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The purpose of this paper is to illustrate the growing role of robots in the logistics industry.

Following an introduction, which identifies key challenges facing the industry, this paper discusses robotic applications in warehouses, followed by sections covering transportation and delivery and conclusions.

The logistics industry faces a number of challenges that drive technological and operational changes. Robots are already playing a role within the warehouse sector and more complex applications have recently arisen from developments in artificial intelligence-enabled vision technology. In the transportation sector, autonomous trucks are being developed and trialled by leading manufacturers. Many major logistics companies are involved and limited services are underway. Last-mile delivery applications are growing rapidly, and trials, pilot schemes and commercial services are underway in Europe, the USA and the Far East. The Chinese market is particularly buoyant, and in 2019, a delivery robot was launched that operates on public roads, based on Level-4 autonomous driving technology. The drone delivery sector has been slower to develop, in part due to regulatory constraints, but services are now being operated by drone manufacturers, retailers and logistics providers.

This paper provides details of existing and future applications of robots in the logistics industry.

The present paper aims to address challenges associated with path planning and obstacle avoidance in mobile robotics. It introduces a pioneering solution called the Bi-directional Adaptive Enhanced A* (BAEA*) algorithm, which uses a new bidirectional search strategy. This approach facilitates simultaneous exploration from both the starting and target nodes and improves the efficiency and effectiveness of the algorithm in navigation environments. By using the heuristic knowledge A*, the algorithm avoids unproductive blind exploration, helps to obtain more efficient data for identifying optimal solutions. The simulation results demonstrate the superior performance of the BAEA* algorithm in achieving rapid convergence towards an optimal action strategy compared to existing methods.

The paper adopts a careful design focusing on the development and evaluation of the BAEA* for mobile robot path planning, based on the reference [18]. The algorithm has remarkable adaptability to dynamically changing environments and ensures robust navigation in the context of environmental changes. Its scale further enhances its applicability in large and complex environments, which means it has flexibility for various practical applications. The rigorous evaluation of our proposed BAEA* algorithm with the Bidirectional adaptive A* (BAA*) algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. The BAEA* algorithm consistently outperforms BAA*, demonstrating its ability to plan shorter and more stable paths and achieve higher success rates in all environments.

The paper adopts a careful design focusing on the development and evaluation of the BAEA* for mobile robot path planning, based on the reference [18]. The algorithm has remarkable adaptability to dynamically changing environments and ensures robust navigation in the context of environmental changes. Its scale further enhances its applicability in large and complex environments, which means it has flexibility for various practical applications. The rigorous evaluation of our proposed BAEA* algorithm with the Bi-directional adaptive A* (BAA*) algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm.

The rigorous evaluation of our proposed BAEA* algorithm with the BAA* algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. The BAEA* algorithm consistently outperforms BAA*, demonstrating its ability to plan shorter and more stable paths and achieve higher success rates in all environments.

The originality of this paper lies in the introduction of the bidirectional adaptive enhancing A* algorithm (BAEA*) as a novel solution for path planning for mobile robots. This algorithm is characterized by its unique characteristics that distinguish it from others in this field. First, BAEA* uses a unique bidirectional search strategy, allowing to explore the same path from both the initial node and the target node. This approach significantly improves efficiency by quickly converging to the best paths and using A* heuristic knowledge. In particular, the algorithm shows remarkable capabilities to quickly recognize shorter and more stable paths while ensuring higher success rates, which is an important feature for time-sensitive applications. In addition, BAEA* shows adaptability and robustness in dynamically changing environments, not only avoiding obstacles but also respecting various constraints, ensuring safe path selection. Its scale further increases its versatility by seamlessly applying it to extensive and complex environments, making it a versatile solution for a wide range of practical applications. The rigorous assessment against established algorithms such as BAA* consistently shows the superior performance of BAEA* in planning shorter paths, achieving higher success rates in different environments and cementing its importance in complex and challenging environments. This originality marks BAEA* as a pioneering contribution, increasing the efficiency, adaptability and applicability of mobile robot path planning methods.

This paper aims to present a novel adaptive sliding mode control (ASMC) method based on the predefined performance barrier function for reusable launch vehicle under attitude constraints and mismatched disturbances.

A novel ASMC based on barrier function is adopted to deal with matched and mismatched disturbances. The upper bounds of the disturbances are not required to be known in advance. Meanwhile, a predefined performance function (PPF) with prescribed convergence time is used to adjust the boundary of the barrier function. The transient performance, including the overshoot, convergence rate and settling time, as well as the steady-state performance of the attitude tracking error are retained in the predetermined region under the barrier function and PPF. The stability of the proposed control method is analyzed via Lyapunov method.

In contrast to conventional adaptive back-stepping methods, the proposed method is comparatively simple and effective which does not need to disassemble the control system into multiple first-order systems. The proposed barrier function based on PPF can adjust not only the switching gain in an adaptive way but also the convergence time and steady-state error. And the efficiency of the proposed method is illustrated by conducting numerical simulations.

A novel barrier function based ASMC method is proposed to fit in the amplitude of the mismatched and matched disturbances. The transient and steady-state performance of attitude tracking error can be selected as prior control parameters.

This chapter provides an in-depth look at how digital supply chain management (DSCM) can revolutionize supply chains in the post-COVID world. The COVID-19 pandemic exposed the vulnerabilities of traditional supply chains, highlighting the need for resilience and adaptability. The chapter begins by examining these COVID-induced disruptions, setting the foundation for the discussion on DSCM. DSCM, leveraging advanced technologies and data insights, offers a solution to these challenges, promoting agility, transparency, and sustainability in supply chain operations. This represents a significant shift from traditional practices, equipping organizations to cope with the dynamic postpandemic environment. Key capabilities of DSCM, such as resilience, integration, agility, and risk management, are discussed, supported by real-world examples from leading companies. These examples showcase the successful implementation of DSCM and its benefits in navigating the complexities of modern supply chains. However, the adoption of DSCM is not without challenges, including cybersecurity risks and integration difficulties. The chapter suggests strategies to overcome these challenges, emphasizing the importance of technology, collaboration, sustainability, and data-driven decision-making. By embracing these strategies, organizations can effectively manage their supply chains in the evolving global market, leveraging DSCM to withstand future uncertainties.

To delve into the integration of global logistics and supply chain networks amidst the digital transformation era. This study aims to investigate the potential role of China’s Belt and Road Initiative (BRI) in facilitating the integration of global flows encompassing both tangible goods and intangibles. Additionally, the study seeks to incorporate third-party logistics activities into a comprehensive global logistics and supply chain integration framework.

Prior research is synthesised into a global logistics and supply chain integration framework. A case study was undertaken on Yuan Tong (YTO) express group to investigate the framework, employing qualitative data analysis techniques. The study specifically examined the context of the BRI to enhance comprehension of its impact on global supply chains. Information was collected in particular to two types of supply chain flows, the physical flow of goods, and intangible information and cash flows.

The proposed framework aligns well with the case study, leading to the identification of global logistics and supply chain integration enablers. The results demonstrate a range of ways BRI promotes global logistics and supply chain integration.

The case study, with multiple examples, focuses on how third-party logistics firms can embrace global logistics and supply chain integration in line with BRI. The case study approach limits generalisation, further applications in different contexts are required to validate the findings.

The framework holds promise for aiding practitioners and researchers in gaining deeper insights into the role of the BRI in global logistics and supply chain integration within the digital era. The identified enablers underscore the importance of emphasising key factors necessary for success in navigating digital transformation within global supply chains.

Since 2020, the COVID-19 pandemic has swept the world, although the current situation is more under control. Because the development of the pandemic took place in the context of a digital society, where digital information and communication technologies (ICT) were already widely used, households certainly had to make greater use of this powerful communication tool, partly for work, and partly for distance learning purposes. It is likely that the increased use of ICT in the home, due to the lockdown, created an environment in which families were more united but also isolated and in conflict and this trend may still be present today.

This chapter is based on a study of ICT in the daily lives of Portuguese and Italian women, who lived in nuclear families, during and after the COVID pandemic. Through the testimonies of these women, therefore, we will discuss the results of the study to describe and understand how families used ICT during and after the pandemic. In particular, we are interested in answering the following questions: Did domestic spaces become more and more like work spaces due to the increased use of ICT due to the pandemic lockdown? Did distance learning, due to the lockdown, lead to an increase in ICT use by children/adolescents that is still perpetuated today?

This study aims to explore the role of frontline service employees’ (FSEs) awareness that their job can be substituted by smart technology, artificial intelligence, robotics and algorithms (STARA) in their job autonomy and proactive service performance and when these relationships can be buffered. Drawing on the cognitive appraisal theory of stress, the study examined the mediating relationship between FSEs’ STARA awareness, job autonomy and proactive service performance and the moderating effects of self-efficacy and resilience on this relationship.

The authors administered two-wave online surveys to 301 South Korean FSEs working in various service sectors (e.g. retailing, food/beverage, hospitality/tourism and banking). The Time 1 survey measured respondents’ STARA awareness, self-efficacy, resilience and job autonomy, and the Time 2 survey assessed their proactive service performance.

FSEs’ STARA awareness negatively affected their subsequent proactive service performance through decreased job autonomy. The negative association between STARA awareness and job autonomy was weaker when FSEs’ self-efficacy was high than when it was low. While the authors observed no significant moderation of resilience, the author found a marginally significant three-way interaction between STARA awareness, self-efficacy and resilience. Specifically, STARA awareness was negatively related to job autonomy only when both self-efficacy and resilience were low. When either self-efficacy or resilience was high, the association between STARA awareness and job autonomy became nonsignificant, suggesting the buffering roles of the two personal resources.

Given that the measurement of variables relied on self-reported data, rater biases might have affected the findings of the study. Moreover, the simultaneous measurement of STARA awareness, self-efficacy, resilience and job autonomy could preclude causal inferences between these variables. The authors encourage future studies to use a more rigorous methodology to reduce rater biases and establish stronger causality between the variables.

Service firms can decrease FSEs’ STARA awareness through training in the knowledge and skills necessary to work with these technologies. To promote FSEs’ proactive service performance in this context, service firms need to involve them in decisions related to STARA adoption and allow them to craft their jobs. Service managers should provide FSEs with social support and exercise empowering and supportive leadership to help them view STARA as a challenge rather than a threat.

Distinct from prior research on STARA awareness and employee outcomes, the study identified proactive service performance as a key outcome in the STARA context. By presenting self-efficacy and resilience as crucial personal resources that buffer FSEs from the deleterious impact of STARA awareness, the study provides practitioners with insights that can help FSEs maintain their job autonomy and proactive service performance in times of digitalization and automation.

Because of the various advantages of reinforcement learning (RL) mentioned above, this study uses RL to train unmanned aerial vehicles to perform two tasks: target search and cooperative obstacle avoidance.

This study draws inspiration from the recurrent state-space model and recurrent models (RPM) to propose a simpler yet highly effective model called the unmanned aerial vehicles prediction model (UAVPM). The main objective is to assist in training the UAV representation model with a recurrent neural network, using the soft actor-critic algorithm.

This study proposes a generalized actor-critic framework consisting of three modules: representation, policy and value. This architecture serves as the foundation for training UAVPM. This study proposes the UAVPM, which is designed to aid in training the recurrent representation using the transition model, reward recovery model and observation recovery model. Unlike traditional approaches reliant solely on reward signals, RPM incorporates temporal information. In addition, it allows the inclusion of extra knowledge or information from virtual training environments. This study designs UAV target search and UAV cooperative obstacle avoidance tasks. The algorithm outperforms baselines in these two environments.

It is important to note that UAVPM does not play a role in the inference phase. This means that the representation model and policy remain independent of UAVPM. Consequently, this study can introduce additional “cheating” information from virtual training environments to guide the UAV representation without concerns about its real-world existence. By leveraging historical information more effectively, this study enhances UAVs’ decision-making abilities, thus improving the performance of both tasks at hand.