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The paper focuses on the issue of manual rebar-binding tasks in the construction industry, which are marked by high labor intensity, high costs and inefficient operations. The rebar-binding robots that are currently available are not fully mature. Most of them can only bind one or two nodes in one position, which leads to significant time wastage in movement. Based on a new type of rebar-binding robot, this paper aims to propose a new movement and binding control that reduces manpower and enhances efficiency.

The robot is combined with photoelectric sensors, travel switches and other sensors. It is supposed to move accurately and run in a limited area on the rebar mesh through logical judgment, speed control and position control. Machine vision is used by the robot to locate the rebar nodes and then adjusts the binding-gun position to ensure that multiple rebar nodes are bound sequentially.

By moving on the rebar mesh with accuracy, the robot meets the positioning accuracy requirements of the binding module, with experimental testing accuracy within 5 mm. Furthermore, its ability to bind four rebar nodes in one place results in a high efficiency and a binding effect that meets building standards.

The innovative design of the robot can adapt itself to the rebar mesh, move accurately to the target position and bind four nodes at that position, which reduces the number of movements on the mesh. Repetitive and heavy rebar-binding tasks can be efficiently completed by the robot, which saves human resources, reduces worker labor intensity and reduces construction overhead. It provides a more feasible and practical solution for using robots to bind rebar nodes.

This study aims to tackle control challenges in soft robots by proposing a visually-guided reinforcement learning approach. Precise tip trajectory tracking is achieved for a soft arm manipulator.

A closed-loop control strategy uses deep learning-powered perception and model-free reinforcement learning. Visual feedback detects the arm’s tip while efficient policy search is conducted via interactive sample collection.

Physical experiments demonstrate a soft arm successfully transporting objects by learning coordinated actuation policies guided by visual observations, without analytical models.

Constraints potentially include simulator gaps and dynamical variations. Future work will focus on enhancing adaptation capabilities.

By eliminating assumptions on precise analytical models or instrumentation requirements, the proposed data-driven framework offers a practical solution for real-world control challenges in soft systems.

This research provides an effective methodology integrating robust machine perception and learning for intelligent autonomous control of soft robots with complex morphologies.

With Industry 4.0 and the extensive rise of smart technologies, we are seeing remarkable transformations in work practices and workplaces. Scholars report the phenomenal progress of smart technologies. At the same time, we can hear the rhetoric emphasising their potential threats. This study focusses on how and where intelligent machines are leveraged in the workplace, how humans co-working with intelligent machines are affected and what they believe can be done to mitigate the risks of the increased use of intelligent machines.

We conducted in-depth interviews with 15 respondents working in various leadership capacities associated with intelligent machines and technologies. Using NVivo, we coded and churned out the themes from the qualitative data collected.

This study shows how intelligent machines are leveraged across different industries, ranging from chatbots, intelligent sensors, cognitive systems and computer vision to the replica of the entire human being. They are used end-to-end in the value chain, increasing productivity, complementing human workers’ skillsets and augmenting decisions made by human workers. Human workers experience a blend of positive and negative emotions whilst co-working with intelligent machines, which influences their job satisfaction level. Organisations adopt several anticipatory strategies, like transforming into a learning organisation, identifying futuristic technologies and upskilling their human workers, regularly conducting social learning events and designing accelerated career paths to embrace intelligent technologies.

This study seeks to understand the emotional and practical implications of the use of intelligent machines by humans and how both entities can integrate and complement each other. These insights can help organisations and employees understand what future workplaces and practices will look like and how to remain relevant in this transformation.

This paper aims to systematically review the application of artificial intelligence (AI) in the tourism industry. By integrating human–computer interaction, machine learning, big data and other relevant technologies, the study establishes a comprehensive research framework that explores the systematic connections between AI and various facets of tourism.

This paper conducts a keyword co-occurrence analysis of 4,048 articles related to AI in tourism. The analysis identifies and classifies dominant topics, which are further refined through thematic literature review and manual coding for detailed discussion.

The analysis reveals five main topics: AI’s impact on tourist experience, AI in tourism marketing and prediction, AI in destination management, AI’s role in tourism enterprises and AI integration in strategic and regulatory framework. Each topic is reviewed to construct an integrated discussion that maps the current landscape and suggests directions for future research.

This paper transcends the fragmented discourse commonly found in the literature by establishing a unified framework that not only enhances understanding of the existing methodologies, theories and applications of AI in tourism but also identifies critical areas for breakthroughs, aiming to inspire a more humane and sustainable integration of AI in the tourism industry.

本文旨在系统回顾人工智能（AI）在旅游业中的应用。通过整合人机交互、机器学习、大数据和其他相关技术, 本研究建立了一个全面的研究框架, 探索人工智能与旅游业各方面之间的系统联系。

本文对4048篇与旅游业人工智能相关的文章进行了关键词共现分析。分析确定了主要议题并对其进行了分类, 然后通过主题文献梳理和手动编码对其进行了进一步完善, 以便进行详细讨论。

分析揭示了五个主要主题：人工智能与旅游体验、人工智能与旅游营销和预测、人工智能与目的地管理、人工智能与旅游企业, 以及人工智能在战略和监管框架中的整合。每个主题都进行了回顾, 以构建一个综合讨论, 勾勒出当前的研究格局, 并提出了未来的研究方向。

研究力图突破目前关于旅游与人工智能的碎片化讨论, 建立了一个统一的框架, 旨在加强对旅游业中人工智能现有方法、理论和应用的理解, 还点明了需要突破的关键领域, 以助力旅游业与人工智能共同创造更加人性化和可持续发展的前景。

Este artículo pretende revisar sistemáticamente la aplicación de la inteligencia artificial (IA) en el sector turístico. Mediante la integración de la interacción humano-ordenador, el aprendizaje automático, big data y otras tecnologías relevantes, el estudio establece un marco de investigación exhaustivo que explora las conexiones sistemáticas entre la IA y diversas facetas del turismo.

Este trabajo realiza un análisis de co-ocurrencia de palabras clave de 4.048 artículos relacionados con la IA en el turismo. El análisis identifica y clasifica los temas dominantes, sobre los que se profundiza mediante una revisión temática de la literatura y una codificación manual para su discusión detallada.

El análisis presenta cinco temas principales: El impacto de la IA en la experiencia turística, la IA en el marketing y la predicción turística, la IA en la gestión de destinos, el papel de la IA en las empresas turísticas y la integración de la IA en el marco estratégico y normativo. Cada tema se revisa para construir un debate integrado que trace el panorama actual y sugiera direcciones para futuras investigaciones.

Este artículo expande el análisis fragmentado que suele encontrarse en la bibliografía al establecer un marco unificado que no sólo mejora la comprensión de las metodologías, teorías y aplicaciones existentes de la IA en el turismo, sino que también identifica las áreas críticas para los avances, con el objetivo de inspirar una integración más humana y sostenible de la IA en la industria turística.

The advancement in science and technology has led to the existence and continuous growth of various physical machines and now computer programs which are supposed to perform once thought unbelievable multi-tasks for humans.

Many firms, governments, industries and syndicates are switching to automated process for achieving maximum output and having minimum cost and errors in the manufacturing and various other processes, hence the role of human involvement is getting decreased. With the Industry 4.0 standards being opted, industry gurus have been forecasting the limiting power of human labour and interaction and fears exist complete replacement of human beings from the working environment by robots and automated machines.

Artificial intelligence has aggressively overtaken almost all fields of businesses and human resource (HR) hasn't been either immune to that. Robotics is an important factor as well.

Technology implementation has its own benefits and negative impacts which is creating fears among various professionals with regards to their complete replacement by machines.

The following paper looks deeply into various researches performed by scholars to have a thorough knowledge of present-day status of human–machine collaboration and the challenges organizations are facing. It will help in understanding the current scenario of HR with modern technologies.

This paper aims to propose a lightweight, high-accuracy object detection model designed to enhance seam tracking quality under strong arcs and splashes condition. Simultaneously, the model aims to reduce computational costs.

The lightweight model is constructed based on Single Shot Multibox Detector (SSD). First, a neural architecture search method based on meta-learning and genetic algorithm is introduced to optimize pruning strategy, reducing human intervention and improving efficiency. Additionally, the Alternating Direction Method of Multipliers (ADMM) is used to perform structural pruning on SSD, effectively compressing the model with minimal loss of accuracy.

Compared to state-of-the-art models, this method better balances feature extraction accuracy and inference speed. Furthermore, seam tracking experiments on this welding robot experimental platform demonstrate that the proposed method exhibits excellent accuracy and robustness in practical applications.

This paper presents an innovative approach that combines ADMM structural pruning and meta-learning-based neural architecture search to significantly enhance the efficiency and performance of the SSD network. This method reduces computational cost while ensuring high detection accuracy, providing a reliable solution for welding robot laser vision systems in practical applications.

This paper aims to address the safety concerns of path-planning algorithms in dynamic obstacle warehouse environments. It proposes a method that uses improved artificial potential fields (IAPF) as expert knowledge for an improved deep deterministic policy gradient (IDDPG) and designs a hierarchical strategy for robots through obstacle detection methods.

The IAPF algorithm is used as the expert experience of reinforcement learning (RL) to reduce the useless exploration in the early stage of RL training. A strategy-switching mechanism is introduced during training to adapt to various scenarios and overcome challenges related to sparse rewards. Sensor inputs, including light detection and ranging data, are integrated to detect obstacles around waypoints, guiding the robot toward the target point.

Simulation experiments demonstrate that the integrated use of IDDPG and the IAPF method significantly enhances the safety and training efficiency of path planning for mobile robots.

This method enhances safety by applying safety domain judgment rules to improve APF’s security and designing an obstacle detection method for better danger anticipation. It also boosts training efficiency through using IAPF as expert experience for DDPG and the classification storage and sampling design for the RL experience pool. Additionally, adjustments to the actor network’s update frequency expedite convergence.

The accurate prediction of driving torque demand is essential for the development of motion controllers for mobile robots on complex terrains. This paper aims to propose a hybrid model of torque prediction, adaptive EC-GPR, for mobile robots to address the problem of estimating the required driving torque with unknown terrain disturbances.

An error compensation (EC) framework is used, and the preliminary prediction driving torque value is achieved using Gaussian process regression (GPR). The error is predicted using a continuous hidden Markov model to generate compensation for the prediction residual caused by terrain disturbances and uncertainties. As the final step, a gain coefficient is used to adaptively tune the significance of the compensation term through parameter resetting. The proposed model is verified on a sample set, including the driving torque of a mobile robot on three different sandy terrains with two driving modes.

The results show that the adaptive EC-GPR yields the highest prediction accuracy when compared with existing methods.

It is demonstrated that the proposed model can predict the driving torque accurately for mobile robots in an unconstructed environment without terrain identification.

This paper tries to retrospect the mounting application of machine learning (ML) and artificial intelligence (AI) in the human resource management area. The document applies bibliometric analysis and uses relational techniques to explore dimensions of documents in the field. The results highlight publication trends, most impactful authors, countries and institutes in the research area. The science mapping along with co-citation and bibliometric coupling analysis revealed major developments in the field. The thematic mapping and trend analysis highlighted the past and emerging trends towards significant and impactful research in the areas of robotics, big data, AI and data analytics. This paper sets the base for future researchers by coordinating and combining various past researches to help in understanding the evolution of ML and AI in human resource management and expansion of knowledgebase.

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.