Robotic Sorting Research Articles

Automatic Control of Color Sorting and Pick/Place of a 6- DOF Robot Arm

This work focuses on the implementation and design of a six degree of freedom, 6-DOF control of automatic color sorting and pick and place tasks for a robot arm using wireless controlling interface – Blynk apps. Based on the collaboration between the servo motor and input color sensor, this wireless control of automatic color sorting robot arm provides a powerful wireless control GUI (Graphics User Interface) and to enable the method for manual color sorting mode. The color sorting mode is performed once the mode is turned on by the user. The robot arm able to differentiate the colors of the object (input) and categorize or classify the object to the correct places automatically. It provides a stable, efficient, and precision results without any vibration of control as the main target for this project. In this work, six servo motors were used to realize the real robotic arm for industrial use. Five servos were operated to control the entire robot arm motion including the base, shoulder, and elbow as well as one servo is reserved for the positioning of the end effector. Two input variables namely TSC3200 Color Sensors & HC-SR04 Ultrasonic Sensors were employed as the input for the robot arm. The output variable mainly focused on the servo motor as the links for the robot arm to reposition and change the motion for the entire system.

LINE FOLLOWING ROBOT WITH HIGH RADIATION MATERIAL DETECTION CAPABILITY

Advancement in technology have led to a rapid development in the design and manufacturing of robots, enabling them to provide human capabilities without the inherent shortcoming associated with human capabilities; such as boredom, fear, inefficiency etc. A mobile robot that can sense and observe the line drawn on the floor is the Line Follower Robot. The direction is usually predefined and can be either visible on a white surface with a high contrast colour like a black line or invisible like a magnetic field. Hence, with its Infrared Ray (IR) sensors mounted under the robot, this sort of robot can feel the line. Then, the information is conveyed by specific transition buses to the processor. The processor will determine the right commands and then send them to the driver, and the line follower robot will then follow the direction. Therefore, this paper focuses on the design of a mobile line following robot for detecting the high radiation level of a sample farmland and display on a 7-segment display by the aid of sensors to navigate through grid. The mobile line following robot must move through the squares and detect the high radiation levels and at the end provides us with information on the number of squares and detected high radiation levels. The robot's area of operation is limited to six squares each of dimension 60cm×60 cm. Silver coloured square spots with dimensions of 5 cm by 5 cm made of foil paper are used to indicate a high radiation level

Classification of Shellfish Recognition Based on Improved Faster R-CNN Framework of Deep Learning

In view of the current absence of any deep learning algorithm for shellfish identification in real contexts, an improved Faster R-CNN-based detection algorithm is proposed in this paper. It achieves multiobject recognition and localization through a second-order detection network and replaces the original feature extraction module with DenseNet, which can fuse multilevel feature information, increase network depth, and avoid the disappearance of network gradients. Meanwhile, the proposal merging strategy is improved with Soft-NMS, where an attenuation function is designed to replace the conventional NMS algorithm, thereby avoiding missed detection of adjacent or overlapping objects and enhancing the network detection accuracy under multiple objects. By constructing a real contexts shellfish dataset and conducting experimental tests on a vision recognition seafood sorting robot production line, we were able to detect the features of shellfish in different scenarios, and the detection accuracy was improved by nearly 4% compared to the original detection model, achieving a better detection accuracy. This provides favorable technical support for future quality sorting of seafood using the improved Faster R-CNN-based approach.

Application and research of robot sorting system based on LabVIEW

Sorting robot is a kind of artificial intelligence robot which has certain sense ability and can classify items. In the sorting industry, its appearance not only reduces the labor input, but also improves the production efficiency of the sorting industry. In this paper, a production line composed of linear Delta robot and 3-DOF robot is studied. The forward and inverse solutions are calculated by MATLAB and simulated by Solid Works. Through the visual processing function, communication function and axle card driving function of Lab VIEW software, the servo motor rotation is controlled to achieve the purpose of the end moving along the predetermined trajectory, and finally the visual sorting function is realized.

Areal‐timeclassification and detection method for mutton parts based on single shot multi‐box detector

AbstractThis article proposes a real‐time classification and detection method for mutton parts based on a single shot detector (SSD). We acquired 9,000 images of various parts of mutton in a sheep slaughtering workshop, characterized by multiple classes and multiple samples. After image preprocessing, an image dataset of the mutton parts was established for later model training. Subsequently, we introduced transfer learning to train an SSD–VGG network and obtain the optimal model. The optimal model was then applied to determine the category and position of each mutton part in the image, thus realizing the classification and detection of mutton parts. In this method, the average accuracymAPand average processing time for a single image are selected as the accuracy and speed indicators, respectively, for judging the detection performance of the model. The feature extraction network VGG is replaced with MobileNetV1 to optimize the real‐time performance of the SSD. Furthermore, we set an additional illumination dataset with two brightness levels “bright” and “dark” to verify the generalization ability of the optimized model. Finally, four common object detection algorithms, namely YoloV3–MobileNetV1, YoloV3–DarkNet53, Fast‐RCNN, and Cascade‐RCNN, are introduced to perform comparative experiments on mutton image datasets. The test results prove that the SSD–MobileNetV1 exhibits high accuracy and good real‐time performance, with a certain generalization ability. It has a better comprehensive detection ability than other methods and can provide technical support for mutton processing.Practical ApplicationsCurrently, in the processing of mutton, the multiple parts of mutton are identified and sorted manually, which is time‐consuming and laborious, and there are certain hidden food safety hazards. A deep‐learning‐based object detection method can solve the above problems effectively. Therefore, this study uses SSD to perform an accurate real‐time recognition of the multiple parts of mutton from its images and provide a visual guidance for mutton sorting robots. It can also aid further research in the slaughtering and processing of other meat.

Design Color Sorting with Conveyor and Robot Arm Using Lego Mindstorms NXT

Lego Mindstorms education robot is an interactive robot because it can be used to design various kinds of robot applications. This robot can be used to design sorting robots before they are applied to real robots. Many sorting techniques are in the form of colorful objects, but only for one-time sorting. This research will design and implement a color sorting robot with 2 detection stages, namely the conveyor and the robot arm, the initial sorting is carried out by the conveyor for sorting color objects, the next sorting uses the robot arm. to move the selected color object to a place that matches the desired color. The results obtained in sorting color objects using 1 color sensor and Bluetooth for communication between the conveyor and the robot arm via Bluetooth works well where three objects in red, blue and green can be moved according to the desired color object.

Goal Lifecycle Networks For Robotics

A Goal Lifecycle Network (GLN) is a conceptual process model that captures the progression of goals from their formulation to their completion, including planning and execution concerns. GLNs synthesize the literature on hierarchical goal networks, goal lifecycles, and plan execution. We formalize GLNs based on a state-variable representation, extend GLNs with an execution lifecycle, describe a partial reference implementation of GLNs, and show how the temporal PDDL language can be translated into GLNs for dispatchable execution. We integrate GLNs in three proof-of-concept robotics demonstrations: (1) a two-armed robot sorting items into baskets; (2) a multi-vehicle quad-rotor team surveying a region; and (3) centralized planning for a simulated disaster relief based on the Robocup Rescue League. The theory, implementation, and demonstrations highlight that GLNs are effective for goal management in the robotics systems we study.

Artificial Intelligence in the Sorting of Municipal Waste as an Enabler of the Circular Economy

The recently finalized research project “ZRR for municipal waste” aimed at testing and evaluating the automation of municipal waste sorting plants by supplementing or replacing manual sorting, with sorting by a robot with artificial intelligence (ZRR). The objectives were to increase the current recycling rates and the purity of the recovered materials; to collect additional materials from the current rejected flows; and to improve the working conditions of the workers, who could then concentrate on, among other things, the maintenance of the robots. Based on the empirical results of the project, this paper presents the main results of the training and operation of the robotic sorting system based on artificial intelligence, which, to our knowledge, is the first attempt at an application for the separation of bulky municipal solid waste (MSW) and an installation in a full-scale waste treatment plant. The key questions for the research project included (a) the design of test protocols to assess the quality of the sorting process and (b) the evaluation of the performance quality in the first six months of the training of the underlying artificial intelligence and its database.

Review on Sensor based Colour Sorting Robot for Candy Manufacturing

In this work, a new approach for high speed sorting of candy into desired locations is introduced. In this work color sorting of candy is done by detecting colour and the edges of given image which are of similar colours. The colour sensor TCS230 detects three colour red,green,blue and black(no colour). When there is no candy in front of the sensor it produces an output of 330Hz range frequency and when there is a candy it produces an output frequency of 7-14 kHz.The microcontroller can find the frequency of the output from TCS230. The output is obtained by sorting the color of candy depending on the frequency analysis of the output of the TCS230 sensor in the Conveyor system.This work focuses on literature review of sensor based colour sorting robot for candy manufacturing.

Cartesian space robot manipulator clamping movement in ROS simulation and experiment

Abstract This paper studies the robot arm sorting position control based on robot operation system (ROS), which works depending on the characteristics of the robot arm sorting operation using the top method, to automate the sorting operation and improve the work efficiency of workpiece sorting. Through the ROS MoveIt! module, the sorting pose and movement path of the robotic arm are planned, the inverse kinematics of the sorting robotic arm is solved, and the movement pose characteristics of the sorting robotic arm are analysed. The robot arm model was created using Solidworks software, and the URDF model file of the robot arm was exported through the sw2urdf plugin conversion tool, and the parameters were configured. Based on ROS for 6-degree-of-freedom (DOF) robot motion simulation, random extended tree (RRT) algorithm from open motion planning library (OMPL) is selected. The robot motion planning analysis and sorting manipulator drive UR5 manipulator. The results show that the sorting pose and motion trajectory of the robot arm are determined by controlling the sorting pose of the sorting robot arm, and the maximum radius value of the tool centre point (TCP) rotation of the robot arm and the position of the workpiece are obtained. This method can improve the success rate of industrial sorting robots in grabbing objects. This analysis is of great significance to the research of robots’ autonomous object grabbing.

The Development of Waste Sorting Robot to Improve Environmental Care Attitudes at MTsN 1 Banda Aceh

One of the waste management efforts is to sort waste. Waste handling in Indonesia, especially in Aceh, is still conventional. Based on these facts, a new breakthrough is needed during the era of the industrial revolution 4.0 by making a waste sorting robot. The purpose of this study was to determine the process of developing a waste sorting robot and to determine the environmental care attitude at MTsN 1 Banda Aceh by developing a waste sorting robot. This research method is Research and Development (R&D). The researcher referred to Sugiyono's development model through 10 sequential steps (procedural), namely: Potential and problems; 2) data collection; 3) Product design; 4) Design validation; 5) Revised design; 6) Product trials; 7) Product revision; 8) Trial use; 9) Product revision; 10) Mass production. Research and development carried out by researchers only from steps 1-9. In the tenth step (10) of mass production, researchers did not do it because of time and cost constraints. The research location was conducted at MTsN 1 Banda Aceh. The research was carried out from October to November 2020. The research instruments were interview guides, a waste sorting robot product feasibility assessment questionnaire, and an environmental care attitude questionnaire. The data analysis technique used descriptive statistics with percentages. The results of the validation of the feasibility design of the waste sorting robot by 2 machine design experts and 1 science teacher at MTsN 1 Banda Aceh, an environmental material expert, obtained a score of 17 with a value of 100% in the less feasible category. Furthermore, in the trial, the score was 29 with a value of 100% in the very feasible category. The conclusion of this study is that the waste sorting robot is very feasible to use. The development of a waste sorting robot can improve environmental awareness at MTsN 1 Banda Aceh.

Research on Machine Vision Technology of High Speed Robot Sorting System based on Deep Learning

Workpiece sorting is an important link in industrial production and transportation, which will directly affect the accuracy and speed of the workpiece. In the traditional manual sorting, we will be affected by the environment and subjective factors, which will cause visual fatigue error. With the rapid development of computer technology, the intelligence of robot is higher and higher, which has been applied to many fields. Robot sorting has many advantages, such as high speed, small inertia, high positioning accuracy, which has become an important part of industrial manufacturing and express transportation. For sorting system, we need to focus on machine vision technology and deep learning algorithm. Through image recognition and target tracking, we can provide accurate basic data. Through deep learning algorithm, we can intelligent sorting, which needs our in-depth study.

A Small Simulated Logistics Transfer Robot Car Structure Design

As a new product of the development of modern science and technology, the research and development of logistics robot has become the focus of social attention. Robot sorting and handling is the designated project of Jiangsu University Robot Competition. According to the requirements of the competition, this paper designs a kind of logistics robot trolley which can identify and grab materials according to a given path and transport them to a predetermined location. The mechanical structure design, driving motor selection and mechanical checking calculation of the car are mainly completed. According to the later experiments, the results show that the desired results can be achieved.

Research on intelligent control algorithm of coal gangue sorting robot armbased on reinforcement learning

Research on intelligent control algorithm of coal gangue sorting robot armbased on reinforcement learning

Visual Sorting of Express Parcels Based on Multi-Task Deep Learning.

Visual sorting of express parcels in complex scenes has always been a key issue in intelligent logistics sorting systems. With existing methods, it is still difficult to achieve fast and accurate sorting of disorderly stacked parcels. In order to achieve accurate detection and efficient sorting of disorderly stacked express parcels, we propose a robot sorting method based on multi-task deep learning. Firstly, a lightweight object detection network model is proposed to improve the real-time performance of the system. A scale variable and the joint weights of the network are used to sparsify the model and automatically identify unimportant channels. Pruning strategies are used to reduce the model size and increase the speed of detection without losing accuracy. Then, an optimal sorting position and pose estimation network model based on multi-task deep learning is proposed. Using an end-to-end network structure, the optimal sorting positions and poses of express parcels are estimated in real time by combining pose and position information for joint training. It is proved that this model can further improve the sorting accuracy. Finally, the accuracy and real-time performance of this method are verified by robotic sorting experiments.

Development of the algorithm for the optimization of objects pick up sequence on a conveyor belt

With recent advances in robotics, various methods and systems for robotic movement planning have gained popularity. In particular, the problem of planning of pick and move operations for the robotic sorting cells has drawn certain attention because new strategies and algorithms become a necessity as a robotic sorting systems application area extends. In the present work, a new algorithm for optimization of sorting operation sequences is proposed and results of its testing on a computer simulation are presented. The algorithm is based on the state tree search method and is aimed for performance improvement of the systems in which the input load can exceed the capacity of the robotic manipulator.

Development of Higher Vocational Artificial Intelligence Course based on Enterprise Real Project

In the era of intelligence, the development of artificial intelligence technology has greatly promoted the demand of relevant professional courses. This paper mainly analyses the framework and implementation process of the enterprise real project “garbage sorting robot system” which is the project carrier of artificial intelligence course. On this basis, according to the knowledge system of artificial intelligence technology, the relevant teaching knowledge points and teaching methods of artificial intelligence course are designed.

Study on Comprehensive Calibration and Image Sieving for Coal-Gangue Separation Parallel Robot

Online sorting robots based on image recognition are key pieces of equipment for the intelligent washing of coal mines. In this paper, a Delta-type, coal gangue sorting, parallel robot is designed to automatically identify and sort scattered coal and gangue on conveyor belts by configuring the image recognition system. Robot calibration technology can reduce the influence of installation error on system accuracy and provides the basis for the robot to accurately track and grab gangue. Due to the fact that the angle deflection error between the conveyor belt coordinate system and the robot coordinate system is not considered in the traditional conveyor belt calibration method, an improved comprehensive calibration method is put forward in this paper. Firstly, the working principle and image recognition and positioning process of the Delta coal gangue sorting robot are introduced. The scale factor parameter Factorc of the conveyor encoder is adopted to characterize the relationship between the moving distance of the conveyor and the encoder. The conveyor belt calibration experiment is described in detail. The transformation matrix between the camera, the conveyor belt, and the robot are obtained after establishment of the three respective coordinate systems. The experimental results show that the maximum cumulative deviation of traditional calibration method is 13.841 mm and the comprehensive calibration method is 3.839 mm. The main innovation of the comprehensive calibration is such that the accurate position of each coordinate in the robot coordinate system can be determined. This comprehensive calibration method is simple and feasible, and can effectively improve system calibration accuracy and reduce robot installation error on the grasping accuracy. Moreover, a calculation method to eliminate duplicate images is put forward, with the frame rate of the vision system set at seven frames per second to avoid image repetition acquisition and missing images. The experimental results show that this calculation method effectively improves the processing efficiency of the recognition system, thereby meeting the demands of the grab precision of coal gangue separation engineering. The goal revolving around “safety with few people and safety with none” can therefore be achieved in coal gangue sorting using robots.

Development of an automatic sorting robot for construction and demolition waste

An automatic sorting robot is designed in this report. The system makes use of height maps and near-infrared (NIR) hyperspectral images to locate the ROI of objects and to do online statistic pixel-based classification in contours. This approach has two advantages: (1) to generate training data for sorting without manual work; (2) to get more stable final result. Two kind of features in hyperspectral image were extracted, a scale-sensitive algorithm was used to identify amplitude feature and a scale-insensitive algorithm was used to identify trend feature. After location and classification, the robot grabs valuable targets based on their position and posture and places them into the corresponding recycling area based on their category. The prototype machine can automatically sort construction and demolition waste (C&DW) with a size range of 0.05–0.5 m. The sorting efficiency can reach 2028 picks/h, and the online recognition accuracy nearly reaches 100%.

A robot system equipped with deep convolutional neural network for autonomous grading and sorting of root-trimmed garlics

The demand for smart automation systems in postharvest technology, particularly in the postharvest process of garlic production, is high. In this study, we introduce an artificial intelligence (AI)-integrated robot system for grading and sorting of garlics after root trimming. We developed a robot system that automatically grades and sorts root-trimmed garlics based on image analysis using a deep learning model, which is equipped with a deep convolutional neural network (CNN). The robot system consists of three main modules: image acquisition module (IAM), image processing module (IPM), and garlic sorting module (GSM). The IAM captures a live video of garlic roots using an independent light-emitting diode (LED) and a web camera connected to a computer. The IPM consists of garlic image processing software (GIPS) installed on a Windows 10 computer. GIPS imports videos, detects the shape of garlic bulbs, and predicts the garlic class using the deep learning model. When a garlic bulb shape is detected, the image is preprocessed, and the deep learning model is automatically activated to predict the garlic class. The GSM physically separates garlics into three containers for good, bad, and scratched classes based on a previous predicted result. A total of 800 root-trimmed garlics were tested and evaluated in nine experiments. The processing time to predict classes from a garlic image on a regular laptop computer (Intel® core™ i7-8650U 1.9 GHz CPU, with 16 Gb RAM) was 0.19–0.58 s. The median time to trim and sort one garlic was 11 s (in a setting with three prediction replications), and the overall grading accuracy for the three classes was 89%. The individual predicted precision for the good, bad, and scratched classes was 95%, 68%, and 100%, respectively. Given that the robot system was robust and inexpensive, it has the potential to be integrated into a garlic root trimming machine to develop a fully automatic garlic root trimming and sorting robot.