Advanced Robotics in Manufacturing: Tools for Automating Production and Enhancing Efficiency

The increasing demand for efficiency and agility in manufacturing has driven the adoption of advanced automation and data-driven decision-making strategies. This study systematically reviews 20 peer-reviewed articles published before 2023, examining key technologies that optimize manufacturing time management, including real-time analytics, robotic process automation (RPA), predictive maintenance, human-robot collaboration (HRC), cybersecurity, and digital twins. The review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, ensuring a rigorous and transparent selection process. The findings indicate that real-time scheduling and predictive analytics reduce production delays by 20% to 40%, while RPA enhances workflow efficiency by 30% to 50%, significantly minimizing manual errors. The study further reveals that predictive maintenance reduces machine failure rates by 40% to 60%, lowering operational disruptions and maintenance costs by 20%. Additionally, collaborative robots (cobots) increase production efficiency by 25% to 35%, improving labor productivity while ensuring worker safety. However, the expansion of cloud-based manufacturing and IoT-enabled automation has introduced cybersecurity risks, with cyberattacks causing up to 30% operational downtime in compromised facilities, necessitating AI-driven security measures. The integration of digital twin technology enhances manufacturing agility by 30% to 45% and improves production accuracy by 25%, enabling real-time process adjustments and predictive optimization. Compared to earlier studies that emphasized static, rule-based automation, recent advancements demonstrate that AI-enhanced, adaptive systems provide superior responsiveness and efficiency. The results underscore the necessity of combining automation, data-driven analytics, and cybersecurity frameworks to achieve sustainable time optimization in smart manufacturing. This review provides valuable insights for industry leaders, researchers, and policymakers seeking to enhance operational efficiency, cost-effectiveness, and resilience in the evolving landscape of industrial automation.

Web-based human-robot collaboration digital twin management and control system

The rapid expansion of e-commerce has driven an unprecedented demand for efficient, accurate, and scalable fulfilment solutions. Traditional warehouse operations, reliant on manual labour and conventional logistics systems, are increasingly being augmented by advanced robotics and system optimization technologies. This paper explores how autonomous mobile robots (AMRs), robotic picking systems, AI-driven warehouse management, and predictive analytics are reshaping e-commerce fulfilment in the U.S. retail sector. By integrating robotics and intelligent automation, companies can enhance operational efficiency, reduce processing times, and minimize costs while improving order accuracy and customer satisfaction. Furthermore, this study examines key challenges associated with implementing these technologies, including high capital investment, workforce displacement, cybersecurity risks, and the need for seamless human-machine collaboration. Despite these hurdles, ongoing advancements in artificial intelligence, machine learning, and cloud-based logistics systems continue to refine fulfilment strategies, making them more adaptive and responsive to shifting consumer expectations. Through a comprehensive analysis of current industry trends, case studies, and future projections, this research highlights how robotics and system optimization will shape the next generation of e-commerce logistics, ensuring greater resilience, sustainability, and competitiveness for U.S. retailers in the digital age. Key Words: Autonomous mobile robots, Robotic picking systems, Collaborative Robots

Digital twin for human-robot collaboration enhancement in manufacturing systems: Literature review and direction for future developments

We present a tool-supported approach for the synthesis, verification and validation of the control software responsible for the safety of the human-robot interaction in manufacturing processes that use collaborative robots. In human-robot collaboration, software-based safety controllers are used to improve operational safety, e.g., by triggering shutdown mechanisms or emergency stops to avoid accidents. Complex robotic tasks and increasingly close human-robot interaction pose new challenges to controller developers and certification authorities. Key among these challenges is the need to assure the correctness of safety controllers under explicit (and preferably weak) assumptions. Our controller synthesis, verification and validation approach is informed by the process, risk analysis, and relevant safety regulations for the target application. Controllers are selected from a design space of feasible controllers according to a set of optimality criteria, are formally verified against correctness criteria, and are translated into executable code and validated in a digital twin. The resulting controller can detect the occurrence of hazards, move the process into a safe state, and, in certain circumstances, return the process to an operational state from which it can resume its original task. We show the effectiveness of our software engineering approach through a case study involving the development of a safety controller for a manufacturing work cell equipped with a collaborative robot.

[EMBARGOED UNTIL 8/1/2024] Order picking, which involves retrieving items from storage locations for an internal or external customer, is a core function in warehouses and accounts for up to 65 percent of the total operating cost. It is considered as a crucial driver for supply chain performance as improper planning of picking operations leads to inefficient asset utilization and delayed deliveries, which, in turn, adversely affects customer satisfaction, operating cost, and competitiveness. A majority of warehouses adopt a picker-to-parts system (workers travel through a warehouse to retrieve and transport items from storage to packing station), and the fulfillment speed of such order picking system (OPS) depends on the following key decisions - (i) set of orders to be picked in a tour (order batching decision), (ii) assignment of a batch to a picker and order in which their a processed (batch assignment and sequencing decisions), and (iii) route followed x by the picker to collect the orders in each batch (picker routing decision). However, with the growing customer demand and global labor shortage, warehouses are seeking efficient and less labor-intensive order picking systems. Autonomous mobile robots (AMRs) or collaborative robots (cobots) have the potential to alleviate the strain on human workers and expedite order picking operations. However, there are several key operational challenges to address for ensuring an effective collaborative human-robot order-picking system (CHR-OPS), where humans perform item retrieval tasks, and AMRs handle item transportation to the depot. This research aims to improve the fulfillment efficiency of a picker-to-parts CHROPS by optimizing key decisions associated with two warehouse picking strategies, namely, static picking and dynamic picking. In the case of static picking, the items to be retrieved from storage for a given day are known as apriority. On the other hand, the dynamic picking strategy allows for orders to arrive over time (e.g., e-commerce warehouses), and the pick cycle (or picking plan) can be updated in real time. First, we address the problem of optimizing the following key subproblems of a CHR-OPS with a static picking strategy: (i) order batching (how many items should be collected in each AMR tour?), (ii) batch assignment and sequencing (how to assign batches to AMRs, and in what order should they be processed?), and (iii) picker-robot routing (how should the AMR and picker be routed to coordinate the picking process?). Existing literature has not dealt with the three subproblems, and this work is the first to address them for a picker-to-parts CHR-OPS system employing multiple pickers and AMRs. A mixed integer linear programming model is developed to jointly optimize the three subproblems with the objective of minimizing the total tardiness of all orders. The MILP model is validated and solved to optimality for small instances. However, since the problem under consideration is NP-hard, it is computationally intractable for larger instances. To efficiently handle large instances, we proposed deterministic and stochastic local search algorithms, namely variable neighborhood descent and a new variant of simulated annealing. The numerical experiments demonstrate the superior performance of the proposed solution approach compared to existing methods. Besides, our results also show that the picking efficiency is impacted by human{robot team composition, AMR speed, AMR capacity and warehouse layout. Subsequently, we address the CHR-OPS with dynamic picking and developing interventionist picking algorithms for a multi-robot multi-human setting. Specifically, we propose two interventionist policies for dynamic collaborative order picking, namely, the collaborative human-robot interventionist picking algorithm (CHRIPA) and the collaborative human-robot rule-based interventionist picking algorithm (CHR-RIPA). The evaluation of the policies demonstrates that the proposed rules can improve the overall performance of the system compared to benchmark approaches (human-only dynamic picking and collaborative picking with no intervention). In addition, results indicate CHR-IPA outperforms CHR-RIPA in terms of average tar diness and order completion time, albeit with a slightly higher travel distance for human workers. The results have led to several managerial implications for a collaborative order picking system. Further, the proposed models and algorithms are modular and can be adapted to any warehouse setting by accounting for the relevant parameters such as warehouse layout, AMR capacity and human-robot composition. Finally, the directions for future research have been identified and summarized.

Packaging operations are undergoing a fundamental transformation thanks to advanced digitalization, automation, and materials science. This allows manufacturers to achieve sustainability goals while increasing mass customization, reducing waste, and delivering higher throughput. The industry's potential for rapid growth is highlighted by market analysts' predictions that packaging automation revenue will surpass $50 billion USD by 2032. The cutting-edge technologies propelling that growth are reviewed in this chapter. The first are autonomous mobile robots, collaborative robots, and AI-enhanced machine-vision systems that increase line speed and accuracy while maintaining flexibility for mixed-SKU production. It then looks at physics-based digital twins and cloud-connected Industrial Internet of Things (IIoT) architectures, which have been highlighted at recent automation congresses for their use in predictive maintenance, virtual commissioning, and real-time performance analytics. pharmaceutical-technology.com. Rapid tooling changes and affordable on-demand personalization are made possible by additive manufacturing and high-resolution digital printing.

This study presents a systematic review of artificial intelligence (AI)–driven Autonomous Mobile Robots (AMRs) within scalable micro-fulfillment centers (MFCs), with a focus on their architectural configurations, operational performance, and integration within modern supply chain ecosystems. The rapid growth of e-commerce and demand for ultra-fast last-mile delivery have accelerated the adoption of compact, urban fulfillment models, where AMRs play a central role in enabling high-density storage, dynamic inventory handling, and real-time order processing. This review synthesizes recent scholarly and industrial contributions to examine how AI techniques—such as reinforcement learning, computer vision, swarm intelligence, and predictive analytics—enhance navigation, task allocation, fleet coordination, and human–robot collaboration in constrained warehouse environments. The paper evaluates system scalability by analyzing throughput optimization, latency reduction, space utilization efficiency, and resilience under variable demand conditions. Furthermore, it investigates interoperability challenges associated with warehouse management systems (WMS), Internet of Things (IoT) infrastructures, and cloud-based orchestration platforms. A comparative assessment of leading AMR deployment frameworks is conducted to identify performance trade-offs, cost implications, and implementation barriers, including energy constraints, safety compliance, and algorithmic reliability. The review also highlights emerging trends such as digital twin integration, edge AI processing, and decentralized decision-making architectures that support adaptive and self-organizing fulfillment systems. By consolidating fragmented research across robotics, logistics, and intelligent systems, this study provides a comprehensive analytical foundation for both academic inquiry and industrial application. The findings aim to guide future research directions toward robust, scalable, and cost-efficient AMR-enabled micro-fulfillment solutions capable of meeting the evolving demands of omnichannel retail and urban logistics environments.

As the development of robotics technologies for collaborative robots (COBOTs), the applications of human–robot collaboration (HRC) have been growing in the past decade. Despite the tremendous efforts from both academia and industry, the overall usage and acceptance of COBOTs are still not so high as expected. One of the major affecting factors is the comfort of humans in HRC, which is usually less emphasized in COBOT development; however, it is critical to the user acceptance during HRC. Therefore, this paper gives a review of human comfort in HRC including the influential factors of human comfort, measurement of human comfort in terms of subjective and objective manners, and human comfort improvement approaches in the context of HRC. Discussions on each topic are also conducted based on the review and analysis.

Industrial maintenance is undergoing a process of digitalization and automation through the use of Industry 4.0 technologies. An example is the employment of digital twins which allow virtualizing assets such as a fleet of Autonomous Mobile Robots (AMR) . In this paper, we analyze two local navigation algorithms for AMRs navigation in a digitized environment: Dynamic Window Approximation (DWA) and Time Elastic Band (TEB) . For this purpose, a digital twin is implemented in the ROS ecosystem. In this environment, a combined mission of a fleet of three AMRs is simulated where they navigate together in order to compare the behavior of the algorithms in dynamic industrial environments. As a result, the trajectories of each AMR that are key for production cost planning are analyzed. Finally, we introduce in this work a web server to interact with the developed digital twin facilitating its use.

The proposal of Industry 5.0 is an effort and attempt to shape a new type of industrialization using human defined values. The core elements of Industry 5.0, including human centeredness, sustainable development, and resilience, are all different forms of high-quality development. This article proposes four corporate value innovation strategies - rainforest strategy, wetland strategy, grassland strategy, and oasis strategy - to provide management suggestions for traditional manufacturing enterprises to shift to smart manufacturing Industry 5.0. Human-robot collaboration, AI and machine learning, IoT and cyber-physical systems, sustainable manufacturing, human-centered workplace designs, resilient and flexible supply chains, and digital twin and simulation technologies are covered in the text. This article presents a detailed plan for the shift towards Industry 5.0, emphasizing the advantages and real-world uses of these sophisticated production techniques. On the new journey, what kind of response can smart manufacturing Industry 5.0 makes and how can they make forward-looking strategic preparations?

Human society is witnessing a revolutionary growth of digital twin (DT) and artificial intelligence (AI) technologies, which has greater impact on Industry 4.0 revolution specially in academia and industry. DT is a digital representation of a physical entity, with data and infrastructure serving as its foundation, algorithms, and models as its core, and software and services as its application. The methodical and thorough integration of domain-specific expertise is even more essential to the foundations of DT and AI in industrial sectors. This paper provides a thorough analysis of more than 30 articles on AI-driven DT technologies employed in Industry 4.0 over the previous five years. It also describes the general advances of these technologies and the current status of AI integration in the domains of advanced robotics and smart manufacturing which are affecting human society. These include established methods like industrial automation as well as complex mechanism like 3D printing and human-robot collaboration. Additionally, the benefits of AI-powered DTs are explained in relation to sustainable development. The development potential and practical difficulties of AI-driven DTs are examined, with varying emphasis on various levels.

Digital twin applications in radiology and radiotherapy: Applications, challenges, and future perspectives.

The flexibility of future production systems envisioned by Industry 4.0 requires safe but efficient Human-Robot Collaboration (HRC). An important enabler of HRC is a sophisticated collision avoidance mechanism which can detect objects and potential collision events and as a response, it calculates detour trajectories avoiding physical contacts. Digital twins provide a novel way to test the impact of different control decisions in a simulated virtual environment even in parallel. The required computational power can be provided by cloud platforms but at the cost of higher delay and jitter. Moreover, clouds bring a versatile set of novel techniques easing the life of both developers and operators. Can digital twins exploit the benefits of these concepts? Can the robots tolerate the delay characteristics coming with the cloud platforms? In this paper, we answer these questions by building on public and private cloud solutions providing different techniques for parallel computation. Our contribution is threefold. First, we introduce a measurement methodology to characterize different approaches in terms of latency. Second, a real HRC use-case is elaborated and a relevant KPI is defined. Third, we evaluate the pros/cons of different solutions and their impact on the performance.

This study uses a wind turbine case study as a subdomain of Industrial Internet of Things (IIoT) to showcase an architecture for implementing a distributed digital twin in which all important aspects of a predictive maintenance solution in a DT use a fog computing paradigm, and the typical predictive maintenance DT is improved to offer better asset utilization and management through real-time condition monitoring, predictive analytics, and health management of selected components of wind turbines in a wind farm. Digital twin (DT) is a technology that sits at the intersection of Internet of Things, Cloud Computing, and Software Engineering to provide a suitable tool for replicating physical objects in the digital space. This can facilitate the implementation of asset management in manufacturing systems through predictive maintenance solutions leveraged by machine learning (ML). With DTs, a solution architecture can easily use data and software to implement asset management solutions such as condition monitoring and predictive maintenance using acquired sensor data from physical objects and computing capabilities in the digital space. While DT offers a good solution, it is an emerging technology that could be improved with better standards, architectural framework, and implementation methodologies. Researchers in both academia and industry have showcased DT implementations with different levels of success. However, DTs remain limited in standards and architectures that offer efficient predictive maintenance solutions with real-time sensor data and intelligent DT capabilities. An appropriate feedback mechanism is also needed to improve asset management operations.