Robotic Sorting Research Articles

Colour sorting ROS-based robot evaluation under different lights and camera angles

Automated colour sorting, aided by mobile robots, is widely prevalent in the current manufacturing industry. Obstacles, such as fluctuating light conditions and camera angles, frequently hinder this procedure. Creating a colour sorting robot is a complex and time-consuming task, especially due to the vulnerability of the RGB colour space to detection errors in extreme brightness or darkness. In response to these concerns, we introduce a mobile robot that operates on the robot operating system (ROS) platform and incorporates OpenCV. This robot employs the hue, saturation, and value (HSV) colour space model for its image processing capabilities in recognising the colours and Welzl’s algorithm for the ball’s diameter estimation. The robot’s performance was assessed across various luminous fluxes and camera tilt angles. It demonstrated exceptional performance at 64 lm and a tilt angle of 40 degrees, achieving an average accuracy of 87.5% for detecting the colour of the ball, and 81.25% for determining its location based on colour. For the ball’s diameter estimation, it was found that the best estimation was received at 64 lm and 30 degrees, with both 96.32%.

Scheme evaluation method of coal gangue sorting robot system with time-varying multi-scenario based on deep learning

The rapid advancement of artificial intelligence and robot technology has spurred the proposal and innovation of a coal gangue sorting robot system (CGSRS) paradigm. The time-varying raw coal flow (TVRCF) with multi-scene and full working conditions affects the gangue queue. Configuring the CGSRS scheme correctly is combative. The field environment puts forward higher requirements for the time complexity of the CGSRS multi-task allocation strategy. Therefore, this paper proposes a scheme evaluation method of the CGSRS with time-varying multi-scenario based on deep learning. Firstly, the gangue queue data set of multi-scene and full-condition TVRCF was obtained according to the belt speed, the maximum coal flow, and the uncorrelated nonlinear changes of coal flow and gangue content. The CGSRS scheme is established based on robot number and rule combination, and the multi-task allocation strategy is adjusted to generate the labels of the gangue queue. Then, the RGB sample set is established based on the labels of the gangue queue. The CGSRS scheme evaluation model is trained based on DenseNet. Finally, the CGSRS scheme evaluation method was designed to realize the prediction of a random gangue queue. In this paper, the CGSRS scheme evaluation model, the stability of the solution, and the comparison of methods are carried out. Experimental results show that the solution of the CGSRS scheme evaluation model is accurate and stable. The time complexity is significantly reduced and very stable. The CGSRS scheme evaluation method is applied to the CGSRS multi-task allocation problem, and the stability of the solution is not affected by the data. It is significantly better than the multi-task allocation strategy. The proposed method is the first attempt to apply deep learning to a multi-task allocation problem in CGSRS.

Development of a Color-Based Image Recognition System for Robotic Sorting and Picking

This research presents the development of an automated object sorting and picking system using a robotic arm controlled by colour-based image recognition. The system is designed to enhance efficiency and accuracy in manufacturing processes by eliminating the need for manual sorting. A Dobot Magician robotic arm, an Arduino microcontroller, a conveyor belt, a photoelectric sensor, and a camera are integrated to achieve this goal. Colour segmentation is implemented using the HSV colour space, enabling the system to accurately classify objects based on colour. Experimental results demonstrate the system's ability to successfully sort objects of three colours in a random sequence with 100% accuracy over ten trials.

Research on efficient matching method of coal gangue recognition image and sorting image

When the coal gangue sorting robot sorts coal gangue, the position of the target coal gangue will change due to belt slippage, deviation, and speed fluctuations of the belt conveyor. This will cause the robotic to fail in grasping or miss grasping. We have developed a solution to this problem: the IMSSP-Net two-stage network gangue image fast matching method. This method will reacquire the target gangue position information and improve the robot’s grasping precision and efficiency. In the first stage, we use SuperPoint to guarantee the scene adaptability and credibility of feature point extraction. We have enhanced Superpoint’s ability to detect feature points further by using the improved Multi-scale Retinex with Color Restoration enhancement algorithm. In the second stage, we introduce SuperGlue for feature matching to improve the robustness of the matching network. We eliminated erroneous feature matching point pairs and improved the accuracy of image matching by adopting the PROSAC algorithm. We conducted image matching comparison experiments under different object distances, scales, rotation angles, and complex conditions. The experimental platform adopts the double-manipulator truss-type coal gangue sorting robot independently developed by the team. The matching precision, recall, and matching time of the method are 98.2%, 98.3%, and 84.6ms, respectively. The method can meet the requirements of efficient and accurate matching between coal gangue recognition images and sorting images.

Occlusive target recognition method of sorting robot based on anchor-free detection network

ABSTRACT The rapid development of industry intelligence has promoted the importance of sorting robots, but the shortcomings of sorting tasks are also gradually obvious. The target detection of the sorting robot in the process of sorting and grasping is affected by the anchor frame setting, and its grasping efficiency and recognition effect are low. Therefore, a detection network based on an anchor frame-free regression algorithm is proposed in this study, and a positioning and grasping method of the sorting robot based on the Deep Belief Network (DBN) algorithm is proposed. The algorithm experiment shows that the prediction accuracy of the proposed anchor-free detection network in the PASCAL VOC dataset reaches 92.91, significantly higher than that of other detection networks. In addition, the sorting robot was researched and designed with high accuracy in occlusion target recognition. When the occlusion area reaches 70%, the accuracy rate is as high as 81.26%, which is far higher than other detection schemes. The above results show that in improving the sorting robot’s target recognition ability, the anchor-free detection network can significantly improve its recognition accuracy. At the same time, introducing the DBN network can improve the robot’s recognition ability for occluded targets, which is of great significance to the development of the sorting robot and the intelligent industry.

Analysis of Business Process Re-Engineering in the Implementation of Robotic Sorting Machines in the Middle Mile Process at PT Pos Indonesia (Persero)

PT Pos Indonesia faces challenges in improving courier service business processes due to business change trends. This study aims to identify and analyze inefficiencies in the middle mile supply chain business process of PT Pos Indonesia Processing Centre Surabaya and determine the number of robotic mega bots needed to improve efficiency. The research method uses quantitative information, observations, interviews, computer simulations, Value Stream Mapping process flow charts, and Business Process Reengineering concepts. The results showed that the use of mega bot robots had a positive and significant impact on the company's reputation, and the sorting process carried out using robotic mega bots was appropriate because it had a significant impact on the effectiveness and efficiency of business process improvements at PT Pos Indonesia (Persero).

Design of a robotic gripper for casting sorting robots with rigid–flexible coupling structures

AbstractIn order to solve the problem of the insufficient adaptability of the current small- and medium-sized casting sorting robot gripper, we have designed a casting sorting robot bionic gripper with rigid–flexible coupling structures based on the robot topology theory. The second-order Yeoh model was used to statically model the clamping belt in the gripper to derive the relationship between the external input air pressure and the bending angle of the driving layer, and the feasibility of multiangle bending of the driving layer was verified by finite element analysis. The maximum gripping diameter of the gripper is 140 mm, and in order to test the adaptive gripping ability of the gripper, a prototype of the casting sorting robot gripper is prepared, and the pneumatic control system and human–machine interface of the gripper are designed. After several experimental analyses, the designed casting sorting robot gripper is characterized by strong adaptability and high robustness, with a maximum load capacity of 930 g and a maximum wrap angle of 296°, which can complete the gripping operation within 1 s, and the comprehensive gripping success rate reaches 96.4%. The casting sorting robot gripper designed in the paper can provide a reference for the design and optimization of various types of shaped workpiece gripping manipulators.

Dis//assemblages of AI: repair labor and resistance in the automated workplace

ABSTRACT Breakdowns and repairs are seldom present in dominant narratives about Artificial Intelligence that depict these technologies as a seamless source of optimization. Yet, AI systems are replete with moments of downtime and delayed action that feminist science and technology scholars have theorized as openings to reimagine technological systems. The following article engages the concept of friction to contend with the contradictory ways in which workers tasked with the continual labor of sustaining Artificial Intelligence technologies simultaneously transform parts of the system. Drawing upon seven months of ethnographic research at a recycling facility integrated with various forms of automation, including AI-powered sorting robots, we show how moments of machine malfunction provide fleeting windows for workers to bring to life alternative AI configurations. Our analytical and observational focus on glitches and breakdown allow us to make two related contributions to the study of the automated workplace: (1) We propose friction as a lens that aids in navigating the messy paradox of reproduction and resistance inherent to repair labor and (2) empirically demonstrate how friction emerges from workers’ material practices that contest datafied technological imaginaries through embodied science and move against pressures to optimize for efficiency and revenue by prioritizing communal care.

Human Skin-Mimicking Ionogel-Based Electronic Skin for Intelligent Robotic Sorting.

Creating bionic intelligent robotic systems that emulate human-like skin perception presents a considerable scientific challenge. This study introduces a multifunctional bionic electronic skin (e-skin) made from polyacrylic acid ionogel (PAIG), designed to detect human motion signals and transmit them to robotic systems for recognition and classification. The PAIG is synthesized using a suspension of liquid metal and graphene oxide nanosheets as initiators and cross-linkers. The resulting PAIGs demonstrate excellent mechanical properties, resistance to freezing and drying, and self-healing capabilities. Functionally, the PAIG effectively captures human motion signals through electromechanical sensing. Furthermore, a bionic intelligent sorting robot system is developed by integrating the PAIG-based e-skin with a robotic manipulator. This system leverages its ability to detect frictional electrical signals, enabling precise identification and sorting of materials. The innovations presented in this study hold significant potential for applications in artificial intelligence, rehabilitation training, and intelligent classification systems.

The “load balancing” and “adaptive task completion” algorithms implementation on a pharmaceutical sorting conveyor line

The subject matter of the article is advantages and disadvantages of using the "load balancing" and "adaptive task execution" algorithms on the pharmaceutical sorting conveyor. The goal of the work is to analyze the Load Balancing (LB) and Adaptive Task Completion (ATC) sorting algorithms application on pharmaceutical sorting conveyor lines. And also, to consider their advantages and disadvantages in the context of optimizing sorting processes for pharmaceutical products. The following tasks were solved in the article: analysis of the latest research and publications on the research topic; study of the specifics of the application of algorithms for sorting pharmaceutical products on conveyor lines to balance the load on sorting robots; analysis of the Load Balancing (LB) algorithm from the point of view of its application for load balancing on conveyor lines with sorting robots in the field of pharmaceuticals; development of the Load Balancing algorithm for load balancing on conveyor lines with sorting robots in the pharmaceutical field; analysis of the Adaptive Task Completion (ATC) algorithm from the point of view of its application for load balancing on conveyor lines with sorting robots in the field of pharmaceuticals; development of a general Adaptive Task Completion (ATC) algorithm for load balancing on conveyor lines with sorting robots in the pharmaceutical field; highlighting the advantages and disadvantages of the specified algorithms. The following methods were used: simulation modeling methods. The following results were obtained – the advantages and disadvantages of Load Balancing and Adaptive Task Completion algorithms for load balancing on conveyor lines with sorting robots in the pharmaceutical field are formulated; recommendations are given, in which situations, under which conditions, which algorithm should be used. Conclusions. In the context of the pharmaceutical industry, where not only speed but also sorting accuracy is important, Adaptive Task Completion may be a better choice because it takes into account different criteria and dynamically responds to changing conditions. On the other hand, Load Balancing can be effective in environments where sorting speed is paramount, and tasks are homogeneous.

Research on dynamic target steady and quasi-grasping method of coal gangue sorting robot based on global planning and local vision

ABSTRACT Using robots instead of manual sorting of coal gangue can significantly improve the sorting rate. However, the stable grasping of the target gangue by the manipulator is the key to the efficient sorting of robot. The coal gangue on the belt conveyor has the characteristics of high speed and high quality. In the process of grasping, the velocity difference between the end of the manipulator and the coal gangue will cause impact load. In addition, the belt speed fluctuation and deviation also lead to the inaccurate location of the gangue, resulting in excessive position error of the manipulator during grasping, leading to grasping failure. For this reason, a synchronous tracking trajectory planning method of manipulator based on global planning and local visual servo is proposed in this paper. In this method, the manipulator is controlled by the BPNG method to reach the top of the target gangue quickly, and the accurate location of the target gangue is completed by the extended FDSST tracking algorithm. The manipulator uses the “position-velocity-acceleration” three-loop PID square method to realize the synchronous tracking trajectory planning of the target gangue. The experimental results show that this method can achieve stable and accurate grasping of target gangue by manipulator.

Intelligent robotic control system based on computer vision technology

Computer vision is a kind of simulation of biological vision using computers and related equipment. It is an important part of the field of artificial intelligence. Its research goal is to make computers have the ability to recognize three-dimensional environmental information through two-dimensional images. Computer vision is based on image processing technology, signal processing technology, probability statistical analysis, computational geometry, neural network, machine learning theory and computer information processing technology, through computer analysis and processing of visual information.The article explores the intersection of computer vision technology and robotic control, highlighting its importance in various fields such as industrial automation, healthcare, and environmental protection. Computer vision technology, which simulates human visual observation, plays a crucial role in enabling robots to perceive and understand their surroundings, leading to advancements in tasks like autonomous navigation, object recognition, and waste management. By integrating computer vision with robot control, robots gain the ability to interact intelligently with their environment, improving efficiency, quality, and environmental sustainability. The article also discusses methodologies for developing intelligent garbage sorting robots, emphasizing the application of computer vision image recognition, feature extraction, and reinforcement learning techniques. Overall, the integration of computer vision technology with robot control holds promise for enhancing human-computer interaction, intelligent manufacturing, and environmental protection efforts.

Automatic loading method for robot guided by 3D vision for scattered and stacked shoe soles

This paper primarily introduces a method for the automated feeding of scattered stacked shoe soles using a 3D visual-guided robot. Initially, addressing issues like slow speed in pose estimation and poor robustness during the robot sorting and feeding process, we introduce an enhanced pose estimation algorithm. This algorithm combines the improved Super 4-Point Congruent Sets (Super 4PCS) with the Truncated Least Squares Semidefinite Relaxation (TEASER) algorithm, significantly boosting the speed and robustness of pose estimation during sole sorting, and achieving precise target pose estimation. Building upon this foundation, we present a sorting strategy for disordered stacked shoe soles. This strategy integrates spatial positional information of each sole, employing multi-objective decision-making and recognition algorithms to determine optimal grasping targets. Finally, an experimental platform for automated sole feeding is designed to validate the proposed method. The experimental results indicate that the pose estimation method proposed in this paper achieves an average distance error of 2.04 mm and an average angular error of 2.72°, with the robot’s average success rate in grasping reaching 97.08%. Moreover, the average processing time of the vision algorithm is only 1.34 s, demonstrating good efficiency, precision, and robustness. This method effectively meets the automated feeding needs of scattered and stacked shoe soles in the actual production processes of shoemaking enterprises.

Fine-Grained Detection Model Based on Attention Mechanism and Multi-Scale Feature Fusion for Cocoon Sorting

Sorting unreelable inferior cocoons during the reeling process is essential for obtaining high-quality silk products. At present, silk reeling enterprises mainly rely on manual sorting, which is inefficient and labor-intensive. Automated sorting based on machine vision and sorting robots is a promising alternative. However, the accuracy and computational complexity of object detection are challenges for the practical application of automatic sorting, especially for small stains of inferior cocoons in images of densely distributed cocoons. To deal with this problem, an efficient fine-grained object detection network based on attention mechanism and multi-scale feature fusion, called AMMF-Net, is proposed for inferior silkworm cocoon recognition. In this model, fine-grained object features are key considerations to improve the detection accuracy. To efficiently extract fine-grained features of silkworm cocoon images, we designed an efficient hybrid feature extraction network (HFE-Net) that combines depth-wise separable convolution and Transformer as the backbone. It captures local and global information to extract fine-grained features of inferior silkworm cocoon images, improving the representation ability of the network. An efficient multi-scale feature fusion module (EMFF) is proposed as the neck of the object detection structure. It improves the typical down-sampling method of multi-scale feature fusion to avoid the loss of key information and achieve better performance. Our method is trained and evaluated on a dataset collected from multiple inferior cocoons. Extensive experiments validated the effectiveness and generalization performance of the HFE-Net network and the EMFF module, and the proposed AMMF-Net achieved the best detection results compared to other popular deep neural networks.

Design and Analysis of Reconfigurable Origami-Based Vacuum Pneumatic Artificial Muscles for Versatile Robotic System.

In this study, a vacuum-based modular actuator system named reconfigurable origami-based vacuum pneumatic artificial muscles (ROV-PAMs) is presented. The system consists of six types of actuating modules and three types of fluidic supporting modules each embedded with magnet-based connectors so that the modules can be assembled to modify the system behavior. The module can be used in a myriad of ways, including extending their working range, creating complex geometries upon deformation, and cooperating to improve overall performance. A simple analytical model for the actuating modules is derived based on the law of conservation of energy, and the model is verified experimentally which shows that this intuitive model can provide a reasonable prediction of performance. A block sorting robot is built using three different types of actuating modules with multiple fluidic supporting modules, and the robot shows that it is possible to flexibly and easily assemble modules to build a robot capable of completing diverse tasks. The ROV-PAM module and its concept can be applied to realize robotic designs, which can be altered on-the-fly to adjust its functionality to meet the evolving requirements required for truly flexible automation.

A new CNN model integrated in onion and garlic sorting robot to improve classification accuracy

The profit share of the vegetable market, which is quite large in the agricultural industry, needs to be equipped with the ability to classify types of vegetables quickly and accurately. Some vegetables have a similar shape, such as onions and garlic, which can lead to misidentification of these types of vegetables. Through the use of computer vision and machine learning, vegetables, especially onions, can be classified based on the characteristics of shape, size, and color. In classifying shallot and garlic images, the CNN model was developed using 4 convolutional layers, with each layer having a kernel matrix of 2x2 and a total of 914,242 train parameters. The activation function on the convolutional layer uses ReLu and the activation function on the output layer is softmax. Model accuracy on training data is 0.9833 with a loss value of 0.762.

AI-DRIVEN WASTE MANAGEMENT SYSTEMS: A COMPARATIVE REVIEW OF INNOVATIONS IN THE USA AND AFRICA

The burgeoning challenges of waste management have propelled the integration of artificial intelligence (AI) into waste management systems, aiming to enhance efficiency, sustainability, and environmental impact. This abstract delves into the comparative review of AI-driven waste management innovations in the USA and Africa, illuminating the divergent strategies employed to address distinct contextual demands. In the USA, where waste management infrastructures are relatively advanced, AI technologies are leveraged to optimize waste collection routes, automate sorting processes, and enhance recycling efficiency. Machine learning algorithms analyze historical data to predict waste generation patterns, enabling municipalities to allocate resources more effectively. Additionally, robotic sorting systems equipped with computer vision contribute to the accurate segregation of recyclables, reducing contamination and promoting a circular economy. Conversely, in Africa, where waste management infrastructures may be less developed, AI applications prioritize scalable and adaptable solutions. Mobile applications powered by AI facilitate crowd-sourced waste reporting, enabling citizens to actively participate in waste management efforts. Furthermore, sensor-equipped smart bins optimize collection routes in real-time, improving resource utilization. The emphasis on community engagement and decentralized solutions reflects the unique challenges and opportunities present in African waste management contexts. Despite these regional disparities, common themes emerge, such as the role of data analytics, automation, and community involvement in shaping effective waste management systems. The comparative analysis underscores the importance of tailoring AI-driven innovations to the specific socio-economic and infrastructural landscapes of each region. Ultimately, understanding the nuanced approaches in the USA and Africa can inform a more holistic and globally adaptable framework for AI-driven waste management systems.
 Keywords: Al, Waste Management, USA, Africa, Innovation, Sanitation.

Cognitive Enhancement of Robot Path Planning and Environmental Perception Based on Gmapping Algorithm Optimization

In the logistics warehouse environment, the autonomous navigation and environment perception of the logistics sorting robot are two key challenges. To deal with the complex obstacles and cargo layout in a warehouse, this study focuses on improving the robot perception and navigation system to achieve efficient path planning and safe motion control. For this purpose, a scheme based on an improved Gmapping algorithm is proposed to construct a high-precision map inside a warehouse through the efficient scanning and processing of environmental data by robots. While the improved algorithm effectively integrates sensor data with robot position information to realize the real-time modeling and analysis of warehouse environments. Consequently, the precise mapping results provide a reliable navigation basis for the robot, enabling it to make intelligent path planning and obstacle avoidance decisions in unknown or dynamic environments. The experimental results show that the robot using the improved Gmapping algorithm has high accuracy and robustness in identifying obstacles and an effectively reduced navigation error, thus improving the intelligence level and efficiency of logistics operations. The improved algorithm significantly enhances obstacle detection rates, increasing them by 4.05%. Simultaneously, it successfully reduces map size accuracy errors by 1.4% and angle accuracy errors by 0.5%. Additionally, the accuracy of the robot’s travel distance improves by 2.4%, and the mapping time is reduced by nine seconds. Significant progress has been made in achieving high-precision environmental perception and intelligent navigation, providing reliable technical support and solutions for autonomous operations in logistics warehouses.

RESEARCH ON THE APPLICATION OF THE ROUND ROBIN ALGORITHM FOR LOAD BALANCING ON CONVEYOR LINES IN THE PHARMACEUTICAL INDUSTRY

Currently, the pharmaceutical industry is experiencing rapid development, and the implementation of modern technologies is becoming a critical aspect of ensuring high production efficiency. Research into load balancing on conveyor lines using sorting robots represents a key element of this optimization [1-3].

Identification of fruit using a flexible tactile sensor array

This article aims to address the issue of low recognition accuracy in existing sorting robots caused by lighting, occlusion, and environmental factors. A fruit recognition method based on a flexible tactile sensor array is described. This method enables the robot to directly perceive the attributes of the objects and identify fruits using a flexible gripper, facilitating intelligent sorting. A novel flexible tactile sensor array is utilized to construct a flexible hand tactile information acquisition platform, which collects tactile time series data for the fruits. Principal component analysis is then employed for dimensionality reduction, followed by the development of an improved particle swarm optimization for the support vector machine model. Through an experimental study, the optimized model is compared with four other models, demonstrating better classification performance. The optimized model achieves an average tactile classification accuracy of up to 98.10% for the five types of fruits. A comparison between the improved optimization algorithm, genetic algorithm, and grid search algorithm reveals the superior optimization performance of the new approach. In the future, this method is expected to be implemented in industrial sorting robots for intelligent sorting on automatic production lines. Furthermore, the algorithm will be further refined to enhance the classification accuracy and efficiency.