Search Results (1,729)

This paper introduces a sociotropy-inspired game-theoretic framework for distributed multiple-input multiple-output (MIMO) beamforming systems, where each antenna element is modeled as a strategic agent that adapts its beamforming parameters by balancing individual transmission performance with coordinated interaction among neighboring antennas. The resulting distributed beamforming problem is formulated as an exact potential game, enabling a unified analysis of cooperative antenna behavior under per-antenna power constraints. A complete mathematical formulation is developed, including the derivation of the utility and potential functions, the associated KKT stationarity conditions, and distributed projected gradient dynamics for beamforming adaptation. In addition, a graph-based multi-agent coordination mechanism is introduced to incorporate structured information exchange among antennas through similarity-driven message passing. Numerical simulations compare the proposed sociotropic strategy against both a selfish non-cooperative baseline and a graph-regularized multi-agent learning approach. Results demonstrate that sociotropic coordination improves interference management, convergence stability, and robustness under dynamic channel conditions, while maintaining lower computational complexity than learning-based coordination methods. Finally, the proposed distributed sociotropic beamforming framework is evaluated against classical maximum ratio transmission (MRT), zero-forcing (ZF), and regularised zero-forcing (RZF) beamforming schemes under identical time-varying channel dynamics. Results demonstrate that while conventional baselines exhibit performance saturation under channel fluctuations, the proposed method achieves continuous adaptation and improved sum-rate evolution over time. Full article

With the rapid development of networked cyber-physical systems, the coordinated control of heterogeneous multi-agent systems has attracted increasing attention in applications such as autonomous vehicles, robotic arms, and distributed sensor networks. This paper investigates the leader-following cluster consensus problem for heterogeneous multi-agent systems over weighted cooperative–competitive networks with matched disturbances generated by linear exosystems. Unlike purely cooperative or binary signed networks, the considered network allows interaction weights to take arbitrary positive or negative values, thereby describing both the type and intensity of cooperative or competitive interactions. To handle heterogeneous agent dynamics and matched disturbances, a disturbance-observer-based distributed control protocol is developed for both first-order and second-order followers. Based on path-product-based coordinate transformations and Lyapunov stability analysis, sufficient conditions are derived to guarantee topology-dependent scaled leader-following cluster consensus under interactively balanced and interactively sub-balanced topologies. For interactively unbalanced topologies, a structurally selected pinning control strategy is introduced to compensate for sign conflicts caused by unbalanced directed cycles and ensure global asymptotic convergence. Numerical simulations verify the effectiveness of the proposed protocol under heterogeneous dynamics, weighted cooperative–competitive interactions, and matched disturbances. Full article

Background: Humanitarian logistics decisions extend beyond immediate relief delivery to include preparedness, recovery, service continuity and the development of durable local capabilities. Simulation can support these decisions under uncertainty, yet the evidence remains fragmented across logistics domains, modelling approaches and phases of the relief–development continuum. This review synthesises how simulation has been used in humanitarian logistics and identifies where the evidence is concentrated and where important gaps remain. Methods: A systematic scoping review was conducted in accordance with PRISMA-ScR and PRISMA-S, using multi-disciplinary and specialist database searches supplemented by backward and forward citation searching. Included studies were coded by logistics decision problem, continuum phase, decision level, performance outcome, simulation approach and operational grounding. Results: The literature is concentrated in preparedness and response, particularly around coordination, network design, inventory, allocation, transport and capacity. System dynamics, agent-based modelling and discrete-event simulation are well established, whereas hybrid simulation and digital twin applications remain limited. Early recovery, reconstruction, development-oriented transition and practice-embedded modelling are comparatively underdeveloped. Conclusions: Simulation-supported humanitarian logistics is strongest for structured preparedness and response problems. Future research should connect decisions across phases and strengthen beneficiary-sensitive, operationally grounded modelling of recovery, localisation, service continuity and longer-term logistics capability. Full article

Cube-shaped automated storage and retrieval systems (AS/RS) enhance storage density by organizing inventory in a three-dimensional grid. However, they face two operational bottlenecks: (1) digging—the temporary removal and restacking of upper bins to access a target bin—and (2) inefficient idle staging and return policies in multi-AGV operations. We proposed a demand-based digging and bin-placement strategy and a waiting-point (staging) selection policy that considers AGV positions and remaining task times. These control policies are implemented in both rule-based and multi-agent reinforcement learning (MARL) variants. Their performance is evaluated using a Discrete Event System Specification (DEVS) simulation framework. In a 30 × 30 × 4 grid, three experiments demonstrated that deploying five AGVs achieved the best performance within the tested configuration; the demand-based digging and placement strategy achieved a 6.2% reduction in makespan, and the rule-based and MARL staging policies achieved additional reductions of 2.5% and 1.1%, respectively. These results highlight the benefits of jointly optimizing digging and multi-AGV staging and provide practical guidance for control-policy design in cube-shaped AS/RS. Full article

Mobile edge computing (MEC) supports computation-intensive and latency-sensitive Internet of Things (IoT) applications. However, collaborative task offloading in dynamic heterogeneous environments remains challenging due to coupled physical constraints, shared resource competition, and high-dimensional decision spaces. Existing multi-agent deep reinforcement learning (MADRL) approaches often rely on static penalties or centralized action truncation for constraint handling. These methods may lead to unstable training, conservative strategies, and limited collaboration. To address these limitations, this paper proposes a constraint-aware multi-agent edge collaborative offloading algorithm (CARE-CTDE). The offloading problem is formulated as a constrained Markov decision process and addressed under a centralized training and decentralized execution (CTDE) framework. Dynamic Lagrange multipliers replace fixed penalties to improve training stability and support smoother exploration near constraint boundaries. A multi-threshold-guided Lagrangian constraint regulation mechanism further coordinates heterogeneous constraints, including energy consumption, latency, and server capacity. In addition, a congestion-driven cost allocation method transforms global resource competition into dynamic cost signals, guiding agents toward more coordinated offloading decisions. The simulation results show that CARE-CTDE achieves better scheduling performance, resource utilization, and constraint satisfaction than baseline methods in dynamic heterogeneous MEC scenarios, demonstrating its effectiveness and robustness for constrained edge computing systems. Full article

In this paper, bipartite consensus is studied for multi-agent systems (MAS) with discrete-time dynamics via intermittent dynamic event-triggered control (IDETC). Firstly, by applying the event-triggered control (ETC) mechanism to the intermittent control strategy, an intermittent dynamic event-triggered state feedback bipartite consensus protocol is proposed, and the stability analysis is given by employing the switching system method. Then, through integration of IDETC and observer-based relative output information, an observer-based IDETC bipartite consensus protocol is proposed and the corresponding consensus conditions are obtained. In the final section, two numerical simulations are given for validating the efficiency of the theoretical findings. Full article

Multiple unmanned aerial vehicles (UAVs) performing cooperative missions in complex environments face challenges such as difficult cooperative decision-making, stringent spatiotemporal consistency constraints, and environmental uncertainty. The cooperative mission considered in this paper aims to enable multiple UAVs to simultaneously arrive at multiple constant-velocity moving targets. To address these challenges, this paper proposes a multi-agent guided soft actor–critic (MAGSAC) deep reinforcement learning algorithm. Under the centralized training with decentralized execution (CTDE) framework, a Guider network is introduced to guide the local actor network in learning coordinated strategies, thereby alleviating the non-stationarity of multi-agent decision-making under uncertain environments. An estimated time of arrival (ETA)-based spatiotemporal coordination reward function is designed to promote synchronized arrival. To address sparse rewards, a hindsight experience replay (HER) mechanism based on backward trajectory reconstruction is developed, and a delayed collision-constraint activation mechanism is incorporated to improve convergence while maintaining flight safety. Simulation results show that MAGSAC outperforms existing mainstream algorithms in synchronization success rate, temporal synchronization accuracy, and safety. Full article

The implementation of safe and reliable Autonomous Driving Stacks in complex urban environments remains a formidable engineering challenge. While classical modular pipelines provide necessary component-level interpretability, they are inherently rigid, often struggling to adapt to novel environments and failing to provide robust scene interpretation in highly interactive scenarios. In this paper, we present a modular End-to-End ROS-based autonomous driving architecture that upgrades a classical modular baseline by injecting learning-based models into its individual processing layers, integrating GaussianCaR and CLIP for dense semantic BEV perception, expanding the Hierarchical Petri Net state space for safe multi-agent reasoning, refining the planning layer with continuous curve optimization, and replacing the previous reactive controller with an Adaptive Nonlinear Model Predictive Control strategy for superior trajectory tracking. Validated in the CARLA simulator across challenging traffic scenarios and adverse environmental conditions, the proposed architecture raises the Driving Score from 53.81% to 66.46% over the previous baseline, driven by a substantial increase in the Infraction Penalty from 0.59 to 0.79, reflecting a fundamental shift towards safer and more conservative driving behavior at the cost of a moderate reduction in route completion. Against pure End-to-End approaches, our architecture achieves the highest Driving Score at 73.9% and the strongest Infraction Penalty at 0.913, demonstrating that modular interpretability and competitive End-to-End performance are not mutually exclusive. Code will be made publicly available online. Full article

Comparisons of Bayesian log-odds and Dempster’s combination rule for occupancy grid mapping typically parameterize the two sensor models independently, so that observed performance differences confound the fusion rule with the sensor parameterization. We develop a pignistic-transform-based matching methodology that derives belief function masses producing identical per-observation decision probabilities, isolating the accumulation rule as the sole variable. We show that the confound is large: in multi-robot experiments under two noise conditions, applying the match reversed boundary sharpness from a +6% to +14% advantage for belief functions to a −17% to −22% deficit favoring Bayesian log-odds—a 23 to 36 percentage-point reversal, consistent across both conditions—motivating per-observation matching as the basis for controlled comparison. Under BetP-matched comparison in single-agent simulation (15 independent runs) and on two real indoor lidar datasets (Intel Research Lab, Freiburg Building 079), the two frameworks produce practically equivalent maps on the reported point-probability metrics (cell accuracy, boundary sharpness, Brier score), with a small directional advantage for Bayesian log-odds (absolute differences 0.001–0.022 on [0, 1] scales). Under normalized plausibility ($P_{\mathrm{Pl}}$) matching, the direction reverses for boundary sharpness and Brier score, indicating that the ranking depends on the probability transform used for matching, not solely on the fusion rule. All evaluation is restricted to point-probability metrics on 2D binary grids with Dempster’s and Yager’s rules. The interval-valued representation $\left[\mathrm{Bel}\right(A),\mathrm{Pl}(A\left)\right]$ unique to belief functions is not assessed. The matching methodology is applicable to other Bayesian/belief function comparisons. Full article

Despite the growing adoption of Augmented Reality (AR) in industrial maintenance, no structured methodology exists to systematically identify which operations are best suited for effective AR deployment. This study addresses this gap by proposing a five-step, Multi-Criteria Decision-Making (MCDM)-based selection framework for determining AR-compatible maintenance operations in high-speed railway systems. The framework—applied under the AFNOR FD X 60-000 standard—integrates maintenance-level compatibility analysis, multi-criteria filtering across five dimensions (operational frequency, execution complexity, safety impact, traceability, and scalability), and expert validation involving 100 railway maintenance professionals. Applied to 12 candidate operations at a high-speed railway maintenance facility in Morocco, the framework identified OP10 (insulating oil level verification of the Main Transformer) as the optimal pilot use case, confirming expert consensus (Kruskal–Wallis: H = 18.479, p < 0.001). The selected operation was subsequently integrated into a hybrid AR–Deep Reinforcement Learning architecture employing a Deep Q-Learning (DQL) agent for adaptive decision support, deployed on a Magic Leap 2 head-mounted device via a Unity-based rendering pipeline with hybrid marker-based and markerless computer vision tracking through Vuforia Engine. Experimental validation conducted over three months under simulated and semi-operational conditions yielded a 34–47% reduction in intervention time, a 55–70% decrease in human error rates, and a 28–42% decline in failure-related costs. While results are currently limited to a single-site context, the proposed methodology is directly transferable to any asset-intensive, regulated maintenance environment beyond the railway sector. Full article

Focusing on the issue that false data injection (FDI) attacks on the secondary controllers of DC microgrids can affect the normal and stable operation of microgrids, a distributed resilient controller for DC microgrids is proposed to ensure that the two control objectives, voltage regulation and current sharing, can still be achieved under the action of time-varying bounded FDI attacks. First, the distributed secondary control problem of the microgrid is viewed as a leader–follower multi-agent consensus problem, and the impact of FDI attacks on the microgrid is analyzed. Second, a fully distributed resilient controller based on an adaptive compensation mechanism is designed to compensate for FDI attacks and mitigate their impact on the microgrid. Next, it is proven through Lyapunov stability theory that the microgrid can operate normally and stably under the designed distributed resilient controller, achieving the two control objectives. Finally, simulation analysis is used to verify the effectiveness of the proposed resilient control method. Full article

Large-scale UAV data collection creates a tension among wide-area coverage, operational efficiency, and delivery continuity. Data must be continuously delivered to a base-station coordinator, but real-time replanning becomes increasingly difficult as the number of sensors and UAVs grows. Standard vehicle-routing methods slow down once routes have to be regenerated often, while reinforcement learning struggles with fixed-wing UAVs that cannot hover or turn sharply. We address this with a two-layer framework. In the lower layer, multirotor UAVs visit sensor nodes and buffer the collected payload until it is retrieved by a fixed-wing UAV. Their routes come from clustering the nodes and solving a capacitated vehicle routing problem within each cluster, with the cost biased toward older data and a short cooldown against immediate revisits. In the upper layer, fixed-wing UAVs deliver the buffered payload to the base-station coordinator, guided by a Multi-Agent Proximal Policy Optimization (MAPPO) policy that receives a local buffer-summary map and selected high-priority cells from a compact global summary. A spacing reward encourages separation before agents enter close-proximity states, instead of only penalizing collisions afterward. Component-level experiments show that the lower-layer planner handles up to 600 active routing targets within 1.3 s on average and that the age/cooldown objective improves freshness and revisit behavior. In integrated simulations with 1000 nodes, 32 multirotor UAVs, and 2 fixed-wing UAVs, the learned fixed-wing policy maintains collection performance comparable to a strong exclusive greedy baseline while recording no collision or persistent-proximity termination events over the reported data-generation-rate sweep. These results support the proposed framework as a scalable coordination-layer design for dynamic sensor workloads, where adaptive multirotor routing and motion-constrained fixed-wing retrieval are evaluated together under a shared data-generation workload. Full article

Safe-separation-and-collision-avoidance unmanned aerial vehicle (UAV) swarms are increasingly used for inspection, emergency response, environmental monitoring, and search-and-rescue support in cluttered airspace where communication links may be delayed, degraded, or intermittently unavailable. These applications require heterogeneous vehicles to maintain situational awareness, allocate tasks, and avoid hazards under partial observability and changing team topology. To address these challenges, this paper proposes a Hierarchical Graph-Attention Multi-Agent Reinforcement Learning architecture (HG-MARL) for safe-separation-and-collision-avoidance heterogeneous UAV swarm coordination. The proposed framework decomposes the task into high-level resource allocation and low-level local-control execution, uses graph attention for changing swarm topology, and applies Transformer memory, action masking, potential-field reward shaping, and domain-randomized simulation training. In the multi-scenario simulation summaries, HG-MARL achieves 92.9%, 89.8%, and 82.6% task success in Scenarios A–C, respectively, improving upon MAPPO by 15.1, 21.4, and 20.1 percentage points. Summary-statistic Welch tests show that all six HG-MARL comparisons against MAPPO and QMIX yield $p<0.01$ with large effect sizes. Fair-control, reward-sensitivity, communication-degradation, safety-ablation, training-stability, latency, and transfer-oriented stress tests further support the contributions of the integrated architecture. The validation scope is simulator-based, with platform-level flight/HIL evaluation discussed as future work. These results suggest that HG-MARL is a promising simulation-validated framework for civilian UAV swarm coordination in collision-and-separation-critical and communication-degraded environments. Full article

Typhoon-eye sensing places unmanned aerial vehicle (UAV) swarms in a setting where the wind field that carries the target signal also displaces aircraft, drains energy, weakens links, and increases failure risk. A rule that improves only routing or only motion can therefore move the swarm into another failure mode. This paper proposes a risk-driven cross-layer coordination scheme for such missions. A bounded risk index, computed from isolation, connectivity loss, and wind intensity, acts as a supervisory variable for multi-hop reachability maintenance, isolated-node recovery, and layered altitude adaptation. For evaluation, graph reachability is separated from useful data return through a degraded multi-hop aggregation model that includes distance loss, wind-dependent reliability, rain-induced packet loss, relay forwarding loss, and mothership collection capacity. The simulator combines a bounded Holland-type storm field, stochastic turbulence, nonlinear propulsion energy consumption, and wind-dependent structural failure. Against three literature-inspired baselines, two AI-inspired comparators, and six ablation variants, the method keeps a balanced profile across connectivity, isolation, wind exposure, data collection, and survival. In 30-run steady-state robustness tests under heavy-rain attenuation, the full strategy showed clear gains over routing-only and multi-agent reinforcement learning (MARL)-routing comparators in connectivity and isolation, but did not uniformly dominate topology reconstruction or the multi-agent deep deterministic policy gradient–artificial potential field (MADDPG-APF) recovery comparator. The results indicate that, in storm-dominated swarm sensing, resilience comes mainly from coordinating exposure reduction with topology stabilization, rather than from optimizing a single layer. Full article

Nickel has become a strategic mineral for the energy transition, yet its supply chain is increasingly shaped by a compound risk regime involving resource nationalism, processing concentration, geopolitical compliance rules, carbon-footprint requirements, and commodity-market volatility. This study develops NiChain-LLM-ABM, a large-language-model-empowered agent-based model for simulating nickel supply chain resilience under semantically rich policy shocks. The framework uses a policy semantic parsing module to transform official policy texts into structured shock parameters, a multi-agent strategy generation module to represent adaptive decisions by seven agent classes, a calibrated supply chain network module to simulate material, financial, and information flows, and a four-dimensional resilience assessment module. The model is anchored in observed nickel production, price, trade, and technology data from USGS, IEA, UN Comtrade, LME, and official legal sources, and its scenario outputs are generated through 100 Monte Carlo replications over 2025–2035. Results show that the baseline Comprehensive Resilience Index (CRI) declines from 0.620 in 2025 to 0.547 in 2035. Indonesian policy tightening causes the sharpest near-term deterioration, with CRI falling to 0.445 in 2028 and the simulated supply deficit reaching 24.5 kt Ni equivalent. A geopolitical compliance shock produces the lowest terminal resilience (CRI = 0.472 in 2035). A green-compliance scenario is disruptive in the short run but exceeds the baseline by 2035, while a coordinated policy portfolio raises the terminal CRI to 0.744, a 36.0% improvement over the baseline. Compared with a conventional rule-based ABM, the LLM-ABM reduces extreme-event backcasting error by 57%, improves policy-response fidelity by 53%, and more than doubles agent heterogeneity differentiation. The results support portfolio-based critical-mineral governance combining strategic reserves, overseas equity investment, recycling, technology substitution, and international cooperation. Full article