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Collaborative robotics and autonomous driving are fairly new disciplines, still with a long way to go to achieve goals, set by the research community, manufacturers and users. For technologies like collaborative robotics and autonomous driving, which focus on closing the gap between humans and machines, the physical, psychological and emotional needs of human individuals becoming increasingly important in order to ensure effective and safe human–machine interaction. The authors' goal was to conceptualize ways to combine experience from both fields and transfer artificial intelligence knowledge from one to another. By identifying transferable meta-knowledge, the authors will increase quality of artificial intelligence applications and raise safety and contextual awareness for users and environment in both fields.

First, the authors presented autonomous driving and collaborative robotics and autonomous driving and collaborative robotics' connection to artificial intelligence. The authors continued with advantages and challenges of both fields and identified potential topics for transferrable practices. Topics were divided into three time slots according to expected research timeline.

The identified research opportunities seem manageable in the presented timeline. The authors' expectation was that autonomous driving and collaborative robotics will start moving closer in the following years and even merging in some areas like driverless and humanless transport and logistics.

The authors' findings confirm the latest trends in autonomous driving and collaborative robotics and expand them into new research and collaboration opportunities for the next few years. The authors' research proposal focuses on those that should have the most positive impact to safety, complement, optimize and evolve human capabilities and increase productivity in line with social expectations. Transferring meta-knowledge between fields will increase progress and, in some cases, cut some shortcuts in achieving the aforementioned goals.

Connecting autonomous drones to ground operations and services is a prerequisite for the adoption of scalable and sustainable drone services in the built environment. Despite the rapid advance in the field of autonomous drones, the development of ground infrastructure has received less attention. Contemporary airport design offers potential solutions for the infrastructure serving autonomous drone services. To that end, this paper aims to construct a framework for connecting air and ground operations for autonomous drone services. Furthermore, the paper defines the minimum facilities needed to support unmanned aerial vehicles for autonomous logistics and the collection of aerial data.

The paper reviews the state-of-the-art in airport design literature as the basis for analysing the guidelines of manned aviation applicable to the development of ground infrastructure for autonomous drone services. Socio-technical system analysis was used for identifying the service needs of drones.

The key findings are functional modularity based on the principles of airport design applies to micro-airports and modular service functions can be connected efficiently with an autonomous ground handling system in a sustainable manner addressing the concerns on maintenance, reliability and lifecycle.

As the study was limited to the airport design literature findings, the evolution of solutions may provide features supporting deviating approaches. The role of autonomy and cloud-based service processes are quintessentially different from the conventional airport design and are likely to impact real-life solutions as the area of future research.

The findings of this study provided a framework for establishing the connection between the airside and the landside for the operations of autonomous aerial services. The lack of such framework and ground infrastructure has hindered the large-scale adoption and easy-to-use solutions for sustainable logistics and aerial data collection for decision-making in the built environment.

The evolution of future autonomous aerial services should be accessible to all users, “democratising” the use of drones. The data collected by drones should comply with the privacy-preserving use of the data. The proposed ground infrastructure can contribute to offloading, storing and handling aerial data to support drone services’ acceptability.

To the best of the authors’ knowledge, the paper describes the first design framework for creating a design concept for a modular and autonomous micro-airport system for unmanned aviation based on the applied functions of full-size conventional airports.

This paper discusses the evolution of sustainability reporting and the role of the Global Reporting Initiative (GRI) in relation to the social and environmental accounting (SEA) literature calling for a revolution in the standardization of sustainability reporting and the inherent complexities. This paper focuses on the future role of GRI in light of the changes resulting from harmonization supported by the International Sustainability Standards Board and the European Financial Reporting Advisory Group’s draft European Sustainability Reporting Directive.

Building on Bourdieu (1983, 1992) and SEA studies, the authors adopt a critical and qualitative approach to theorize power dynamics in the sustainability reporting field. After identifying the main issues arising from the complexity of the sustainability reporting standards and practices according to SEA scholars, the authors connect them with Bourdieu’s (1992, 1983) field theory to discuss the future role of GRI.

The findings suggest two distinct but intertwined roles that GRI could play in the future, namely, power related and theoretical/technical, aimed at engendering revolutionary rather than evolutionary changes in sustainability reporting.

This study offers practical implications for GRI to strengthen its future role in sustainability reporting standardization.

The limited time available to mitigate the disastrous consequences of non-sustainable business on society and the environment calls for urgently addressing the complexities of sustainability accounting to foster a positive impact on society and the environment.

The authors’ reflections reclaim the SEA literature as central to identifying sustainability complexity and Bourdieu’s (1983, 1992) notions of power as key to understanding the role of GRI in the sustainability field. Furthermore, this paper emphasizes the intersection of different critical concepts, including power, complexity, value, capital and materiality.

This paper aims to provide a better understanding of the challenges and potential solutions in Visual Simultaneous Localization and Mapping (SLAM), laying the foundation for its applications in autonomous navigation, intelligent driving and other related domains.

In analyzing the latest research, the review presents representative achievements, including methods to enhance efficiency, robustness and accuracy. Additionally, the review provides insights into the future development direction of Visual SLAM, emphasizing the importance of improving system robustness when dealing with dynamic environments. The research methodology of this review involves a literature review and data set analysis, enabling a comprehensive understanding of the current status and prospects in the field of Visual SLAM.

This review aims to comprehensively evaluate the latest advances and challenges in the field of Visual SLAM. By collecting and analyzing relevant research papers and classic data sets, it reveals the current issues faced by Visual SLAM in complex environments and proposes potential solutions. The review begins by introducing the fundamental principles and application areas of Visual SLAM, followed by an in-depth discussion of the challenges encountered when dealing with dynamic objects and complex environments. To enhance the performance of SLAM algorithms, researchers have made progress by integrating different sensor modalities, improving feature extraction and incorporating deep learning techniques, driving advancements in the field.

To the best of the authors’ knowledge, the originality of this review lies in its in-depth analysis of current research hotspots and predictions for future development, providing valuable references for researchers in this field.

The promotion of autonomous vehicles introduces privacy and security risks, underscoring the pressing need for responsible innovation implementation. To more effectively address the societal risks posed by autonomous vehicles, considering collaborative engagement of key stakeholders is essential. This study aims to provide insights into the governance of potential privacy and security issues in the innovation of autonomous driving technology by analyzing the micro-level decision-making processes of various stakeholders.

For this study, the authors use a nuanced approach, integrating key stakeholder theory, perceived value theory and prospect theory. The study constructs a model based on evolutionary game for the privacy and security governance mechanism of autonomous vehicles, involving enterprises, governments and consumers.

The governance of privacy and security in autonomous driving technology is influenced by key stakeholders’ decision-making behaviors and pivotal factors such as perceived value factors. The study finds that the governmental is influenced to a lesser extent by the decisions of other stakeholders, and factors such as risk preference coefficient, which contribute to perceived value, have a more significant influence than appearance factors like participation costs.

This study lacks an investigation into the risk sensitivity of various stakeholders in different scenarios.

The study delineates the roles and behaviors of key stakeholders and contributes valuable insights toward addressing pertinent risk concerns within the governance of autonomous vehicles. Through the study, the practical application of Responsible Innovation theory has been enriched, addressing the shortcomings in the analysis of micro-level processes within the framework of evolutionary game.

Companies target globally mobile workers and face the war for talent, while individuals are more reluctant to engage in global mobility. This scenario led us to propose a model to understand the individuals' decision process to engage in global mobility.

Building on the self-determination theory, the theory of planned behavior and the literature on decisions for global mobility, the authors propose mechanisms through which psychological variables and assignments' factual and perceived contextual aspects (directly or indirectly) explain the decision to engage or not in global mobility.

This study offers a conceptual model with the authors' novel propositions to explain individuals' decision to engage in global mobility.

The model provides a more comprehensive explanation of the individuals' decision-making process to engage in global mobility than previous models and potentially yields more effective organizational practices to attract both well-established and emerging phenomena of globally mobile workers.

Many popular simultaneous localization and mapping (SLAM) techniques have low accuracy, especially when localizing environments containing dynamically moving objects since their presence can potentially cause inaccurate data associations. To address this issue, the proposed FADM-SLAM system aims to improve the accuracy of SLAM techniques in environments containing dynamically moving objects. It uses a pipeline of feature-based approaches accompanied by sparse optical flow and multi-view geometry as constraints to achieve this goal.

FADM-SLAM, which works with monocular, stereo and RGB-D sensors, combines an instance segmentation network incorporating an intelligent motion detection strategy (iM) with an optical flow technique to improve location accuracy. The proposed AS-SLAM system comprises four principal modules, which are the optical flow mask and iM, the ego motion estimation, dynamic point detection and the feature-based extraction framework.

Experiment results using the publicly available RGBD-Bonn data set indicate that FADM-SLAM outperforms established visual SLAM systems in highly dynamic conditions.

In summary, the first module generates the indication of dynamic objects by using the optical flow and iM with geometric-wise segmentation, which is then used by the second module to compute the starting point of a posture. The third module, meanwhile, first searches for the dynamic feature points in the environment, and second, eliminates them from further processing. An algorithm based on epipolar constraints is implemented to do this. In this way, only the static feature points are retained, which are then fed to the fourth module for extracting important features.

Nature’s evolution has shaped intelligent behaviors in creatures like insects and birds, inspiring the field of Swarm Intelligence. Researchers have developed bio-inspired algorithms to address complex optimization problems efficiently. These algorithms strike a balance between computational efficiency and solution optimality, attracting significant attention across domains.

Bio-inspired optimization techniques for feature engineering and its applications are systematically reviewed with chief objective of assessing statistical influence and significance of “Bio-inspired optimization”-based computational models by referring to vast research literature published between year 2015 and 2022.

The Scopus and Web of Science databases were explored for review with focus on parameters such as country-wise publications, keyword occurrences and citations per year. Springer and IEEE emerge as the most creative publishers, with indicative prominent and superior journals, namely, PLoS ONE, Neural Computing and Applications, Lecture Notes in Computer Science and IEEE Transactions. The “National Natural Science Foundation” of China and the “Ministry of Electronics and Information Technology” of India lead in funding projects in this area. China, India and Germany stand out as leaders in publications related to bio-inspired algorithms for feature engineering research.

The review findings integrate various bio-inspired algorithm selection techniques over a diverse spectrum of optimization techniques. Anti colony optimization contributes to decentralized and cooperative search strategies, bee colony optimization (BCO) improves collaborative decision-making, particle swarm optimization leads to exploration-exploitation balance and bio-inspired algorithms offer a range of nature-inspired heuristics.

In this chapter, the interplay between the development of the discipline, the development of the field of study, and the emergence of professional fields is examined using the example of mathematics. In connection with the formation of the modern research university, mathematics has emerged as an independent scientific discipline and as an independent field of study. In the process, mathematics attains a high degree of formalization and internal coherence. This is the basis for the penetration of mathematicians into more and more professional fields, even outside science. Real problems or real facts are reduced to aspects that are amenable to mathematical modeling by treating them as quantifiable parameters. As mathematics expands as a field of study, more and more professional sectors become applications of mathematical models. As a consequence, more mathematical fields of study are differentiating themselves, specializing in these application fields. This chapter analyzes this dynamic and its preconditions.

Despite the growing interest in the field of supply chain sustainability (SCS), little exploration of new theories exists. Therefore, this paper aims to introduce practice theories to SCS studies through a practice turn.

This is a conceptual paper in nature. Hence, based on theoretical arguments, the authors elaborate on how the practice turn can arise in the SCS field.

The theoretical elaboration is rooted in the understanding that sustainability is not limited to the materiality of environmental and social issues, as often observed. Instead, there is a need to include immaterial, emotional and intangible elements to better comprehend SCS practice. The authors argue that a continuum exists for a practice turn, including practice-based view, practice-based studies and critical practice theory.

The authors provide a research agenda with a comprehensive perspective of understanding the application and implications of practice theories to SCS.

The practice turn in SCS studies can support managers to better understand their practices not only through recognizing explicit activities but also mainly by reflecting on hidden elements that affect their performance.

SCS studies can better engage with grand challenges through a practice turn, which helps increase its contribution to solving social problems.

Unlike previous literature, the paper elaborates on how practice theories are powerful in supporting both scholars and practitioners in moving away from an extremely economic focus to genuinely embrace sustainability practice. In doing so, the practice turn appears as an important phase for SCS field maturity.