Search Results (113)

Precise odor source localization in turbulent and sparse environments plays a vital role in enabling robotic systems for hazardous chemical monitoring and effective disaster response. To address this, we propose Cooperative Gravitational-Rényi Infotaxis (CGRInfotaxis), a distributed decision-optimization framework that combines multi-agent collaboration with hybrid cognitive strategy to improve search efficiency and robustness. The method integrates a gravitational potential field for rapid source convergence and Rényi divergence-based probabilistic exploration to handle sparse detections, dynamically balanced via a regulation factor. Particle filtering optimizes posterior probability estimation to autonomously refine search areas while preserving computational efficiency, alongside a distributed interactive-optimization mechanism for real-time decision updates through agent cooperation. The algorithm’s performance is evaluated in scenarios with fixed and randomized odor source locations, as well as with varying numbers of agents. Results demonstrate that CGRInfotaxis achieves a near-100% success rate with high consistency across diverse conditions, outperforming existing methods in stability and adaptability. Increasing the number of agents further enhances search efficiency without compromising reliability. These findings suggest that CGRInfotaxis significantly advances multi-agent odor source localization in turbulent, sparse environments, offering practical utility for real-world applications. Full article

Due to the limited endurance of UAVs, especially in scenarios involving large areas and dense target nodes, it is challenging for multiple UAVs to complete diverse tasks while ensuring timely execution. Toward this, we propose a cross-platform system consisting of an aircraft carrier (AC) and multiple UAVs, which makes unified task planning for included heterogeneous platforms to maximize the efficiency of the entire combat system. The carrier-based UAV swarm mission planning problem is formulated to minimize completion time and resource utilization, taking into account large-scale targets, multi-type tasks, and multi-obstacle environments. Since the problem is complex, we design a decoupled framework to simplify the solution by decomposing it into two levels: upper-level AC path planning and bottom-level multi-UAV cooperative mission planning. At the upper level, a drop point determination method and a discrete genetic algorithm incorporating improved A* (DGAIIA) are proposed to plan the AC’s path in the presence of no-fly zones and radar threats. At the bottom level, an improved differential evolution algorithm with a market mechanism (IDEMM) is proposed to minimize task completion time and maximize UAV utilization. Specifically, a dual-switching search strategy and a neighborhood-first buying-and-selling mechanism are developed to improve the search efficiency of the IDEMM. Simulation results validate the effectiveness of both the DGAIIA and IDEMM. An animation of the simulation results is available at simulation section. Full article

Unmanned aerial vehicles (UAVs), commonly known as drones, offer unprecedented flexibility for complex missions such as area surveillance, search and rescue, and cooperative inspection. This paper presents a unified evaluation framework for the comparison of centralized and distributed task allocation algorithms specifically tailored to multi-UAV operations. We first contextualize the classical assignment problem (AP) under UAV mission constraints, including the flight time, propulsion energy capacity, and communication range, and evaluate optimal one-to-one solvers including the Hungarian algorithm, the Bertsekas $ϵ$-auction algorithm, and a minimum cost maximum flow formulation. To reflect the dynamic, uncertain environments that UAV fleets encounter, we extend our analysis to distributed multi-UAV task allocation (MUTA) methods. In particular, we examine the consensus-based bundle algorithm (CBBA) and a distributed auction 2-opt refinement strategy, both of which iteratively negotiate task bundles across UAVs to accommodate real-time task arrivals and intermittent connectivity. Finally, we outline how reinforcement learning (RL) can be incorporated to learn adaptive policies that balance energy efficiency and mission success under varying wind conditions and obstacle fields. Through simulations incorporating UAV-specific cost models and communication topologies, we assess each algorithm’s mission completion time, total energy expenditure, communication overhead, and resilience to UAV failures. Our results highlight the trade-off between strict optimality, which is suitable for small fleets in static scenarios, and scalable, robust coordination, necessary for large, dynamic multi-UAV deployments. Full article

Path planning for picking safflowers is a key component in ensuring the efficient operation of robotic safflower-picking systems. However, existing single-arm picking devices have become a bottleneck due to their limited operating range, and a breakthrough in multi-arm cooperative picking is urgently needed. To address the issue of inadequate adaptability in current path planning strategies for dual-arm systems, this paper proposes a novel path planning method for dual-arm picking (LTSACO). The technique centers on a dynamic-weight heuristic strategy and achieves optimization through the following steps: first, the K-means clustering algorithm divides the target area; second, the heuristic mechanism of the Ant Colony Optimization (ACO) algorithm is improved by dynamically adjusting the weight factor of the state transition probability, thereby enhancing the diversity of path selection; third, a 2-OPT local search strategy eliminates path crossings through neighborhood search; finally, a cubic Bézier curve heuristically smooths and optimizes the picking trajectory, ensuring the continuity of the trajectory’s curvature. Experimental results show that the length of the parallelogram trajectory, after smoothing with the Bézier curve, is reduced by 20.52% compared to the gantry trajectory. In terms of average picking time, the LTSACO algorithm reduces the time by 2.00%, 2.60%, and 5.60% compared to DCACO, IACO, and the traditional ACO algorithm, respectively. In conclusion, the LTSACO algorithm demonstrates high efficiency and strong robustness, providing an effective optimization solution for multi-arm cooperative picking and significantly contributing to the advancement of multi-arm robotic picking systems. Full article

Decentralized desalination systems driven by renewable energy sources have surfaced as a feasible way to alleviate water scarcity in arid and rural areas. This bibliometric study aims to clarify the research trends, conceptual frameworks, and cooperative dynamics in the scientific literature on decentralized renewable-powered desalination techniques. Using a thorough search approach, 1354 papers were found. Duplicates, thematically unrelated works, and entries with poor information were removed using the PRISMA 2020 framework. A selected 832 relevant papers from a filtered dataset were chosen for in-depth analysis. Quantitative measures were obtained by means of Bibliometrix; network visualisation was obtained by means of VOSviewer (version 1.6.19) and covered co-authorship, keyword co-occurrence, and citation structures. Over the previous 20 years, the data show a steady rise in academic production, especially in the fields of environmental science, renewable energy engineering, and water treatment technologies. Author keyword co-occurrence mapping revealed strong theme clusters centred on solar stills, thermoelectric modules, reverse osmosis, and off-grid systems. Emphasizing current research paths and emerging subject borders, this paper clarifies the intellectual and social structure of the field. The outcomes are expected to help policy creation, cooperative projects, and strategic planning meant to hasten innovation in sustainable and decentralized water desalination. Full article

As climate change continues to endanger a sustainable global condition, a growing literature investigates how to pursue green practices to fight its effects. Individuals are the essential starting point for such bottom-up attempts, with their attitudes towards sustainability driving pro-environmental behaviors (PEBs). Objectives: Based on the available relevant literature, this scoping review aims to delve into the processes underlying people’s sustainable decision-making (SDM) associated with PEBs. Methods: A scientific literature search was performed through (a) an active database search and (b) the identification of studies via reference and citation tracking. Results were screened and selected in Rayyan. Results: Included articles (n = 30) heterogeneously reported cognitive and neural aspects of SDM shaping PEBs. These proved to (a) recruit brain areas involved in mentalizing and moral cognition (likely because of their role in processing the interplay between personal and contextual factors rather than moral considerations in themselves); (b) undergo the same modulatory influences shaping other kinds of prosocial/cooperative behaviors; and (c) include brain areas involved in attentional/monitoring and emotional/motivational processes, alongside those consistently associated with decision-making processes. Conclusions: These results help interpret the available evidence on the neuro-cognitive bases of SDM while focusing on potential interventions to foster better practices and mitigate the adverse repercussions of climate change on human and global health. Full article

The inherent intermittency and uncertainty of renewable energy generation pose significant challenges to the safe and stable operation of power grids, particularly when power demand does not match renewable energy supply, leading to issues such as wind and solar power curtailment. To effectively promote the consumption of renewable energy while leveraging electric vehicles (EVs) in virtual power plants (VPPs) as distributed energy storage resources, this paper proposes an ordered scheduling strategy for EVs in campus areas oriented towards renewable energy consumption. Firstly, to address the uncertainty of renewable energy output, this paper uses Conditional Generative Adversarial Network (CGAN) technology to generate a series of typical scenarios. Subsequently, a mathematical model for EV aggregation is established, treating the numerous dispersed EVs within the campus as a collectively controllable resource, laying the foundation for their ordered scheduling. Then, to maximize renewable energy consumption and optimize EV charging scheduling, an improved Particle Swarm Optimization (PSO) algorithm is adopted to solve the problem. Finally, case studies using a real-world testing system demonstrate the feasibility and effectiveness of the proposed method. By introducing a dynamic inertia weight adjustment mechanism and a multi-population cooperative search strategy, the algorithm’s convergence speed and global search capability in solving high-dimensional non-convex optimization problems are significantly improved. Compared with conventional algorithms, the computational efficiency can be increased by up to 54.7%, and economic benefits can be enhanced by 8.6%. Full article

Drones are widely used in urban air pollution monitoring. Although studies have focused on single-drone applications, collaborative applications for air pollution detection are relatively underexplored. This paper presents a 3D cube-based adaptive cooperative search algorithm that allows two drones to collaborate to explore air pollution. The search space is divided into cubic regions, and each drone explores the upper or lower halves of the cubes and collects data from their vertices. The vertex with the highest measurement is selected by comparing the collected data, and an adjacent cube-shaped search area is generated for exploration. The search continues iteratively until any vertex measurement reaches a predefined threshold. An improved algorithm is also proposed to address the divergence and oscillation that occur during the search. In simulations, the proposed method consumed 21 times less CPU time and required 23 times less search distance compared to linear search. Additionally, the cooperative search method using multiple drones was more efficient than single-drone exploration in terms of the same parameters. Specifically, compared to single-drone exploration, the collaborative drone search reduced CPU time by a factor of 2.6 and search distance by approximately a factor of 2. In experiments in real-world scenarios, multiple drones equipped with the proposed algorithm successfully detected cubes containing air pollution above the threshold level. The findings serve as an important reference for research on drone-assisted target exploration, including air pollution detection. Full article

Nigeria is among the countries highly vulnerable to climate change impact. Thus, there has been growing emphasis on the pursuit of decarbonization and net-zero (net-zero transition) strategies. The aim of this work is to review major concepts in research publications associated with climate change mitigation in Nigeria. The literature search was conducted on the Scopus database using relevant keyword operators. Mixed methods were adopted to conduct bibliometric, text mining and content analysis. Bibliometric software (VOSviewer) was used. The research objectives were to identify how net-zero transition research has evolved in Nigeria; their important research themes and trends in Nigeria, and potential directions for future research on achieving them in Nigeria. The results show that the number of publications in the field has been increasing, with 87% of the articles included in the dataset published between 2016 and 2024. Through data clustering, eight clusters of articles were identified, namely (i) the renewable energy, economic growth and emission reduction nexus (ii) energy transition in the Nigerian power system, (iii) policy drivers (socio-technical and economic) for a cleaner energy system, (iv) energy transition governance, (v) hybrid renewable energy systems, (vi) low-carbon transition, (vii) energy efficiency and low-carbon growth and others. By checking through the keywords used by authors, it appears that the most popular keywords are carbon neutrality, hydrogen, biomass, circular economy, and electric vehicles. These keywords further highlight areas of research interests. Some of the potential future directions identified include the need for effective research communication and strong cooperation between academia and relevant CC policy-making bodies to translate scientific research into evidence-based policies and actionable frameworks; tiered subsidies or tax rebates to low-income households to promote CC mitigating technologies and align CC objectives with social equity; and others. Although this work focuses solely on Nigeria, the country shares similar characteristics with many sub-Saharan African countries, and some others in the global South. Accordingly, the findings will be relevant to those areas, with some unique adaptations. Full article

This paper addresses the challenge of conducting cover searches for unmanned surface vessels operating in unknown waters. To tackle this problem, we propose a cover algorithm that combines job partitioning with a joint network protocol. The algorithm starts by dividing the map area based on an exploration-based approach, followed by task area calculation and assignment using the Boustrophedon technique. Subsequently, a distributed joint network protocol is utilized to dynamically allocate search tasks among the members of the USV (unmanned surface vessel) group, maximizing the overall search efficiency. Three basic strategies are designed for collaboration between USVs (namely, obstacle recognition, distributed communication, and regional transfer), facilitating the real-time allocation of water coverage tasks among unmanned vessels until the entire body of water is completely covered. Simulation experiments demonstrate the effectiveness of the proposed algorithm. Compared to several non-cooperative area coverage algorithms, our algorithm reduces calculation task usage time and total travel distance for the cluster. Furthermore, the proposed algorithm performs well in dynamic environments, efficiently handling coverage search tasks. Notably, the B-CNP (Boustrophedon-contract network protocol) algorithm proposed in this paper achieves an approximate 3.22% reduction in path length compared to the BA* (Boustrophedon-A*) algorithm. Full article

The cooperative patrol control of multiple unmanned surface vehicles (Multi-USVs) in dynamic aquatic environments presents significant challenges in global coverage efficiency and system robustness. The study proposes a cooperative patrol control algorithm for multiple unmanned surface vehicles (Multi-USVs) based on a hybrid embedded task state information model and reward reshaping techniques, addressing global coverage challenges in dynamic aquatic environments. By integrating patrol, collaboration, and obstacle information graphs, the algorithm generates kinematically feasible control actions in real time and optimizes the exploration-cooperation trade-off through a dense reward structure. Simulation results demonstrate that the algorithm achieves 99.75% coverage in a 1 km × 1 km task area, reducing completion time by 23% and 74% compared to anti-flocking and partition scanning algorithms, respectively, while maintaining collision rates between agents (CRBAA) and obstacles (CRBAO) below 0.15% and 0.5%. Compared to DDPG, SAC, and PPO frameworks, the proposed training framework (TFMUSV) achieves 28% higher rewards with 40% smaller fluctuations in later training stages. This study provides an efficient and reliable solution for autonomous monitoring and search-rescue missions in complex aquatic environments. Full article

The cooperative control of multiple autonomous surface vehicles (ASVs) is a critical area of research due to its significant applications in maritime operations, such as search and rescue and environmental monitoring. However, challenges such as communication delays and dynamic topologies often hinder stable cooperative control in practical scenarios. This study addresses these challenges by developing a formation control method based on consensus theory, focusing on both formation control and time delay. First, a simplified ASV characteristic model is established, and a basic consensus control algorithm is designed and analyzed for stability, considering different communication topologies. Then, to handle delays, the formation control method is extended, and the stability of the revised algorithm is rigorously proven using the Lyapunov function. Simulation results demonstrate that the proposed control strategy effectively maintains formations, even in the presence of communication delays. In the end, comparative simulations are carried out to demonstrate the effectiveness and robustness of the proposed controller. Simulation results demonstrate that the proposed control strategy effectively maintains formations, even in the presence of communication delays, with a convergence time of approximately 100 s and a formation error stabilizing at around 7 m. This research lays a foundation for more reliable cooperative control systems for ships, with potential applications in a variety of maritime and autonomous systems. 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

Due to the high mobility and low cost, unmanned aerial vehicle (UAV)-mounted edge computing (UMEC) provides an efficient way to provision computing offloading services for Internet of Forestry Things (IoFT) applications in forest areas without sufficient infrastructure. Multiple IoFT applications can be consolidated into fewer UAV-mounted servers to improve the resource utilization and reduce deployment costs with the precondition that all applications’ Quality of Service (QoS) can be met. However, most existing application placement schemes in UMEC did not consider the dynamic nature of the aggregated computing resource demand. In this paper, the resource allocation and application placement problem based on fine-grained cooperative overbooking in UMEC is studied. First, for the two-tenant overbooking case, a Two-tenant Cooperative Resource Overbooking (2CROB) scheme is designed, which allows tenants to share resource demand violations (RDVs) in the cooperative overbooking region. In 2CROB, an aggregated-resource-demand minimization problem is modeled, and a bisection search algorithm is designed to obtain the minimized aggregated resource demand. Second, for the multiple-tenant overbooking case, a Proportional Fairness-based Cooperative Resource Overbooking (PF-MCROB) scheme is designed, and a bisection search algorithm is also designed to obtain the corresponding minimized aggregated resource demand. Then, on the basis of PF-MCROB, a First Fit Decreasing-based Cooperative Application Placement (FFD-CAP) scheme is proposed to accommodate applications in as few servers as possible. Simulation results verify that the proposed cooperative resource overbooking schemes can save more computing resource in cases including more tenants with higher or differentiated resource demand violation ratio (RDVR) thresholds, and the FFD-ACP scheme can reduce about one third of necessarily deployed UAVs compared with traditional overbooking. Thus, applying efficient cooperative overbooking in application placement can considerably reduce deployment and maintenance costs and improve onboard computing resource utilization and operating revenues in UMEC-aided IoFT applications. Full article

Prior research has demonstrated the value of an entrepreneurial mindset in business. The so-called third mission is also becoming an increasingly important aspect of university operations. This involves leveraging knowledge generated at the university level to create close links with society and the economy. The role of intrapreneurs has been examined in the corporate, for-profit sector. However, these agents of change also play a significant role in the advancement of entrepreneurial universities. The present research investigates the role of intrapreneurs in entrepreneurial universities through a bibliographic analysis using RStudio biblioshiny on the Scopus and Web of Science databases. It is evident that the literature on this subject has gained interest in recent years, yet the number of documents remains limited, with a small number of authors publishing them. The development of keywords is also notable, including the emergence of sustainability, which is linked to intrapreneurs and the entrepreneurial universities. Although this study has its limitations, it can show how and where authors should publish, what the basic and the emerging topics are, what the most important keywords are and how these are connected and how countries cooperate in searching for solutions in this globally recognized research area. Full article