Search Results (883)

Pro-environmental behavior (PEB) is crucial for achieving a sustainable future. Although prior research has investigated the relationship between virtual nature exposure and PEB, empirical findings have been inconsistent; some studies suggest a positive association, while others report null effects. Furthermore, the use of laboratory tasks to assess PEB often risks conflating it with cooperative behavior, potentially undermining the validity of the conclusions. To address these limitations, this study employed a double-randomization design, utilizing the Greater Good Game (GGG) as a measure of PEB. This research comprised two main studies, each consisting of two sub-studies. Study 1 examined the direct effect of virtual nature exposure on PEB (Study 1a) and the moderating role of familiarity with nature exposure (Study 1b). Study 2 included two phases: Study 2a investigated the effects of familiarity with nature exposure on both nature connectedness and PEB, while Study 2b implemented a randomized pre–post-intervention design to manipulate nature connectedness and examine its causal effect on PEB. Results indicated that virtual nature exposure more effectively enhanced PEB when participants were exposed to familiar virtual environments, and nature connectedness mediated this relationship. These findings provide insights into the reasons for previous inconsistencies and offer valuable practical implications for educational programs and policies aimed at promoting sustainable behaviors. Full article

Multi-robot systems (MRSs) are widely applied in the fields of joint search and rescue, exploration, and carrying. To achieve cooperative tasks and guarantee physical safety, the robots should avoid inter-robot collisions as well as robot–obstacle collisions. However, the collision/obstacle avoidance task usually conflicts with the given cooperative task, which poses a significant challenge for the achievement of multi-robot cooperative tasks. This paper provides a review of the state-of-the-art results in the collision/obstacle avoidance cooperative control of MRSs. Specifically, the latest developments of collision/obstacle avoidance cooperative control are summarized according to different planning strategies and classified into three categories: (1) offline planning; (2) receding horizon planning; and (3) reactive control. Furthermore, specific design solutions for existing reference/command governors are highlighted to demonstrate the latest research advances. Finally, several challenging issues are discussed to guide future research. Full article

Cloud computing, a superset of heterogeneous distributed computing, allows sharing of geographically dispersed resources across multiple organizations on a rental basis using virtualization as per demand. In cloud computing, workflow allocation to achieve the optimum schedule has been reported to be NP-hard. This paper proposes a Levelized Multiple Workflow Heterogeneous Earliest Finish Time (LMHEFT) model to optimize makespan in the cloud computing environment. The model has two phases: task prioritization and task allocation. The task prioritization phase begins by dividing workflows into the number of partitions as per the level attribute; after that, upward rank is employed to determine the partition-wise task allocation order. In the allocation phase, the best-suited virtual machine is determined to offer the lowest finish time for each task in partition-wise mapping to minimize the workflow task’s completion time. The model considers the inter-task communication between the cooperative workflow tasks. A comparative performance evaluation of LMHEFT has been conducted with the competitive models from the literature implemented in MATLAB, i.e., heterogeneous earliest finish time (HEFT) and dynamic level scheduling (DLS), on makespan, flowtime, and utilization. The experimental findings indicate that LMHEFT surpasses HEFT and DLS in terms of makespan 15.51% and 85.12% when varying the number of workflows, 41.19% and 86.73% when varying depth levels, and 13.74% and 80.24% when varying virtual machines, respectively. Further statistical analysis has been carried out to confirm the hypothesis developed in the simulation study by using normality tests, homogeneity tests, and the Kruskal–Wallis test. Full article

In the globalized, technologically advanced landscape, coopetition—simultaneously cooperating and competing—has become a key strategy for innovation and enhanced value creation. This research focuses on the impact of technology-driven coopetition networks in the Portuguese ornamental stone sector, using a framework based on Service-Dominant Logic (S-D Logic). It emphasizes the importance of resource integration, service exchange, and institutional arrangements in successful coopetition. Employing a two-phase experimental approach with selected small and medium enterprises (SMEs), this study assesses customer perceptions of product quality under traditional best practices versus those enabled by technology-driven coopetition networks. The results indicate a notable improvement in the customer-perceived quality and outcome consistency. The statistical analysis shows that these networks allow firms to better align with customer expectations, optimize resource allocation, and improve operational coordination. The findings highlight the strategic potential of coopetition networks, particularly when augmented by advanced technologies like IoT-based systems. These networks facilitate sustainable value co-creation and operational resilience by enabling firms to share expertise, distribute tasks, and synchronize efforts. This research contributes to the coopetition and S-D Logic literature by offering a practical framework for firms aiming to boost competitiveness and sustain growth in dynamic service ecosystems. Full article

The problem of allocating multiple UAV tasks is a complex combinatorial optimization challenge, involving various constraints. This paper presents an autonomous multi-UAV cooperative task allocation method based on an improved Great Wall Construction Algorithm. A model integrating battlefield environmental factors, 3D terrain data, and threat assessments is developed for optimized task allocation and trajectory planning. The algorithm is enhanced using a good point set initialization strategy, Gaussian distribution estimation, and a Cauchy reorganization variant. The simulation results show that replacing straight-line distances with actual flight distances leads to more rational mission sequences, improving combat effectiveness under realistic terrain and threat conditions. The enhanced algorithm demonstrates superior accuracy and faster convergence. Full article

The increasing volume of traffic has led to severe challenges, including traffic congestion, heightened energy consumption, increased air pollution, and prolonged travel times. Addressing these issues requires innovative approaches for optimizing road network utilization. While Deep Reinforcement Learning (DRL)-based methods have shown remarkable effectiveness in dynamic scenarios like traffic management, their primary focus has been on single-agent setups, limiting their applicability to real-world multi-agent systems. Managing agents and fostering collaboration in a multi-agent reinforcement learning scenario remains a challenging task. This paper introduces a cooperative multi-agent federated reinforcement learning algorithm named FLDQN to address the challenge of agent cooperation by solving travel time minimization challenges in dynamic multi-agent reinforcement learning (MARL) scenarios. FLDQN leverages federated learning to facilitate collaboration and knowledge sharing among intelligent agents, optimizing vehicle routing and reducing congestion in dynamic traffic environments. Using the SUMO simulator, multiple agents equipped with deep Q-learning models interact with their local environments, share model updates via a federated server, and collectively enhance their policies using unique local observations while benefiting from the collective experiences of other agents. Experimental evaluations demonstrate that FLDQN achieves a significant average reduction of over 34.6% in travel time compared to non-cooperative methods while simultaneously lowering the computational overhead through distributed learning. FLDQN underscores the vital impact of agent cooperation and provides an innovative solution for enabling agent cooperation in a multi-agent environment. Full article

Background and Objectives: The aim of this study was to determine the effect of immersive virtual reality used as a short-term multifaceted activity with a focus on motor and cognitive function in patients with Parkinson’s Disease. The sub-objective focused on quality of life in the study group of patients. Materials and Methods: Nineteen patients (64.2 ± 12.8 years) were included in this study. Inclusion criteria for this study: adult patients in Hoehn and Yahr’s stage 1–3, cooperative, with stable health status, independent and mobile. IVR therapy was performed twice a week for 20 min for one month. Input and output measurements were taken within 14 days of starting or ending therapy. The 10 Meter Walk test was used to examine and assess both comfortable and fast walking, and the Timed Up and Go (TUG) + s dual task was applied to quickly assess the highest possible level of functional mobility. The Berg Balance Scale test (BBS) was used to assess balance with a 14-item balance scale containing specific movement tasks. The standardized Parkinson’s Disease Questionnaire (PDQ-39) was used to assess quality of life. Data were processed in the PAST program using a nonparametric paired Wilcoxon test. The significance level was set at α = 0.05. The value of the r score was used to evaluate the effect size. Results: A significant reduction in the time in the fast walk 10MWT (p = 0.006; r = 0.63) and TUG (p < 0.001; r = 0.80) parameter were found after therapy. Significant improvement in the BBS score was found after applied therapy (p = 0.016; r = 0.55). In the PDQ-39 questionnaire, significant improvements were found in the study group after therapy in the domains of mobility (p = 0.027; r = 0.51) and emotional well-being (p = 0.011; r = 0.58). Conclusions: The results of this study indicate a positive effect of virtual reality therapy on balance and gait, which is also good in terms of reducing the risk of falls in the study group. Therapy also promoted quality of life in the study group. Full article

Enabling robots to work safely close to humans requires both adherence to safety standards and the development of appropriate strategies to plan and control robot movements in accordance with human movements. Collaboration between humans and robots in a shared environment is a joint activity aimed at completing specific tasks, requiring coordination, synchronisation, and sometimes physical contact, in which each party contributes its own skills and resources. Among the most challenging tasks of human–robot cooperation is the co-transport of deformable materials such as fabrics. This paper proposes a method for generating the trajectory of a collaborative manipulator. The method is designed for the co-transport of materials such as fabrics. It combines a near time-optimal control strategy that ensures responsiveness in following human actions while simultaneously guaranteeing compliance with the safety limits imposed by current regulations. The combination of these two elements results in a viable co-transport solution which preserves the safety of human operators. This is achieved by constraining the path of the robot trajectory with prescribed velocities and accelerations while simultaneously ensuring a near time-optimal control strategy. In short, the robot movement is generated in such a way as to ensure both the tracking of humans in the co-transportation task and compliance with safety limits. As a first attempt to adopt the proposed approach to integrate time-optimal strategies into human–robot interaction, the simulations and preliminary experimental result obtained are promising. Full article

This study presents a cooperative system where drones and ground robots share information to efficiently complete tasks in environments that challenge the capabilities of a single robot. Drones focus on exploring high-interest areas for ground robots, generating occupancy grid maps and identifying high-risk routes. Ground robots use this information to evaluate and adapt routes as needed. Flexible action planning through behavior trees enables the robots to respond dynamically to environmental changes, facilitating spontaneous and adaptable cooperation. Experiments with real robots confirmed the system’s performance and adaptability to various settings. Specifically, when high-risk areas were identified from drone provided information, ground robots generated alternative routes to bypass these zones, demonstrating the system’s capacity to navigate complex paths while minimizing risks. This establishes a basis for scaling to larger environments. The proposed system is expected to improve the safety and efficiency of robot operations by enabling multiple robots to accomplish complex tasks collaboratively-tasks that would be difficult or time consuming for an individual robot. The findings demonstrate the potential for multi-robot cooperation to enhance task execution in challenging environments and provide a framework for future research on effective role sharing and information exchange in autonomous systems. Full article

Traditional methods for estimating tea yield mainly rely on manual sampling surveys and empirical estimation, which are labor-intensive and time-consuming. Accurately estimating fresh tea production in different seasons has become a challenging task. It is possible to estimate the seasonal yield of tea at the field scale by using the spatial resolution of 10 m, 5-day revisit period and rich spectral information of Sentinel-2 imagery. This study integrated Sentinel-2 images and uncrewed aerial vehicle (UAV) RGB imagery to develop six regression models at the field scale, which were employed for the estimation of seasonal and annual fresh tea yields of the Yunlong Tea Cooperatives in Yixiang Town, Pu’er City, China. Firstly, we gathered fresh tea production data from 133 farmers in the cooperative over the past five years and obtained UAV RGB and Sentinel-2 imagery. Secondly, 23 spectral features were extracted from Sentinel-2 images. Based on the UAV images, the parcel of each farmer was positioned and three topographic features of slope, aspect, and elevation were extracted. Subsequently, these 26 features were screened using the random forest algorithm and Pearson correlation analysis. Thirdly, we applied six different regression algorithms to establish fresh tea yield models for each season and evaluated their estimation accuracy. The results showed that random forest regression models were the optimal choice for estimating spring and summer yields, with the spring model achieving an R² value of 0.45, an RMSE of 40.38 kg/acre, and an rRMSE of 40.79%. Similarly, the summer model achieved an R² value of 0.5, an RMSE of 78.46 kg/acre, and an rRMSE of 39.81%. For autumn and annual yield estimation, voting regression models demonstrated superior performance, with the autumn model achieving an R² value of 0.42, an RMSE of 70.6 kg/acre, and an rRMSE of 39.77%, and the annual model attained an R² value of 0.47, an RMSE of 168.7 kg/acre, and an rRMSE of 34.62%. This study provides a promising new method for estimating fresh tea yield in different seasons at the field scale. Full article

With the widespread application of unmanned aerial vehicles (UAV) in surveying, disaster search and rescue, agricultural spraying, war reconnaissance, and other fields, coverage path planning is one of the most important problems to be explored. In this paper, a large-area coverage path planning (CCP) method based on vehicle–UAV collaboration is proposed. The core idea of the proposed method is adopting a divide-and conquer-strategy to divide a large area into small areas, and then completing efficient coverage scanning tasks through the collaborative cooperation of vehicles and UAVs. The supply points are generated and adjusted based on the construction of regular hexagons and a Voronoi diagram, and the segmentation and adjustment of sub-areas are also achieved during this procedure. The vehicle paths are constructed based on the classical ant colony optimization algorithm, providing an efficient way to traverse all supply points within the coverage area. The classic zigzag CCP method is adopted to fill the contours of each sub-area, and the UAV paths collaborate with vehicle supply points using few switching points. The simulation experiments verify the effectiveness and feasibility of the proposed vehicle–UAV collaboration CCP method, and two comparative experiments demonstrate that the proposed method excels at large-scale CCP scenarios, and achieves a significant improvement in coverage efficiency. Full article

This study presents a fault-tolerant control system for Selective Compliance Assembly Robot Arm (SCARA) robots, ensuring operational continuity in cooperative tasks. It is evaluated in five scenarios: normal operation, failures without reconfiguration, and with active reconfiguration. The system employs redundant actuators, differential gears, torque limiters, and rapid detection and reconfiguration algorithms. Simulations in MATLAB R2024a demonstrated reconfiguration times of 0.5 s and reduced trajectory errors (0.0042 m on the X-axis for Robot 1), achieving efficiency above 99%. Nonlinear Model Predictive Controllers (NLMPCs) and Adaptive Sliding Mode Control (ASMC) were compared, with NLMPC excelling in stability and ASMC in precision. The system showcased high productivity in pick-and-place tasks, even under critical failures, establishing itself as a robust solution for industrial environments requiring high reliability and advanced automation. Full article

The complexity and uncertainty of the marine environment pose significant challenges to the stability and coverage of communication links. Due to the limited coverage range of traditional onshore base stations (BSs) in marine environments, relay technology has become an essential approach to extending communication coverage. However, the rapid variations in marine wireless channels and the complexity of hydrological conditions make it extremely difficult to obtain accurate channel state information (CSI). In particular, dynamic environmental factors such as waves, tides and wind speed cause channel parameters to fluctuate significantly over time. To address these challenges, this paper proposes a cooperative communication strategy based on ships and designs a novel channel modeling method to accurately capture the characteristics of marine wireless channels. Furthermore, a deep learning-based optimization scheme is proposed, which formulates the relay selection problem as a spatiotemporal classification task. By integrating the spatial positions of candidate relays and their communication conditions, the proposed scheme enables real-time selection of the optimal relay while evaluating link connectivity probabilities under hydrological influences. Simulation results confirm that the proposed method achieves high accuracy even in rapidly changing marine environments. Full article

Hyper-redundant space manipulators (HRSMs), with their extensive degrees of freedom, offer a promising solution for complex space operations such as on-orbit assembly and manipulation of non-cooperative objects. A critical challenge lies in achieving stable and effective grasping configurations, particularly when dealing with irregularly shaped objects in microgravity. This study addresses these challenges by developing a segmented hybrid impedance control architecture tailored to multi-point contact scenarios. The proposed framework reduces the contact forces and enhances object manipulation, enabling the secure handling of irregular objects and improving operational reliability. Numerical simulations demonstrate significant reductions in the contact forces during initial engagements, ensuring stable grasping and effective force regulation. The approach also enables precise trajectory tracking, robust collision avoidance, and resilience to external disturbances. The complete non-linear dynamics of the HRSM system are derived using the Kane method, incorporating both the free-space and constrained motion phases. These results highlight the practical capabilities of HRSM systems, including their potential to grasp and manipulate obstacles effectively, paving the way for applications in autonomous on-orbit servicing and assembly tasks. By integrating advanced control strategies and robust stability guarantees, this work provides a foundation for the deployment of HRSMs in real-world space operations, offering greater versatility and efficiency in complex environments. Full article

The distributed tracking of multiple unmanned aerial vehicles (UAVs) is a hotspot due to its broad applications in various fields, while continuous communication among UAVs is often impractical, especially in time-sensitive tasks or environments with limited bandwidth. With this in mind, this paper presents a finite-time leader-following distributed tracking control scheme for general multi-agent systems, with a particular emphasis on its application for networked UAVs. Theoretically, a dynamic event-triggered mechanism is proposed, which features a novel finite-time stable dynamic variable within its triggering rule, ensuring that neither controller updates nor trigger detection requires continuous communication. This event-triggered finite-time controller facilitates efficient network resource management and timely mission response in UAV cooperation, enhancing adaptability to onboard wireless communication networks and time-sensitive tasks. The method allows for the customization of parameters in the internal dynamic variables to adjust the convergence rate and event-triggering frequency of the system, while also preventing Zeno behavior. Moreover, a Lyapunov-based analysis is conducted to theoretically verify the finite-time stability of the closed-loop system and its applicability in directed communication networks. Finally, some numerical simulations are performed to validate the effectiveness of the proposed distributed control scheme for networked multi-UAV systems. Full article