Search Results (923)

With the surge of artificial intelligence (AI) systems, autonomous Large Language Model (LLM)-based negotiator agents are being developed to negotiate on behalf of humans, particularly in commercial contexts. In human interactions, marginalized groups, such as racial minorities and women, often face unequal outcomes due to gender and social biases. Since these models are trained on human data, a key question arises: do LLM-based agents reflect existing biases in human interaction in their negotiation strategies? To address this question, we investigated the impact of such biases in one of the most advanced LLMs available, ChatGPT-4 Turbo, by employing a buyer–seller game approach using male and female agents from four racial groups (White, Black, Asian, and Latino). We found that when either the seller or buyer is aware of the gender and race of the other player, they secure more profit compared to when negotiations are gender- and race-blind. Additionally, we examined the influence of conditioning buyer agents to improve their negotiation strategy by prompting them with additional persona. Interestingly, we observed that such conditioning can mitigate LLM-based agents’ biases, suggesting a way to empower underrepresented groups to achieve more equitable outcomes. Based on the findings of this study, while LLM-generated text may not exhibit explicit biases, hidden gender and social biases in the training data can still lead to skewed outcomes for users. Therefore, it is crucial to mitigate these biases and prevent their transfer during dataset curation to ensure fair human–agent interactions and build user trust. Full article

Agentic artificial intelligence (AI) is emerging as a paradigm for next-generation smart grids, enabling autonomous decision-making, adaptive coordination, and resilient control in complex cyber–physical environments. Unlike traditional AI models, which are typically static predictors or offline optimizers, agentic AI systems perceive grid states, reason about goals, plan multi-step actions, and interact with operators in real time. This review presents the latest advances in agentic AI for power systems, including architectures, multi-agent control strategies, reinforcement learning frameworks, digital twin optimization, and physics-based control approaches. The synthesis is based on new literature sources to provide an aggregate of techniques that fill the gap between theoretical development and practical implementation. The main application areas studied were voltage and frequency control, power quality improvement, fault detection and self-healing, coordination of distributed energy resources, electric vehicle aggregation, demand response, and grid restoration. We examine the most effective agentic AI techniques in each domain for achieving operational goals and enhancing system reliability. A systematic evaluation is proposed based on criteria such as stability, safety, interpretability, certification readiness, and interoperability for grid codes, as well as being ready to deploy in the field. This framework is designed to help researchers and practitioners evaluate agentic AI solutions holistically and identify areas in which more research and development are needed. The analysis identifies important opportunities, such as hierarchical architectures of autonomous control, constraint-aware learning paradigms, and explainable supervisory agents, as well as challenges such as developing methodologies for formal verification, the availability of benchmark data, robustness to uncertainty, and building human operator trust. This study aims to provide a common point of reference for scholars and grid operators alike, giving detailed information on design patterns, system architectures, and potential research directions for pursuing the implementation of agentic AI in modern power systems. Full article

Autonomous UAV/UGV swarms increasingly operate in contested environments where purely digital control architectures are vulnerable to cyber compromise, communication denial, and timing faults. This paper presents Guarded Swarms, a hybrid framework that combines digital coordination with hardware-level analog safety enforcement. The architecture builds on Topic-Based Communication Space Petri Nets (TB-CSPN) for structured multi-agent coordination, extending this digital foundation with independent analog guard channels—thrust clamps, attitude limiters, proximity sensors, and emergency stops—that operate in parallel at the actuator interface. Each channel can unilaterally veto unsafe commands within microseconds, independently of software state. The digital–analog interface is formalized via timing contracts that specify sensor-consistency windows and actuation latency bounds. A two-robot case study demonstrates token-based arbitration at the digital level and OR-style inhibition at the analog level. The framework ensures local safety deterministically while maintaining global coordination as a best-effort property. This paper presents an architectural contribution establishing design principles and interface contracts. Empirical validation remains future work. Full article

As autonomous vehicles (AVs) become part of urban environments, pedestrian safety and interactions with these vehicles are critical to creating sustainable, walkable cities. Intuitive pedestrian-vehicle communication is essential not only for reducing crash risk but also for supporting policies that promote active mobility and efficient traffic flow. This study investigates pedestrian crossing behavior in a fully immersive virtual reality environment, building on previous work by the authors conducted in a CAVE-type simulator. Participants crossed between a conventional vehicle and an AV when they perceived it was safe. The analysis examines how external human–machine interfaces (eHMIs) influence crossing decisions, collisions, safety margins, and crossing initiation time (CIT) across different vehicle speeds and traffic gaps. Three hypotheses were tested regarding the effects of eHMIs on CIT, risk-taking behavior, and perceived safety. Results show that eHMIs significantly affect pedestrian decisions: participants delayed crossings when the eHMI indicated non-yielding behavior and initiated crossings earlier when yielding was signaled. Risk-taking behavior increased at higher vehicle speeds and shorter time gaps. Although perceived safety did not increase, behavioral results indicate reliance on visual cues. These findings underscore the importance of standardizing eHMIs to support pedestrian safety and sustainable urban mobility. Full article

This study develops an explainable reinforcement learning (RL) control framework for hybrid ventilation in Mediterranean office buildings to enhance thermal comfort, energy efficiency, and long-term climate resilience. A working environment was created Using EnergyPlus to represent an office test cell equipped with natural ventilation and air conditioning. The RL controller, based on Proximal Policy Optimization (PPO), was trained exclusively on present-day Typical Meteorological Year (TMY) data from Beirut and subsequently evaluated, without retraining, under future 2050 and 2080 climate projections (SSP1-2.6 and SSP5-8.5) generated using the Belcher morphing technique, in order to quantify robustness under projected climate stressors. Results showed that the RL control achieved consistent, though moderate, annual HVAC energy reductions (6–9%), and a reduction in indoor overheating degree (IOD) by about 35.66% compared to rule-based control, while maintaining comfort and increasing natural ventilation hours. The Climate Change Overheating Resistivity (CCOR) improved by 24.32%, demonstrating the controller’s resilience under warming conditions. Explainability was achieved through Kernel SHAP, which revealed physically coherent feature influences consistent with thermal comfort logic. The findings confirmed that physics-informed RL can autonomously learn and sustain effective ventilation control, remaining transparent, reliable, and robust under future climates. This framework establishes a foundation for adaptive and interpretable RL-based hybrid ventilation control, enabling long-lived office buildings in Mediterranean climates to reduce cooling energy demand and mitigate overheating risks under future climate change. Full article

As the population ages, autonomous shuttles (AS) present a potential solution for older adults’ mobility needs. However, acceptance—often assessed through hypothetical scenarios rather than lived experience—remains a significant barrier. This study aimed to explore older adults’ perceptions of AS through pre- and post-exposure, and to examine how these experiences shape their AS acceptance within the Diffusion of Innovations (DOI) framework. Using existing qualitative data from pre- and post-exposure focus groups, with 32 older adults across Florida, we used hybrid thematic analysis, grounded in DOI theory. The results revealed that the technology’s ease of use, as experienced when riding the AS (Trialability), reduced initial concerns related to Complexity. While participants acknowledged the Relative Advantage of AS in enhancing their mobility and safety, their acceptance was conditional upon addressing the AS’s slow speed and abrupt braking. Acceptance was also contingent upon Compatibility with personal lifestyles and the establishment of clear AS Regulations, to build trust. The findings indicate that for older adults, AS acceptance is a dynamic process where direct exposure is essential for overcoming initial concerns. However, widespread adoption will ultimately be influenced by AS performance, seamless integration of AS into their daily lives, and a robust regulatory framework. Full article

Manual window cleaning in high-rise urban buildings is labor-intensive, risky, and resource-inefficient. This study addresses these challenges by investigating a resource-aware mechatronic architecture through the design, development, and experimental validation of a modular Automated Window Cleaning System (AWCS). Unlike conventional open-loop solutions, the AWCS integrates mechanical scrubbing with a closed-loop fluid management system, featuring precise dispensing and vacuum-assisted recovery. The system is governed by a deterministic finite state machine implemented on an ESP32 microcontroller, enabling low-latency IoT connectivity and autonomous operation. Two implementation variants—integrated and retrofit—were validated to ensure structural adaptability. Experimental results across 30 cycles demonstrate a cleaning efficiency of ~2 min/m², a water consumption of <150 mL/m² (representing a >95% reduction compared to manual methods), and an optical cleaning efficacy of 96.9% ± 1.4%. Safety protocols were substantiated through a calculated mechanical safety factor of 6.12 for retrofit applications. This research establishes the AWCS as a sustainable, safe, and scalable solution for autonomous building maintenance, contributing to the advancement of resource-circular domestic robotics and smart home automation. Full article

Promoting the value realization of ecological products is a central issue in practicing the concept that “lucid waters and lush mountains are invaluable assets.” This is particularly urgent for alpine regions, which are vital ecological security barriers but face stringent developmental constraints. This study takes Golog Tibetan Autonomous Prefecture in Qinghai Province as a case study. It establishes a Gross Ecosystem Product (GEP) accounting framework tailored to the characteristics of alpine ecosystems and conducts continuous empirical accounting for the period 2020–2023. The findings reveal that: (i) The total GEP of Golog is immense (reaching 655.586 billion yuan in 2023) but exhibits significant dynamic non-stationarity driven by climatic fluctuations, with a coefficient of variation as high as 11.48%. (ii) The value structure of the GEP is highly unbalanced, with regulatory services contributing over 97.6%. Water conservation and biodiversity protection are the two pillars, highlighting its role as a supplier of public ecological products and the predicament of market failure. (iii) The spatial distribution of GEP is highly heterogeneous. Maduo County, comprising 34% of the prefecture’s land area, contributes 48% of its total GEP, with its value per unit area being 1.68 times that of Gande County, revealing the spatial agglomeration of key ecosystem services. To address the dynamic, structural, and spatial constraints identified by these quantitative features, this paper proposes synergistic realization pathways centered on “monetizing regulatory services,” “precision policy regulation,” and “capacity and institution building”. The aim is to overcome the systemic bottlenecks—“difficulties in measurement, trading, coarse compensation, and weak incentives”—in alpine ecological functional zones. This provides a systematic theoretical and practical solution for fostering a virtuous cycle between ecological conservation and regional sustainable development. Full article

Agricultural robotics and artificial intelligence (AI) are becoming essential to building more sustainable, efficient, and resilient food systems. As climate change, food security pressures, and labour shortages intensify, the integration of intelligent technologies in agriculture has gained strategic importance. This systematic review provides a consolidated assessment of AI and robotics research in agriculture from 2000 to 2025, identifying major trends, methodological trajectories, and underexplored domains. A structured search was conducted in the Scopus database—which was selected for its broad coverage of engineering, computer science, and agricultural technology—and records were screened using predefined inclusion and exclusion criteria across title, abstract, keywords, and eligibility levels. The final dataset was analysed through descriptive statistics and science-mapping techniques (VOSviewer, SciMAT). Out of 4894 retrieved records, 3673 studies met the eligibility criteria and were included. As with all bibliometric reviews, the synthesis reflects the scope of indexed publications and available metadata, and potential selection bias was mitigated through a multi-stage screening workflow. The analysis revealed four dominant research themes: deep-learning-based perception, UAV-enabled remote sensing, data-driven decision systems, and precision agriculture. Several strategically relevant but underdeveloped areas also emerged, including soft manipulation, multimodal sensing, sim-to-real transfer, and adaptive autonomy. Geographical patterns highlight a strong concentration of research in China and India, reflecting agricultural scale and investment dynamics. Overall, the field appears technologically mature in perception and aerial sensing but remains limited in physical interaction, uncertainty-aware control, and long-term autonomous operation. These gaps indicate concrete opportunities for advancing next-generation AI-driven robotic systems in agriculture. Funding sources are reported in the full manuscript. Full article

The increasing proliferation of new energy vehicles and autonomous vehicles has led to the formation of mixed traffic flows characterized by diverse driving behaviors, posing new challenges for intersection signal control. To address this issue, this study proposes a multi-class customer feedback queuing network (MCFFQN) model that incorporates state-dependent road capacity and congestion propagation mechanisms to accurately capture the stochastic and dynamic nature of mixed traffic flows. An evaluation framework for intersection performance is established based on key indicators such as vehicle delay, the energy consumption of new energy vehicles, and the fuel consumption and emissions of conventional vehicles. A recursive solution algorithm is developed and validated through simulations under various traffic demand scenarios. Building on this model, a signal timing optimization model aimed at minimizing total costs—including delay and environmental impacts—is formulated and solved using the Mesh Adaptive Direct Search (MADS) algorithm. A case study demonstrates that the optimized signal timing scheme significantly enhances intersection performance, reducing vehicle delay, energy consumption, fuel consumption, and emissions by over 20%. The proposed methodology provides a theoretical foundation for sustainable traffic management under mixed traffic conditions. Full article

Human–machine shared control has been widely adopted to enhance driving performance and facilitate smooth transitions between manual and fully autonomous driving. However, existing authority allocation strategies often neglect real-time assessment of scenario complexity and driver workload. To address this gap, we leverage non-invasive eye-tracking devices and the 3D virtual driving simulator Car Learning to Act (CARLA) to collect multimodal data—including physiological measures and vehicle dynamics—for the real-time classification of scenario complexity and cognitive workload. Feature importance is quantified using the SHAP (SHapley Additive exPlanations) values derived from Random Forest classifiers, enabling robust feature selection. Building upon a Hidden Markov Model (HMM) for workload inference and a Model Predictive Control (MPC) framework, we propose a novel human–machine shared control architecture with adaptive authority allocation. Human-in-the-loop validation experiments under both high- and low-workload conditions demonstrate that the proposed strategy significantly improves driving safety, stability, and overall performance. Notably, under high-workload scenarios, it achieves substantially greater reductions in Time to Collision (TTC) and Time to Lane Crossing (TLC) compared to low-workload conditions. Moreover, the adaptive approach yields lower controller load than alternative authority allocation methods, thereby minimizing human–machine conflict. Full article

Engineering programmes have been giving more weight to experiential learning, largely because many students still find it difficult to see how classroom theory connects to the work that engineers handle on the ground. With this in mind, a robotics-centred Project-based Learning (PBL) module was introduced to first-year general engineering students as part of the faculty’s engineering spine. The module asks students to design, build, and program small autonomous robots capable of navigating and competing in a set arena. Even a simple task of this kind draws together multiple strands of engineering. Students shift between sketching mechanical layouts, wiring basic circuits, writing code, testing prototypes, and negotiating the usual challenges that arise when several people share responsibility for the same piece of hardware. To explore how students learned through the module, a mixed-methods evaluation was carried out using survey responses alongside reflective pieces written by the students themselves. Certain patterns appeared repeatedly. Many students felt that their technical skills had grown, particularly in breaking down a messy problem into smaller, more workable components. Teamwork also surfaced as a prominent theme. Groups often had to sort out issues such as a robot veering off course due to a misaligned sensor or a block of code producing unpredictable behaviour. These issues were undoubtedly challenging for the students, but they also had a certain pedagogical flavour, with many students describing them as a source of frustration as well as a learning opportunity. Later iterations of the module may benefit from more targeted support at key stages. Despite the many challenges, robotics has been shown to be an attractive way for students to step into engineering practice. The project helped them build technical capability, but it also encouraged habits that matter just as much in real work, such as planning, communicating clearly, and returning to a problem until it behaves as expected. Taken together, the experience offers useful guidance for curriculum designers seeking to create early learning environments that feel authentic and manageable and for motivating students who are just beginning their engineering journey. Full article

The article presents the analytic method for real-time detection of the stationary state of a vehicle based on information retrieved from 6 DoF IMU sensor. Reliable detection of stillness is essential for the application of resetting the inertial sensor’s output bias, called Zero Velocity Update method. It is obvious that the signal from the strapped on inertial sensor differs while the vehicle is stationary or moving. Effort was then made to find a computational method that would automatically discriminate between both states with possibly small impact on the vehicle embedded controller. An algorithmic step-by-step method for building, optimizing, and implementing a diagnostic system that detects the vehicle’s stationary state was developed. The proposed method adopts the “Mahalanobis Distance” quantity widely used in industrial quality assurance systems. The method transforms (fuses) information from multiple diagnostic variables (including linear accelerations and angular velocities) into one scalar variable, expressing the degree of deviation in the robot’s current state from the stationary state. Then, the method was implemented and tested in the dead reckoning navigation system of an autonomous wheeled mobile robot. The method correctly classified nearly 93% of all stationary states of the robot and obtained only less than 0.3% wrong states. Full article

Edge detection is a key building block in real-time image-processing applications such as drone-based infrastructure inspection, autonomous navigation, and remote sensing. However, its computational cost remains a challenge for resource-constrained embedded systems. This work presents a hardware-accelerated edge detection architecture based on a homogeneous 2D-mesh Network-on-Chip (NoC) integrating systolic arrays to efficiently perform the convolution operations required by the Sobel filter. The proposed architecture was first developed and validated as a 3 × 3 mesh prototype on FPGA (Xilinx Zynq-7000, Zynq-7010, XC7Z010-CLG400A, Zybo board, utilizing 26,112 LUTs, 24,851 flip-flops, and 162 DSP blocks), achieving a throughput of 8.8 Gb/s with a power consumption of 0.79 W at 100 MHz. Building upon this validated prototype, a reduced 2 × 2 node cluster with 14-bit word width was subsequently synthesized at the physical level as a proof-of-concept using the OpenLane RTL-to-GDSII open-source flow targeting the SkyWater 130 nm PDK (sky130A). Post-layout analysis confirms the manufacturability of the design, with a total power consumption of 378 mW and compliance with timing constraints, demonstrating the feasibility of mapping the proposed architecture to silicon and its suitability for drone-based infrastructure monitoring applications. Full article

High-altitude long-endurance (HALE) UAVs require navigation payloads that are both fully autonomous and lightweight. This paper presents a full-parameter calibration method for a dual-axis rotational-modulation RINS/CNS integrated system in which the IMU is mounted on a two-axis indexing mechanism and the reconnaissance camera is reused as the star sensor. We establish a unified error propagation model that simultaneously covers IMU device errors (bias, scale, cross-axis/installation), gimbal non-orthogonality and encoder angle errors, and camera exterior/interior parameters (EOPs/IOPs), including Brown–Conrady distortion. Building on this model, we design an error-decoupled calibration path that exploits (i) odd/even symmetry under inner-axis scans, (ii) basis switching via outer-axis waypoints, and (iii) frequency tagging through rate-limited triangular motions. A piecewise-constant system (PWCS)/SVD analysis quantifies segment-wise observability and guides trajectory tuning. Simulation and hardware-in-the-loop results show that all parameter groups converge primarily within the segments that excite them; the final relative errors are typically ≤5% in simulation and 6–$16\%$ with real IMU/gimbal data and catalog-based star pixels. Full article