An Innovative Automated Robotic System based on Deep Learning Approach for Recycling Objects

Kim, Jaeseok; Nocentini, Olivia; Scafuro, Marco; Limosani, Raffaele; Manzi, Alessandro; Dario, P.; Cavallo, Filippo

doi:10.5220/0007839906130622

Cited by 14 publications

(3 citation statements)

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“…With the development of automation technology, robotics has grown by leaps and bounds, and a variety of intelligent robots are used in industrial, medical, educational, and agricultural applications. Robot grasping is an important function of robots in industrial scenarios, which is traditionally implemented based on manual teaching methods and 2D or 3D model matching to obtain grasping postures [1]. The former does not address the need to capture any object in any pose; the latter requires a large number of templates for different objects to be created in advance, and more than one template may be needed for the same object, making it laborious to build a template search library [2].…”

Section: Introductionmentioning

confidence: 99%

Multifunctional Robot Grasping System Based on Deep Learning and Image Processing

Zhang

Yang

2022

Wireless Communications and Mobile Computing

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This paper firstly introduces the general architecture of the multifunctional harvesting robot grasping system; then, deep learning is used to investigate the object target recognition, and a set of target detection algorithm based on convolutional neural network is implemented; then, image processing technology is used to realize the function of target object localization, which can guide the multifunctional harvesting robot to complete the picking of the target multifunctional. The experimental results show that the multifunctional harvesting robot has a small calculation error of multifunctional coordinates and has a strong multifunctional recognition and positioning capability.

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Section: Introductionmentioning

confidence: 99%

Multifunctional Robot Grasping System Based on Deep Learning and Image Processing

Zhang

Yang

2022

Wireless Communications and Mobile Computing

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“…Because they do not affect each other in modification and operation. When designing an object search algorithm, the rule-based attributes are defined by the topic tree, which contains the titles and placement tags of the items to be placed [21]. In Figure 6, we can get a good tree diagram representation.…”

Section: Figmentioning

confidence: 99%

State of the art review on automatic sorting system for industrial robots using Internet of Robotic Things

Gueye

Wang

Mushtaq

et al. 2022

Preprint

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The idea for the Internet of Robotic Things (IoRT) comes from activities. IoRT allows smart devices to combine sensor information from multiple sources, use broader nearby insights to determine the best reason for the activity and display events on-screen. The Internet of Things can connect web applications to other gadgets to make them smarter. Automated engineers combine the two and arm themselves with sensors that can analyze the current situation. Mechanical improvements such as ordinary voice links (PC) vision sensor innovations and artificial intelligence (AI) have resulted in a turn of events and programmed placement frameworks under advanced machinery. This article examines frameworks for arranging programmed objects to assemble robots and innovative patterns in ordinary language programmed programming, and three-dimensional (3D) visual insight. The relevant statement "regulation scene" should describe the combination of all three modules. This computation enables people to relate through a three-tiered information scene and guides managers to use discourse to provide reasonable criteria whenever an order is legitimate. After receiving precise criteria, the robot can use this diary's planned execution calculations and programmed programming to direct the placement of programmed items. The play area provides a context for the natural product, while the landscape design provides a valuable part of the framework.

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“…If the trash is detected, the manipulator picks it up and places it in the trash container. Jaeseok Kim et al [21] integrate deep learning with the industrial robotic arm to classify garbage according to its material. First, the points cloud is processed utilizing the Kinect.…”

Section: Introductionmentioning

confidence: 99%

Object Detection-based Automatic Waste Segregation using Robotic Arm

Ibrahim¹,

Shoitan²,

Moussa³

et al. 2023

IJACSA

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Today's overpopulation and fast urbanization present a significant challenge for developing countries in the form of excessive garbage generation. Managing waste is essential in creating sustainable and habitable communities, but it remains an issue for developing countries. Finding an efficient smart waste management system is a challenge in current research. In recent years, robots and artificial intelligence have influenced a wide range of industries, especially waste management. This research proposes a waste segregation system that integrates the robot arm and YOLOv6 object detection model to automatically sort the garbage according to its type and achieve real-time requirements. The proposed algorithm utilizes the pros of the hardware-friendly architecture of YOLOv6 while keeping high detection accuracy in detecting and classifying garbage. Moreover, the proposed system creates a 3D model of a 4 DOF robotic arm by CAD tools. A new approach based on a geometric method is proposed to solve the inverse kinematics problem in order to precisely calculate the proper angles of the robot arm's joints via a unique solution with less computational time. The proposed system is evaluated on a modified TrashNet dataset with seven garbage classes. The experiments reveal that the proposed algorithm outperforms the other recent YOLO models in terms of precision, recall, F1 score, and model size. Furthermore, the proposed algorithm consumes approximately fractions of a second for picking up and placing a single object in its proper basket.

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An Innovative Automated Robotic System based on Deep Learning Approach for Recycling Objects

Cited by 14 publications

References 0 publications

Multifunctional Robot Grasping System Based on Deep Learning and Image Processing

Multifunctional Robot Grasping System Based on Deep Learning and Image Processing

State of the art review on automatic sorting system for industrial robots using Internet of Robotic Things

Object Detection-based Automatic Waste Segregation using Robotic Arm

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