Logistics Automation Research Articles

A Cable-driven Parallel Robot for High-rack Logistics Automation

A Cable-driven Parallel Robot for High-rack Logistics Automation

A Study on Strategic Approaches to Efficient Sustainable Logistics

With the fast evolution of eCommerce, logistics is vitally involved in ensuring-with due efficiency-the movement of goods from the seller to the buyer. In an environmental sense, such efficiency would mean cost and time optimization, matching expectations of swift, timely product delivery. However, with growing environmental concerns, the need for sustainable logistics becomes no less critical. This research will dwell on strategic paths to balancing logistics efficiency with sustainability, leveraging state-of- the-art technologies such as Artificial Intelligence and automated route optimization. The study further proposes the integration of electric vehicles and green logistics practices in the sector of e-commerce, especially in the Delhi NCR region. It will quantify, through qualitative analysis, the effect that these techniques have on the environmental performance of sustainable logistics-such as carbon footprint reduction and general economic efficiency. Key Words eCommerce logistics, Sustainable logistics, Artificial intelligence (AI), Automation in logistics, Electric vehicles, Carbon emissions reduction, Route optimization, Eco-friendly packaging, Logistics efficiency, Environmental sustainability

Practical experiences of digitalization and automation in logistics

Recently, digitalization and automation have gained more and more space, which has long been felt in the field of logistics. Although we can find examples of both in almost every major company, we can state that each company uses them to a different extent and in different areas. In addition to the fact that we find many different examples, it is typical that they are specifically customized and therefore their introduction requires additional expenditures and changes at other companies. What can and to what extent is it reasonable to digitize and automate? What are the prerequisites for these to be implemented cost-effectively and in the most targeted manner

Logistics Automation as a Smart Factory Enabler

Logistics Automation as a Smart Factory Enabler

Rising need of Automation in Logistics and Supply Chain Industry

Rising need of Automation in Logistics and Supply Chain Industry

Enabling Logistics Automation in Nanofactory: Cobalt Phosphide Embedded Metal-Organic Frameworks for Efficient Electrocatalytic Nitrate Reduction to Ammonia.

Electrocatalytic nitrate reduction reaction (NitRR) in neutral condition offers a promising strategy for green ammonia synthesis and wastewater treatment, the rational design of electrocatalysts is the cornerstone. Inspired by modern factory design where both machines and logistics matter for manufacturing, it is reported that cobalt phosphide (CoP) nanoparticles embedded in zinc-based zeolite imidazole frameworks (Zn-ZIF) function as a nanofactory with high performance. By selective phosphorization of ZnCo bimetallic zeolite imidazole framework (ZnCo-ZIF), the generated CoP nanoparticles act as "machines" (active sites) for molecular manufacturing (NO3 - to NH4 + conversion). The purposely retained framework (Zn-ZIFs) with positive charge promotes logistics automation, i.e., the automatic delivery of NO3 - reactants and timely discharge of NH4 + products in-and-out the nanofactory due to electrostatic interaction. Moreover, the interaction between Zn-ZIF and CoP modulates the Co sites into electron insufficient state with upshifted d-band center, facilitating the reduction/hydrogenation of NO3 - to ammonia and restricting the competitive hydrogen evolution. Consequently, the assembled CoP/Zn-ZIF nanofactory exhibits superior NitRR performances with a high Faraday efficiency of ≈97% and a high ammonia yield of 0.89mmol cm-1 h-1 in neutral condition, among the best of reported electrocatalysts. The work provides new insights into the design principles of efficient NitRR electrocatalysts.

Design of Rural E-commerce Logistics Information Management System Based on Spring Boot

With the improvement of science and technology, the Internet has more and more affected people's daily life. People can stay at home and learn the latest information, movies, animations, music, etc. through the Internet. They can also buy goods, learn knowledge, and even find jobs through the Internet. It can be seen that the development of the Internet has brought great convenience to people's lives, and many industries have been born. As an emerging Internet industry, e-commerce has provided a large number of job opportunities for the market. Merchants can promote through e-commerce platforms, and customers can purchase goods through the platform. To ensure the normal and orderly flow of this business activity, efficient logistics systems are indispensable. With the rise of the concept of smart logistics, it not only requires employees to have professional theoretical knowledge and management service knowledge, but also professional technical knowledge such as electronic information technology. Designing an efficient logistics information management system requires cultivating smart logistics talents. E-commerce logistics, through information management systems, can not only achieve intelligent management and control of the logistics process, improve logistics efficiency and reduce logistics costs, but also enhance the automation and intelligence level of logistics, promoting the development of the logistics industry towards a more intelligent, efficient, and sustainable direction.

Automation and digitalisation of transport forwarding services or logistics

With the spread of globalisation, the transition of various states to common standards, including in the field of international freight transportation, becomes especially relevant. The purpose of this work is to assess the effectiveness of logistics in examples of countries that are members of the Commonwealth of Independent States, as well as the USA, to compare them and identify key trends in the development of the field of road freight transport. In this work, methods of statistical analysis and forecasting were applied, graphs of the logistics efficiency index of the countries studied were drawn up and the most promising international transport and logistics projects were identified. As a result, logistics performance indices were obtained in the context of indicators from 2007 to 2023, and the average Commonwealth indicator was determined and compared with a similar indicator of the USA. Also, key innovations in the field of transport services were considered, allowing Western companies to have a significant advantage in the development of logistics efficiency. The prospects of Commonwealth countries in the leading Eurasian transport corridors and the advantages of such integration in comparison with intra-union projects were determined.

Design of Logistics Sorting Algorithm Based on Deep Learning and Sampling Evaluation

The advancement of automation and Internet of Things technology has bolstered the automation process in the logistics sector. To address the challenge of localizing and generating grasping positions for intelligent robots in logistics sorting, this study developed an algorithm for item localization. The algorithm relies on enhanced YOLOv3 target detection and instance segmentation technologies to design a position generation algorithm for the robotic arm, which was further refined using sampling evaluation. The experimental results showed that the research-improved target detection model performed better on different datasets in terms of F1 value, accuracy and Area under the Curve (AUC) metrics, with the highest values of 95.77%, 94.05%, and 91.30%, respectively, which was effective in localizing document-like parcels. Meanwhile, the instance segmentation algorithm with fused features took significantly lower values than other target detection algorithms in terms of average absolute value error and root mean square error. The accuracy rate and all-class average precision value were higher than other target detection models, and the fluctuation of the value taken was smaller, which was suitable for logistics parcel localization. The position generation model, based on a sampling evaluation, yielded significantly different values compared to other algorithms. The relative position error and absolute trajectory error indexes were all below 0.4. The combined indexes of grasping accuracy and error indicate the superior performance of the research-designed algorithms. They can effectively enhance the sorting effects of real logistics scenarios. This research contributes to the improvement of the automated sorting system through the use of visual robotic arm technology. Additionally, it encourages the development of logistics automation and the establishment of intelligent logistics factories.

Compliance checking for cross-border construction logistics clearance using blockchain smart contracts and oracles

ABSTRACT This research aims to develop a deployable blockchain smart contract and oracle model for automated and transparent customs compliance checking in cross-border construction logistics. A scene analysis is conducted to extract requirements, and then a blockchain-enabled model is prototyped and tested in two case studies. We find that the prototype is feasible, and the performance is satisfactory in terms of compliance checking time (t = 1523–1530 s), accuracy (a = 55.9% to 100%), private data collection (p = 100%), and information transparency (l = 0.787–1). The innovation of this research is to combine blockchain smart contracts and oracles to achieve automation and transparency in cross-border construction logistics.

Effortless Logistics Automation - SAP Logistics Process Automation with UiPath RPA

In the rapidly evolving landscape of business process optimization, the integration of Robotic Process Automation (RPA) with SAP systems has emerged as a transformative solution. This paper explores the synergy between UiPath RPA and SAP, shedding light on the potential for enhanced efficiency, accuracy, and scalability in enterprise processes. The abstract delves into the key concepts, challenges, and benefits associated with SAP logistics process automation using UiPath, offering insights for organizations seeking to streamline their operations through advanced automation

Digitization of Macro-Logistics Systems in Ukraine

This article delves into the multifaceted realm of digitizing macrologistical systems, scrutinizing their nature, influencing factors, and emerging trends. It intricately explores how the integration of digital technologies empowers transport enterprises to deliver superior logistics services while concurrently curtailing costs. A comparative analysis meticulously dissects various facets of the macrologistical system, elucidating the interplay and correlation of its elements within the realms of commodity-money and contractual relationships. The study accentuates the pivotal role of the macro-logistics system in supply chain management.In scrutinizing Ukraine’s Logistics Performance Index scores vis-à-vis EU countries, the authors attribute the disparity to the enduring conflict. A notable European logistics market trend is identified: a surge in digitization investments by individual companies and the establishment of cohesive online platforms. The paper advocates for the integration of automation in transport logistics, particularly in the backdrop of a wartime economy.European standards for a contemporary transport management system are analyzed, culminating in the selection of the CargoClix logistics platform. The article expounds on the platform’s merits in adeptly digitizing the operations of transport and logistics enterprises, offering real-time tracking crucial for prompt responses amid wartime exigencies.The article concludes by substantiating findings with pertinent diagrams and tables. In essence, the digitization of macrologistical systems entails a holistic amalgamation of digital technologies, data analytics, and decision-making solutions for information management in logistics and supply chain operations. This transformative journey, leveraging technologies such as the Internet of Things, unmanned technologies, identification technologies, blockchain, big data, robotic systems, artificial intelligence, and neural networks, aspires to amplify efficiency, visibility, and coordination throughout the supply chain. This paradigm shift is pivotal for evaluating the efficacy of transport and logistics companies, determining their competitive stance in today’s market. Future research trajectories encompass formulating strategies for national companies operating in the domestic logistics market, identifying and implementing key competitive advantages, and devising measures and tools to ensure efficiency during wartime.

Current state of process automation in internal logistics: a Germany-wide expert survey

Current state of process automation in internal logistics: a Germany-wide expert survey

A Systematic Literature Review on the Application of Automation in Logistics

Background: in recent years, automation has emerged as a hot topic, showcasing its capacity to perform tasks independently, without constant supervision. While automation has witnessed substantial growth in various sectors like engineering and medicine, the logistics industry has yet to witness an equivalent surge in research and implementation. Therefore, it becomes imperative to explore the application of automation in logistics. Methods: this article aims to provide a systematic analysis of the scientific literature concerning artificial intelligence (AI) and automation in logistics, laying the groundwork for robust and relevant advancements in the field. Results: the foundation of automation lies in cutting-edge technologies such as AI, machine learning, and deep learning, enabling self-problem resolution and autonomous task execution, reducing the reliance on human labor. Consequently, the implementation of smart logistics through automation has the potential to enhance competitiveness and minimize the margin of error. The impact of AI and robot-driven logistics on automation in logistics is profound. Through collaborative efforts in human–robot integration (HRI), there emerges an opportunity to develop social service robots that coexist harmoniously with humans. This integration can lead to a revolutionary transformation in logistics operations. By exploring the scientific literature on AI and automation in logistics, this article seeks to unravel critical insights into the practical application of automation, thus bridging the existing research gap in the logistics industry. Conclusions: the findings underscore the impact of artificial intelligence and robot-driven logistics on improving operational efficiency, reducing errors, and enhancing competitiveness. The research also provided valuable insights into the applications of various automation techniques, including machine learning and deep learning, in the logistics domain. Hence, the study’s insights can guide practitioners and decision makers in implementing effective automation strategies, thereby improving overall performance and adaptability in the dynamic logistics landscape. Understanding these foundations can pave the way for a future where automation and human expertise work hand in hand to drive logistics toward unparalleled efficiency and success.

Deep Learning for Precise Robot Position Prediction in Logistics

This study presents an interdisciplinary investigation at the nexus of mechanical engineering and computer science, aimed at advancing the field of logistics automation. In response to the escalating demands of global cargo transportation, the integration of these disciplines assumes paramount importance. Conducted within the domain of Dortmund University of Technology’s Material Flow and Warehousing Chair, this research focuses on the precise control of robots, a task contingent on accurate positional information. Leveraging a controlled internal logistics precinct, the study delves into the transformation of raw sensor data, comprising accelerometers, gyroscopes, and magnetometers, into precise position predictions. This process entails meticulous data preprocessing, encompassing synchronization and calibration procedures, yielding crucial parameters such as absolute velocity and accelerations along both parallel and perpendicular axes. The study employs deep learning, specifically a 2D Convolutional Neural Network (2D-CNN), for predictive modeling. This architecture excels in extracting intricate spatial features from sensor data. Training is conducted under the guidance of an Asymmetric Gaussian loss function, custom-tailored to accommodate the idiosyn- crasies of real-world sensor data. The results evince the efficacy of this approach, evidenced by remarkably low mean squared errors in predicting robot positions. Beyond its immediate applications in logistics automation, this research underscores the potential of interdisciplinary collaboration in addressing complex sensor data challenges.

Four-DOF Robotic Arm System for Goods Transport

Paper presents the design, development, and implementation of a Four-Degree-of-Freedom (Four-DOF) Robotic Arm System tailored for the purpose of efficient goods transport. The increasing demand for automation in logistics and industrial sectors has prompted the need for versatile robotic solutions capable of manipulating objects in various environments. The proposed robotic arm system addresses this need by offering four independent degrees of freedom, enabling it to perform intricate tasks involved in goods transport. The system's mechanical design incorporates four joints, each contributing to a specific movement: lifting, lowering, rotating, and extending. These movements provide the necessary flexibility to manoeuvre in complex spaces, making it suitable for applications within warehouses, factories, and distribution canters. The design process involved careful consideration of structural integrity, weight distribution, and motion optimization to ensure safe and precise operation. In conjunction with the mechanical design, the robotic arm is equipped with advanced sensing and control mechanisms. Sensor arrays, including proximity sensors and cameras, enable the arm to perceive its environment and detect objects, ensuring accurate object detection and collision avoidance. The control system, driven by a combination of microcontrollers and software algorithms, orchestrates the movement of each joint to achieve seamless coordination and efficient goods transport. Real-world implementation of the Four-DOF Robotic Arm System showcases its ability to streamline goods transport operations. By automating tasks that previously required manual intervention, the system enhances operational efficiency, reduces labour costs, and minimizes the risk of human error. Furthermore, its adaptability to diverse environments underscores its potential to revolutionize the logistics industry.

ONE BELT, ONE ROAD PROJECT: THE IMPACT OF SMART TECHNOLOGIES ON INFRASTRUCTURE AND LOGISTICS

This article explores the impact of the smart economy on the infrastructure and logistics of the One Belt, One Road (BRI – Belt and Road Initiative) initiative. The integration of smart technologies into the project has the potential to transform traditional systems, improve communication, increase efficiency and contribute to sustainable development. Purpose. This study aims to fill the existing knowledge gaps and shed light on the impact of the smart economy on the infrastructure and logistics of the One Belt, One Road project. By examining the opportunities, challenges and implications, this study aims to contribute to the understanding and successful implementation of smart technologies in this ambitious initiative. Methodology. The article uses the methods of quantitative empirical research, statistical analysis and visualisation, analysis and evaluation of the analytical assessment of tools for forming an innovative logistics network within the framework of the One Belt, One Road project. Results. The article examines the impact of the smart economy on connectivity, efficiency and sustainability, and highlights the perspectives and recommendations of different stakeholders. It emphasises the importance of cooperation between governments, businesses and local communities for successful implementation. The findings suggest that while the smart economy offers immense opportunities, challenges such as the digital divide, data protection and investment requirements need to be addressed. The article concludes with recommendations to foster cooperation, develop supportive policies, invest in digital infrastructure and prioritise sustainability. By following these recommendations, the One Belt, One Road project can effectively harness the transformative potential of the smart economy and create a sustainable and inclusive future. Practical implications. The materials can be used to plan a development strategy, taking into account new technologies and the possibility of implementation in the national development strategy. Value/originality. The integration of smart technologies into the infrastructure and logistics systems of the One Belt, One Road project plays a pivotal role in shaping the impact of the smart economy. By leveraging advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), big data analytics and blockchain, the project aims to optimise connectivity, enhance operational efficiency and promote sustainable development. Key aspects of development include smart infrastructure projects, digitisation and automation in logistics, blockchain technology for transparency and security, and data-driven decision-making and predictive analytics.

Research on Machine Vision-Based Control System for Cold Storage Warehouse Robots

In recent years, the global cold chain logistics market has grown rapidly, but the level of automation remains low. Compared to traditional logistics, automation in cold storage logistics requires a balance between safety and efficiency, and the current detection algorithms are poor at meeting these requirements. Therefore, based on YOLOv5, this paper proposes a recognition and grasping system for cartons in cold storage warehouses. A human–machine interaction system is designed for the cold storage environment, enabling remote control and unmanned grasping. At the algorithm level, the CA attention mechanism is introduced to improve accuracy. The Ghost lightweight module replaces the CBS structure to enhance runtime speed. The Alpha-DIoU loss function is utilized to improve detection accuracy. With the comprehensive improvements, the modified algorithm in this study achieves a 0.711% increase in mAP and a 0.7% increase in FPS while maintaining accuracy. Experimental results demonstrate that the CA attention mechanism increases fidelity by 2.32%, the Ghost lightweight module reduces response time by 13.89%, and the Alpha-DIoU loss function enhances positioning accuracy by 7.14%. By incorporating all the improvements, the system exhibits a 2.16% reduction in response time, a 4.67% improvement in positioning accuracy, and a significant overall performance enhancement.

Development of warehouse logistics at the present stage

Using the examples from foreign practice the article considers the present stage of development of ware-house logistics based on the concept of digitalization and automation of logistics and transport. A practical solution to the problems of logistics development is implemented through real projects, the introduction of the latest technologies, the construction of new facilities and the modernization of existing ones, the use of innovative handling, storage and process equipment, as well as new software products and digital systems. Technical solutions in engineering and construction projects of the new general cargo warehouses and logistics centers are discussed.Much attention is paid to IT systems in warehouse logistics, which made it possible to implement a number of innovative solutions in digitalization and automation abroad. Without technology and IT systems, effective internal logistics, service, technical support for handling and storage equipment are impossible today. Therefore, lifecycle management and preventive maintenance require smart tools.Examples of the new types of handling and storage equipment are given, which control transport & storage and loading & unloading operations. The creation and practical application of new types of handling and storage equipment actively supports the implementation of common goals for the development of warehouse logistics.

Bioinspired Soft Wrist Based on Multicable Jamming With Hybrid Motion and Stiffness Control for Dexterous Manipulation

Dexterous manipulation is important for modern logistics automation. Industrial robots remain inferior to human labor in this area, especially in handling unknown objects in unstructured scenarios. It is observed that the human wrist contributes significantly to the dexterous manipulation capability by agilely changing the hand orientation to approach the target object and adjusting wrist stiffness to adapt to dynamic interaction. Inspired by the human wrist, we propose a novel soft wrist to achieve hybrid motion/stiffness control in a compact and lightweight structure. The dexterous motion and stiffness adjustment are simultaneously enabled by a novel multicable jamming mechanism, which is achieved by a precalibration of the relationship between stiffness with robot length and bending angle to adapt to both compliant and forceful tasks. Dedicated experiments were performed to validate its motion dexterity and variable stiffness property. A soft grasping system was developed to demonstrate the manipulation capability of the soft wrist by tasking it with automatic packaging, which is challenging for robots. Overall, the proposed soft wrist demonstrates a promising solution to enhance robotic manipulation capability to a human comparable level.