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This paper aims to use redundant manipulators to solve the challenge of collision avoidance in construction operations such as welding and painting.

In this paper, a null-space-based task-priority adjustment approach is developed to avoid collisions. The method establishes the relative position of the obstacle and the robot arm by defining the “link space,” and then the priority of the collision avoidance task and the end-effector task is adjusted according to the relative position by introducing the null space task conversion factors.

Numerical simulations demonstrate that the proposed method can realize collision-free maneuvers for redundant manipulators and guarantee the tracking precision of the end-effector task. The experimental results show that the method can avoid dynamic obstacles in redundant manipulator welding tasks.

A new formula for task priority adjustment for collision avoidance of redundant manipulators is proposed, and the original task tracking accuracy is guaranteed under the premise of safety.

Shifting to OLSL classes during the pandemic can bring learners ambivalent experiences: negative, positive or both appraisals toward the technologies. However, few studies have examined how ambivalent experiences can influence students' learning behaviors, specifically cyberslacking and active participation. Using the challenge-hindrance stressor framework, this study investigates the impact of challenge and hindrance appraisals on these learning behaviors.

This study uses a mixed methods approach to answer research questions. An interview was conducted to identify the key components of ambivalent appraisals, and a survey was conducted to empirically examine the impact of challenge and hindrance appraisals on learners' behaviors in online live streaming learning (OLSL) contexts. The data of 675 university students were analyzed using structural equation modeling.

This study found that hindrance appraisal leads to cyberslacking while challenge appraisal leads to active participation, but it can also cause cyberslacking. Social presence has a double-edged effect, acting as both a facilitator and inhibitor, strengthening the effect of hindrance appraisal on cyberslacking and the impact of challenge appraisal on active participation.

Prior studies have primarily focused on the negative side (techno-distress) of technology appraisals. This study simultaneously examines the positive side, techno-eustress, on learners' behaviors in OLSL contexts, and explores the moderating effects of social presence. This study contributes to the technostress and technology adaptation literature by revealing how technology-induced ambivalent appraisals impact behavioral responses. It offers important theoretical and practical implications for education tool designers.

Robotic arms play a crucial role in various industrial operations, such as sorting, assembly, handling and spraying. However, traditional robotic arm control algorithms often struggle to adapt when faced with the challenge of dynamic obstacles. This paper aims to propose a dynamic obstacle avoidance method based on reinforcement learning to address real-time processing of dynamic obstacles.

This paper introduces an innovative method that introduces a feature extraction network that integrates gating mechanisms on the basis of traditional reinforcement learning algorithms. Additionally, an adaptive dynamic reward mechanism is designed to optimize the obstacle avoidance strategy.

Validation through the CoppeliaSim simulation environment and on-site testing has demonstrated the method's capability to effectively evade randomly moving obstacles, with a significant improvement in the convergence speed compared to traditional algorithms.

The proposed dynamic obstacle avoidance method based on Reinforcement Learning not only accomplishes the task of dynamic obstacle avoidance efficiently but also offers a distinct advantage in terms of convergence speed. This approach provides a novel solution to the obstacle avoidance methods for robotic arms.

Imitation learning is a powerful tool for planning the trajectory of robotic end-effectors in Cartesian space. Present methods can adapt the trajectory to the obstacle; however, the solutions may not always satisfy users, whereas it is hard for a nonexpert user to teach the robot to avoid obstacles in time as he/she wishes through demonstrations. This paper aims to address the above problem by proposing an approach that combines human supervision with the kernelized movement primitives (KMP) model.

This approach first extracts the reference database used to train KMP from demonstrations by using Gaussian mixture model and Gaussian mixture regression. Subsequently, KMP is used to modulate the trajectory of robotic end-effectors in real time based on feedback from its interaction with humans to avoid obstacles, which benefits from a novel reference database update strategy. The user can test different obstacle avoidance trajectories in the current task until a satisfactory solution is found.

Experiments performed with the KUKA cobot for obstacle avoidance show that this approach can adapt the trajectories of the robotic end-effector to the user’s wishes in real time, including trajectories that the robot has already passed and has not yet passed. Simulation comparisons also show that it exhibits better performance than KMP with the original reference database update strategy.

An interactive learning approach based on KMP is proposed and verified, which not only enables users to plan the trajectory of robotic end-effectors for obstacle avoidance more conveniently and efficiently but also provides an effective idea for accomplishing interactive learning tasks under constraints.

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.

The purpose of this study is to investigate how much visual attention is given to banner ads embedded in Web page content dependent on whether the user’s task is goal- or not goal-oriented, as well as the interplay between attention, banner location, banner click and banner recognition.

The authors used a within-subjects design where 100 participants performed two tasks – reading a news and finding where to click next – on a Web page containing three banner ads embedded into the website content. The authors gathered behavioral and eye-tracking data.

Consumers disregard banner ads when they are performing a focused task (reading news). Visual attention paid to the banners while reading – but not while free browsing – and banner location do not impact ad clicking. In addition, it is not necessary to pay full attention to a banner ad to be able to recognize it afterward.

The strategy of embedding banners in the main content of a Web page leads to higher visual attention when consumers are browsing a Web page compared to a focused task (e.g. reading). It also increases ad recognition over time compared to benchmark levels for ads placed in traditional positions.

Previous studies mainly assessed effectiveness of banners located at the top or lateral of a Web page. The authors used eye tracking as an objective measure of visual attention to banner ads embedded in Web page content and behavioral metrics to assess ad interest and measured ad recognition over time.

Investigar cuánta atención visual se presta a los banners publicitarios incrustados en el contenido de una página Web en función de si la tarea del usuario está orientada a un objetivo o no, así como la interacción entre la atención, la ubicación del banner, el clic en el banner y el reconocimiento del banner.

Se utilizó un diseño entre sujetos en el que 100 participantes realizaban dos tareas – leer una noticia y encontrar dónde hacer clic a continuación – en una página Web que contenía tres banners publicitarios incrustados en el contenido del sitio Web. Se recogieron datos conductuales y de seguimiento ocular.

Los consumidores no prestan atención a los banners publicitarios cuando están realizando una tarea concentrada (leer noticias). La atención visual prestada a los banners durante la lectura – pero no durante la navegación libre – y la ubicación de los banners no influyen en el hecho de hacer clic en los anuncios. Además, no es necesario prestar toda la atención a un banner publicitario para poder reconocerlo después.

Los estudios anteriores evaluaban principalmente la eficacia de los banners situados en la parte superior o lateral de una página Web. Nosotros utilizamos el seguimiento ocular como medida objetiva de la atención visual a los banners incrustados en el contenido de la página Web y métricas de comportamiento para evaluar el interés por el anuncio, y medimos el reconocimiento del anuncio a lo largo del tiempo.

La estrategia de incrustar banners en el contenido principal de una página Web aumenta la atención visual de los consumidores cuando navegan por una página Web en comparación con una tarea específica (por ejemplo, leer). También aumenta el reconocimiento del anuncio a lo largo del tiempo en comparación con los niveles de referencia de los anuncios colocados en posiciones tradicionales.

研究用户对嵌入在网页内容中的横幅广告的视觉注意程度, 取决于用户的任务是否以目标为导向, 以及注意、横幅位置、横幅点击和横幅识别之间的相互作用。

我们采用了主体内设计, 100名参与者在一个含有三个嵌入网站内容的横幅广告的网页上执行两项任务–阅读新闻和寻找下一步的点击位置。我们收集了行为和眼球追踪数据。

消费者在执行重点任务（阅读新闻）时忽略了横幅广告。阅读时对横幅广告的视觉关注–而不是自由浏览时–以及横幅广告的位置并不影响广告点击。此外, 不一定要完全注意横幅广告才能在事后认出它。

以前的研究主要评估位于网页顶部或侧面的横幅广告的效果。我们用眼动仪作为对嵌入网页内容的横幅广告的视觉注意力的客观测量, 用行为指标来评估广告的兴趣, 并测量了广告在一段时间内的识别度。

在网页的主要内容中嵌入横幅广告的策略导致消费者在浏览网页时, 与重点任务（如阅读）相比, 视觉注意力更高。与放置在传统位置的广告的基准水平相比, 它也会随着时间的推移增加广告识别度。

Fast obstacle avoidance path planning is a challenging task for multijoint robots navigating through cluttered workspaces. This paper aims to address this issue by proposing an improved path-planning method based on the distorted space (DS) method, specifically designed for high-dimensional complex environments.

The proposed method, termed topology-preserved distorted space (TP-DS) method, mitigates the limitations of the original DS method by preserving space topology through elastic deformation. By applying distinct spring constants, the TP-DS autonomously shrinks obstacles to microscopic areas within the configuration space, maintaining consistent topology. This enhancement extends the application scope of the DS method to handle complex environments effectively.

Comparative analysis demonstrates that the proposed TP-DS method outperforms traditional methods regarding planning efficiency. Successful obstacle avoidance tasks in the cluttered workspace validate its applicability on a physical 6-DOF manipulator, highlighting its potential for industrial implementations.

The novel TP-DS method generates a topology-preserved collision-free space by leveraging elastic deformation and shows significant capability and efficiency in planning obstacle-avoidance paths in complex application scenarios.

This paper aims to propose a robust kinematic controller based on sliding mode theory designed to solve the trajectory tracking problem and also the formation control using the leader–follower strategy for nonholonomic differential-drive wheeled mobile robots with a PD dynamic controller.

To deal with classical sliding mode control shortcomings, such as the chattering and the requirement of a priori knowledge of the limits of the effects of disturbances, an immune regulation mechanism-inspired approach is proposed to adjust the control effort magnitude adaptively. A simple fuzzy boundary layer method and an adaptation law for the immune portion gain online adjustment are also considered. An obstacle avoidance reactive strategy is proposed for the leader robot, given the importance of the leader in the formation control structure.

To verify the adaptability of the controller, obstacles are distributed along the reference trajectory, and the simulation and experimental results show the effectiveness of the proposed controller, which was capable of generating control signals avoiding chattering, compensating for disturbances and avoiding the obstacles.

The proposed design stands out for the ability to adapt in a case involving obstacle avoidance, trajectory tracking and leader–follower formation control by nonholonomic robots under the incidence of uncertainties and disturbances and also considering that the immune-based control provided chattering mitigation by adjusting the magnitude of the control effort, with adaptability improved by a simple integral-type adaptive law derived by Lyapunov stability analysis.

The purpose of this study is to address the welding demands within large steel structures by presenting a global spatial motion planning algorithm for a mobile manipulator. This algorithm is based on an independently developed wall-climbing robot, which comprises a four-wheeled climbing mobile platform and a six-degree-of-freedom robotic manipulator, ensuring high mobility and operational flexibility.

A convex hull feasible domain constraint is developed for motion planning in the mobile manipulator. For extensive spatial movements, connected sequences of convex polyhedra are established between the composite robot’s initial and target states. The composite robot’s path and obstacle avoidance optimization problem are solved by constraining the control points on B-spline curves. A dynamic spatial constraint rapidlye-xploring random trees-connect (RRTC) motion planning algorithm is proposed for the manipulator, which quickly generates reference paths using spherical spatial constraints at the manipulator’s end, eliminating the need for complex nonconvex constraint modeling.

Experimental results show that the proposed motion planning algorithm achieves optimal paths that meet task constraints, significantly reducing computation times in task conditions and shortening operation times in non-task conditions.

The algorithm proposed in this paper holds certain application value for the realization of automated welding operations within large steel structures using mobile manipulator.

This study attempts to examine how employee orientations and paranoid arousal intersect and influence the voice behaviors of employees, as they significantly influence workplace practices.

Drawing on a multi-source and multi-time-based survey, the data were collected from 37 supervisors and 270 employees in 80 branches of 11 microfinance banks. SmartPLS-4.1 was used to test the measurement and structural model. A total of 307 questionnaires were used for the data analysis.

This research presents the contrasting results of employee orientations with voice behavior. Results support the direct relationship between paranoid arousal and promotive-prohibitive voice behaviors. However, no support was found for approach-avoidance orientation and promotive-prohibitive voice behaviors.

Employees' ability to voice-out in organizations depends on several motivational factors, consolidated into three areas – organizational culture, leadership, and individual traits. Organizations must implement a culture of transparency and open communication. Participative and inclusive leadership styles increase the quality of relationships with employees, stimulating a promotive voice. A support system for employees is needed to advance in their careers, increasing promotive behaviors.

The study adds value to the existing body of knowledge by studying approach-avoidance orientations and voice behaviors through the lens of paranoid arousal in the framework.