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This study aims to address the issue of random movement and non coordination between docking mechanisms and locking mechanisms, and proposes a comprehensive dynamic docking control architecture that integrates perception, planning, and motion control.

Firstly, the proposed dynamic docking control architecture uses laser sensors and a charge-coupled device camera to perceive the pose of the target. The sensor data are mapped to a high-dimensional potential field space and fused to reduce interference caused by detection noise. Next, a new potential function based on multi-dimensional space is developed for docking path planning, which enables the docking mechanism based on Stewart platform to rapidly converge to the target axis of the locking mechanism, which improves the adaptability and terminal docking accuracy of the docking state. Finally, to achieve precise tracking and flexible docking in the final stage, the system combines a self-impedance controller and an impedance control algorithm based on the planned trajectory.

Extensive simulations and experiments have been conducted to validate the effectiveness of the dynamic docking system and its control architecture. The results indicate that even if the target moves randomly, the system can successfully achieve accurate, stable and flexible dynamic docking.

This research can provide technical guidance and reference for docking task of unmanned vehicles under the ground conditions. It can also provide ideas for space docking missions, such as space simulator docking.

Dynamically tracking the target by unmanned ground vehicles (UGVs) plays a critical role in mobile drone recovery. This study aims to solve this challenge under diverse random disturbances, proposing a dynamic target tracking framework for UGVs based on target state estimation, trajectory prediction, and UGV control.

To mitigate the adverse effects of noise contamination in target detection, the authors use the extended Kalman filter (EKF) to improve the accuracy of locating unmanned aerial vehicles (UAVs). Furthermore, a robust motion prediction algorithm based on polynomial fitting is developed to reduce the impact of trajectory jitter caused by crosswinds, enhancing the stability of drone trajectory prediction. Regarding UGV control, a dynamic vehicle model featuring independent front and rear wheel steering is derived. Additionally, a linear time-varying model predictive control algorithm is proposed to minimize tracking errors for the UGV.

To validate the feasibility of the framework, the algorithms were deployed on the designed UGV. Experimental results demonstrate the effectiveness of the proposed dynamic tracking algorithm of UGV under random disturbances.

This paper proposes a tracking framework of UGV based on target state estimation, trajectory prediction and UGV predictive control, enabling the system to achieve dynamic tracking to the UAV under multiple disturbance conditions.

Unlocking the potential of Big Data Analytics (BDA) has proven to be a transformative factor for the Architecture, Engineering and Construction (AEC) industry. This has prompted researchers to focus attention on BDA in the AEC industry (BDA-in-AECI) in recent years, leading to a proliferation of relevant research. However, an in-depth exploration of the literature on BDA-in-AECI remains scarce. As a result, this study seeks to systematically explore the state-of-the-art review on BDA-in-AECI and identify research trends and gaps in knowledge to guide future research.

This state-of-the-art review was conducted using a mixed-method systematic review. Relevant publications were retrieved from Scopus and then subjected to inclusion and exclusion criteria. A quantitative bibliometric analysis was conducted using VOSviewer software and Gephi to reveal the status quo of research in the domain. A further qualitative analysis was performed on carefully screened articles. Based on this mixed-method systematic review, knowledge gaps were identified and future research agendas of BDA-in-AECI were proposed.

The results show that BDA has been adopted to support AEC decision-making, safety and risk assessment, structural health monitoring, damage detection, waste management, project management and facilities management. BDA also plays a major role in achieving construction 4.0 and Industry 4.0. The study further revealed that data mining, cloud computing, predictive analytics, machine learning and artificial intelligence methods, such as deep learning, natural language processing and computer vision, are the key methods used for BDA-in-AECI. Moreover, several data acquisition platforms and technologies were identified, including building information modeling, Internet of Things (IoT), social networking and blockchain. Further studies are needed to examine the synergies between BDA and AI, BDA and Digital twin and BDA and blockchain in the AEC industry.

The study contributes to the BDA-in-AECI body of knowledge by providing a comprehensive scope of understanding and revealing areas for future research directions beneficial to the stakeholders in the AEC industry.

Unlike many existing methods that are primarily focused on two-dimensional localization, this research paper extended the scope to three-dimensional localization. This enhancement is particularly significant for unmanned aerial vehicle (UAV) applications that demand precise altitude information, such as infrastructure inspection and aerial surveillance, thereby broadening the applicability of UAV-assisted wireless networks.

The paper introduced a novel method that employs recurrent neural networks (RNNs) for node localization in three-dimensional space within UAV-assisted wireless networks. It presented an optimization perspective to the node localization problem, aiming to balance localization accuracy with computational efficiency. By formulating the localization task as an optimization challenge, the study proposed strategies to minimize errors while ensuring manageable computational overhead, which are crucial for real-time deployment in dynamic UAV environments.

Simulation results demonstrated significant improvements, including a channel capacity of 99.95%, energy savings of 89.42%, reduced latency by 99.88% and notable data rates for UAV-based communication with an average localization error of 0.8462. Hence, the proposed model can be used to enhance the capacity of UAVs to work effectively in diverse environmental conditions, offering a reliable solution for maintaining connectivity during critical scenarios such as terrestrial environmental crises when traditional infrastructure is unavailable.

Conventional localization methods in wireless sensor networks (WSNs), such as received signal strength (RSS), often entail manual configuration and are beset by limitations in terms of capacity, scalability and efficiency. It is not considered for 3-D localization. In this paper, machine learning such as multi-layer perceptrons (MLP) and RNN are employed to facilitate the capture of intricate spatial relationships and patterns (3-D), resulting in enhanced localization precision and also improved in channel capacity, energy savings and reduced latency of UAVs for wireless communication.

This study aims to explore and segment consumer preferences for electric and hybrid vehicles in Germany, Sweden, the Netherlands and Turkey, focusing on understanding the various factors that influence consumer decisions in these markets.

Using latent class analysis (LCA) on data collected through online surveys and discrete choice experiments, this research categorizes consumers into distinct segments. The approach allows for a nuanced understanding of how various factors such as income level, fuel cost, age, CO₂ emissions, purchase price, vehicle range, policy policies and environmental concerns interact with shape consumer preferences.

The analysis uncovers significant heterogeneity in consumer preferences for electric and hybrid vehicles across Germany, Sweden, the Netherlands and Turkey, revealing four key segments: “Eco-Driven Innovators,” “Value-Focused Pragmatists,” “Tech-Savvy Early Adopters” and “Reluctant Traditionalists.” “Eco-Driven Innovators” prioritize environmental benefits and are less sensitive to price, demonstrating a strong inclination toward vehicle CO₂ emissions and policy policies. “Value-Focused Pragmatists” weigh economic factors heavily, showing a sharp interest in fuel costs and purchase prices but are open to considering electric and hybrid vehicles if they present clear long-term savings. Technology-savvy early adopters are attracted by the latest technological advancements in vehicles, regardless of the type, and are motivated by factors beyond just environmental concerns or cost savings. Lastly, “Reluctant Traditionalists” exhibit minimal interest in electric and hybrid vehicles due to concerns over charging infrastructure and upfront costs. This detailed segmentation illustrates the diverse motivations and barriers influencing consumer choices, from governmental policies and environmental concerns to individual financial considerations and technological appeal.

This study stands out for its pioneering application of LCA to dissect the complexity of consumer preferences for electric and hybrid vehicles, a methodological approach not widely used in this research domain. Using LCA, the authors are able to uncover nuanced consumer segments, each with distinct preferences and motivations, providing a depth of insight into market dynamics that traditional analysis methods may overlook. This approach enables a more granular understanding of how diverse factors – ranging from environmental concerns to economic considerations and technological attributes – interact to shape consumer choices in different countries. The findings not only fill a critical gap in the existing literature by mapping the intricate landscape of consumer preferences, but also offer a novel perspective on strategizing market interventions. Therefore, the application of LCA enriches the discourse on sustainable transportation, offering stakeholders, manufacturers, policymakers and researchers – a refined toolkit for navigating the evolving market dynamics and fostering the adoption of electric and hybrid vehicles.

The a priori identification and development of army personnel competencies are necessary to enable effective and efficient responses to rapidly changing climate conditions. Accordingly, this study aims to identify the performance requirements of a military flood responder and the competencies (knowledge, skills and abilities) required to perform it.

Using an abductive approach, the authors conducted both secondary and primary research to generate a validated framework of performance criteria and competencies for army personnel responding to floods. This literature review integrated both the peer-reviewed academic literature and public sector grey literature. Using the critical incident technique, the authors then conducted semi-structured interviews with 15 members of the Canadian Armed Forces (CAF) who had previously been tasked with flood response operations. Participants were asked about the tasks required while conducting flood response operations. Interview transcripts were then content analysed to identify themes regarding those tasks, and the competencies needed to perform those tasks were then extracted and contrasted with the literature review findings. Inter-rater reliability for the analysis was established via iterative discussion between the two co-authors.

The primary data reinforced and expanded the list of performance expectations that the authors deductively identified from the integrated literature review, adding granularity to each. It also identified competencies (including both hard and soft skills) and highlighted previously neglected contextual antecedents of military flood response effectiveness.

though knowledge saturation was achieved from the 15 interviews conducted, further research with larger samples could more deeply ground the evidence discovered in this study. Nevertheless, the competencies identified in this paper could serve as a starting guide to staffing and/or training interventions targeted at improving these competencies for personnel responding to flood scenarios.

The theoretical findings also have immediate practical relevance to training for flood response operations. In particular, the subtle challenges in competency crossover from military operations to flood response operations may facilitate not only more efficient, targeted training (that could improve the effectiveness of army personnel involved in humanitarian roles), but could be applied to the selection of army personnel as well. This study may also help provincial/municipal operators and emergency planners by better communicating the strengths and limitations of army personnel in addressing civilian military cooperation for humanitarian operations. Thus, the findings of this research study represent an important first step in prompting attention to the strategic human resource planning studies required to make all responders more efficient and effective in their respective division of labour within the humanitarian domain.

Peering a little beyond these research findings, human-induced climate change is expected to continue increasing the frequency of such events (IPCC, 2021), and a timely, national force is likely to be increasingly required for Canadians impacted by major disasters stemming from natural hazards when local resources become overwhelmed. Yet, there is some concern from the CAF that increasing responsiveness to disaster operations will affect their military readiness (Leuprecht and Kasurak, 2020). One can indeed envision a paradox whereby the CAF is both a “force of last resort” while increasingly becoming a “first choice for domestic disaster and emergency assistance”. The practical implications from this research also suggest that military personnel, while fully capable of successfully conducting flood response operations, may become overburdened and less able to adopt yet greater capacity and training for other additional humanitarian work. Nevertheless, the competencies highlighted by participants can help inform the next flood response operation in Canada.

Most literature in the field of emergency response focuses on cooperation between civilian and military resources and other strategic-level themes. The findings address critical granularity missing at the operational and tactical levels of humanitarian assistance and disaster relief research. The authors also draw implications beyond the military context, including for local/regional governmental players (operators and emergency planners) as well as for volunteers in flood response roles.

Autonomous flight of unmanned aerial vehicles (UAVs) in global position system (GPS)-denied environments has become an increasing research hotspot. This paper aims to realize the indoor fixed-point hovering control and autonomous flight for UAVs based on visual inertial simultaneous localization and mapping (SLAM) and sensor fusion algorithm based on extended Kalman filter.

The fundamental of the proposed method is using visual inertial SLAM to estimate the position information of the UAV and position-speed double-loop controller to control the UAV. The motion and observation models of the UAV and the fusion algorithm are given. Finally, experiments are performed to test the proposed algorithms.

A position-speed double-loop controller is proposed, by fusing the position information obtained by visual inertial SLAM with the data of airborne sensors. The experiment results of the indoor fixed-points hovering show that UAV flight control can be realized based on visual inertial SLAM in the absence of GPS.

A position-speed double-loop controller for UAV is designed and tested, which provides a more stable position estimation and enabled UAV to fly autonomously and hover in GPS-denied environment.

Urban air mobility (UAM) development and deployment into future cities is gaining increasing and relevant interest in the past years. This study, a conceptual paper, aims to report the high-level description of the most relevant UAM application use cases (UCs) emerging from the research activities carried out in the ASSURED UAM project.

The UAM application UCs have been obtained from the ASSURED UAM project dedicated activities that have been carried out to, first, develop suitable operational concepts for UAM deployment in the next decades and, then, to further refine and design the most relevant UCs for UAM deployment in the next decades, leading to the public issue of dedicated overall document.

The ASSURED UAM UCs for UAM deployment in the next decades encompass both public (point-to-point, point-to-everywhere, direct medical transport of people) and private (direct last-mile delivery, advanced last-mile delivery, automatic personal aerial transportation) services applications, evolving in incremental way over time according to three considered time horizons (2025, 2030 and 2035), toward progressive integration into metropolitan transport system.

This paper provides final outline of the ASSURED UAM UCs, starting from the analysis of overall identified possible UAM applications, focusing on the description of the six main UCs considered as relevant for the application under the wider societal benefits point of view. The UCs are described in terms of expected operational environment, needed technological enablers and envisaged regulatory implications.

Subscription offerings are being hailed as the next service growth engine for companies in both business-to-consumer (B2C) and business-to-business (B2B) markets. The study analyzes how a manufacturing firm can develop and implement a scalable service-based subscription business model for B2C and B2B customers alongside its existing product-centric model.

A longitudinal case study is conducted, drawing on 25 in-depth interviews with company executives and dealers in key European markets.

The study outlines an iterative process model for subscription business model innovation. It reveals key events and decisions taken in developing, implementing, and scaling the new business model and how internal and external tensions involving intermediaries arose and were mitigated during the four stages of the process.

The findings highlight the dynamics of business model innovation processes and underscore the importance of organizational learning, collaborative relationships with channel partners, and strategic talent acquisition during business model innovation.

The findings suggest how product-centric firms can implement new service business models alongside existing product models and what this means for partner and customer journey management.

While servitization research predominantly concerns B2B manufacturers, B2C research focuses on digital subscription contexts. The study bridges this divide by investigating the move to subscriptions in both markets.