Solid Waste Selection System is presented in the papers [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Optimization of a Robot Used for a Solid Waste Selection System

Boantă¹,

Brişan²

2019

IJMO

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Solid waste selection is a common problem of the cities in emerging economies. In order to overcome this problem, a concept of smart selection of solid waste system has been developed, composed by a transportation system, a sensorial systems and a robotic system for selection of the solid waste. This paper focuses of the robotic system, the aim of this paper being the multi-objective optimization of a parallel robotic system in order to achieve maximal workspace and dexterity. Hence, the paper illustrates a small introduction in the concept of the new solid waste selection system and presents a recent literature review regarding the optimization of robots. Then, the structure and formal analysis of the robotic systems that has application for a smart selection of solid waste. The optimization is carried out using Genetics Algorithms and the objective function of the optimizations that takes into consideration the volume of the constant orientation workspace alongside with the average isotropy index within the workspace.

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Aspects concerning the optimal development of robotic systems architecture for waste sorting tasks

Cited by 5 publications

References 12 publications

Recyclable plastic waste segregation with deep learning based hand-eye coordination

Recyclable plastic waste segregation with deep learning based hand-eye coordination

Performance Comparison in the Optimisation of a Parallel Robot Using Particle Swarm Optimisation

Optimization of a Robot Used for a Solid Waste Selection System

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