Search Results (748)

This paper introduces the overall design plan, development timeline, and preliminary progress of the Autonomous Pit Exploration System project. This project aims to develop an advanced multi-robot system for the efficient inspection of nuclear waste-storage tank pits. The project is structured into three phases: Phase 1 involves data collection and interface definition in collaboration with Hanford Site experts and university partners, focusing on tank riser geometry and hardware solutions. Phase 2 includes the selection of sensors and robot components, detailed mechanical design, and prototyping. Phase 3 integrates all components into a cohesive system managed by a master control package which also incorporates digital twin and surrogate models, and culminates in comprehensive testing and validation at a simulated tank pit at the Idaho National Laboratory. Additionally, the system’s communication design ensures coordinated operation through shared data, power, and control signals. For transportation and deployment, an electric vehicle (EV) is chosen to support the system for a full 10 h shift with better regulatory compliance for field deployment. A telescopic arm design is selected for its simple configuration and superior reach capability and controllability. Preliminary testing utilizes an educational robot to demonstrate the feasibility of splitting computational tasks between edge and cloud computers. Successful simultaneous localization and mapping (SLAM) tasks validate our distributed computing approach. More design considerations are also discussed, including radiation hardness assurance, SLAM performance, software transferability, and digital twinning strategies. Full article

An airship is a lighter-than-air vehicle that offers static lift without consuming much power. This property makes it a potential candidate for many commercial applications. The target applications include rescue operations, surveillance, communication, a data collection platform for research activities and payload delivery that requires hovering capabilities, etc. To successfully apply airships in these applications and many others, airship autonomous control development is of paramount importance. To accomplish this goal, the initial step is to model airship dynamics that cover the complete flight envelope accurately. The goal is to develop a flight simulator that can test the advanced autonomous control algorithms. In the proposed work, first, the nonlinear six-degree-of-freedom equations of motion are developed using Newtonian mechanics. These equations are used to develop a flight simulator for the University of Engineering and Technology Taxila (UETT) airship. Airship responses to different control inputs are investigated, and the results are validated with the available data in the literature for other airship projects. Also, the obtained longitudinal and lateral eigenmodes show good agreement with the experimental flight data of the UETT airship. The extensive simulation results favour the dynamic analysis of the airship. Full article

This project presents the development of an open-source educational platform based on an automotive Electrically Excited Synchronous Machine (EESM) repurposed from a KIA Sportage mild-hybrid vehicle. The introduction provides an overview of hybrid drive systems and the primary configurations employed in automotive applications, including classifications based on power flow and the placement of electric motors. The focus is placed on the parallel hybrid configuration, where a belt-driven starter-generator assists the internal combustion engine (ICE). Due to the proprietary nature of the original control system, the unit was disassembled, and a custom control board was designed using a Texas Instruments C2000 Digital Signal Processor (DSP). The motor features a six-phase dual three-phase stator, offering improved torque smoothness, fault tolerance, and reduced current per phase. A compact Anisotropic Magneto Resistive (AMR) position sensor was implemented for position and speed measurements. Current sensing was achieved using both direct and magnetic field-based methods. The control algorithm was verified on a modified six-phase inverter under simulated vehicle conditions utilizing a dynamometer. Results confirmed reliable operation and validated the control approach. Future work will involve complete hardware testing with the new control board to finalize the platform as a flexible, open-source tool for research and education in hybrid drive technologies. Full article

This study presents the development of an open-source Driver-in-the-Loop simulation platform, specifically designed to test and analyze advanced automated driving functions. We emphasize the creation of a versatile system architecture that ensures seamless integration and interchangeability of components, supporting diverse research needs. Central to the simulator’s configuration is a hexapod motion platform with six degrees of freedom, chosen through a detailed benchmarking process to ensure dynamic accuracy and fidelity. The simulator employs a half-vehicle cabin, providing an immersive environment where drivers can interact with authentic human–machine interfaces such as pedals, steering, and gear shifters. By projecting complex driving scenarios onto a curved screen, drivers engage with critical maneuvers in a controlled virtual environment. Key innovations include the integration of a motion cueing algorithm and an adaptable, cost-effective open-source framework, facilitating collaboration among researchers and industry experts. The platform enables standardized testing and offers a robust solution for the iterative development and validation of automated driving technologies. Functionality and effectiveness were validated through testing with the ISO lane change maneuver, affirming the simulator’s capabilities. Full article

While technologies like renewable energy and low-carbon transportation are known to mitigate carbon emissions from urban expansion, achieving carbon neutrality during this process remains a critical unresolved challenge. This issue is particularly pressing for developing countries striving to balance urbanization with carbon reduction. Taking Qionglai City as a case study, this study simulated the territorial spatial functional patterns (TSFPs) and carbon emission distribution for 2025 and 2030. Based on the key drivers of carbon emissions from urban expansion identified through the Geographical and Temporal Weighted Regression (GTWR) model, carbon-neutral pathways were designed for two scenarios: urban expansion scenarios under historical evolution patterns (Scenario I) and urban expansion scenarios optimized under carbon neutrality targets (Scenario II). The results indicate that (1) urban space is projected to expand from 6094.73 hm² in 2020 to 6249.77 hm² in 2025 and 6385.75 hm² in 2030; (2) total carbon emissions are forecasted to reach 1.25 × 10⁶ t (metric tons) and 1.40 × 10⁶ t in 2025 and 2030, respectively, exhibiting a spatial pattern of “high in the central-eastern regions, low in the west”; (3) GDP, Net Primary Productivity (NPP), and the number of fuel vehicles are the dominant drivers of carbon emissions from urban expansion; and (4) a four-pronged strategy, optimizing urban green space vegetation types, replacing fuel vehicles with new energy vehicles, controlling carbon emissions per GDP, and purchasing carbon credits, proves effective. Scenario II presents the optimal pathway: carbon neutrality in the expansion zone can be achieved by 2025 using the first three measures (e.g., optimizing 66.73 hm² of green space, replacing 800 fuel vehicles, and maintaining emissions at 0.21 t/10⁴ CNY per GDP). By 2030, carbon neutrality can be achieved by implementing all four measures (e.g., optimizing 67.57 hm² of green space, replacing 1470 fuel vehicles, and achieving 0.15 t/10⁴ CNY per GDP). This study provides a methodological basis for local governments to promote low-carbon urban development and offers practical insights for developing nations to reconcile urban expansion with carbon neutrality goals. Full article

The field of electric vehicles and electric mobility, like other modern engineering practice, not only requires deep analytical skills but increasingly demands the ability to synthesise and integrate knowledge across multiple disciplines (e.g., electrical engineering, mechanical engineering, sustainability engineering, design engineering) to create innovative systems. Education today, however, still has a strong analysis focus: learning, exploring, and understanding theories and concepts is the main drive. Design and synthesis build on those and aim at bringing together theories and concepts into creative and innovative systems. Teaching design and synthesis is notoriously hard. The design of electric vehicles exemplifies the complexity of contemporary engineering problems, requiring the integration of multiple domains to experience the challenges connected to design and synthesis. This paper presents the need for, rationale behind, setup of, and experiences with a 5 European Credit (140 h) Master’s-level (postgraduate) course named “Electric Vehicle System Design” that we developed as a joint effort for the University of Twente and the University of South-Eastern Norway. The course is specifically designed to immerse students in the multidisciplinary design and synthesis processes central to electric mobility. In the paper, the course framework, project-based approach, and lessons learned are discussed. This highlights how engineering students can be equipped for the challenges inherent to designing electric vehicles. Full article

In this work, a fault detection method and a distributed consensus fault-tolerant control (FTC) scheme are proposed for multiple quadrotor unmanned aerial vehicles (multi-QUAVs) with actuator faults. In order to identify the actuator faults in time, an auxiliary state observer is constructed first. Subsequently, a fault detection scheme based on the observer error is presented, which can improve the early warning ability of the multi-QUAVs. Meanwhile, to handle unknown sudden faults, the Nussbaum function approach is combined with the consensus theory to design a distributed consensus FTC strategy for multi-QUAVs. Compared with the traditional direct fault estimation method using the projection function technique, the proposed Nussbaum-based FTC method can avoid the singularity problem of the controller in a simple way. Moreover, all error signals of the closed-loop system are proved to be uniformly ultimately bounded via Lyapunov stability theory and the consensus control algorithm. Finally, simulation comparison results indicate the early warning capability of the fault detection method and the formation maintenance performance of the developed fault-tolerant controller. Full article

Focusing on the Lingshan section of Guangxi’s Pinglu Canal, this study addresses frequent waterlogging during construction under subtropical monsoon rainfall. Human disturbances alter hydrological processes, causing project delays and economic losses. We developed a coupled Storm Water Management Model (SWMM 1D hydrological) and Hydrologic Engineering Center—River Analysis System 2D (HEC-RAS 2D hydrodynamic) model. High-resolution Unmanned Aerial Vehicle—Light Detection and Ranging (UAV-LiDAR) Digital Elevation Model (DEM) delineated sub-catchments, while the Green-Ampt model quantified soil conductivity decay. Synchronized runoff data drove high-resolution HEC-RAS 2D simulations of waterlogging evolution under design storms (1–100-year return periods) and a real event (10 May 2025). Key results: Water depth exhibits nonlinear growth with return period—slow at low intensities but accelerating beyond 50-year events, particularly at temporary road junctions where embankments impede flow. Additionally, intensive intermittent rainfall causes significant ponding at excavation pit-road intersections, and optimized drainage drastically shortens recession time. The study reveals a “rapid runoff generation–restricted convergence–prolonged ponding” mechanism under construction disturbance, validates the model’s capability for complex scenarios, and provides critical data for real-time waterlogging risk prediction and drainage optimization during the canal’s construction. Full article

Geospatial sensing and data-driven technologies (GSDDTs) are playing an increasingly important role in transforming (agro)forestry practices across the Western Balkans 6 region (WB6). This review critically examines the current state of GSDDT application in six WB countries (also known as the WB6 group)—Albania, Bosnia and Herzegovina, Kosovo*, Montenegro, North Macedonia, and Serbia—with a focus on their contributions to sustainable (agro)forest management. The analysis explores the use of unmanned aerial vehicles (UAVs), light detection and ranging (LiDAR), geographic information systems (GIS), and satellite imagery in (agro)forest monitoring, biodiversity assessment, landscape restoration, and the promotion of circular economy models. Drawing on 25 identified case studies across WB6—for example, ALFIS, Forest Beyond Borders, ForestConnect, Kuklica Geosite Survey, CREDIT Vibes, and Project O₂ (including drone-assisted reforestation in Kosovo*)—this review highlights both technological advancements and systemic limitations. Key barriers to effective GSDDT deployment across WB6 in the (agro)forestry sector and its cross-border cooperation initiatives include fragmented legal frameworks, limited technical expertise, weak institutional coordination, and reliance on short-term donor funding. In addition to mapping current practices, this paper offers a comparative overview of UAV regulations across the WB6 region and identifies six major challenges influencing the adoption and scaling of GSDDTs. To address these, it proposes targeted policy interventions, such as establishing national LiDAR inventories, harmonizing UAV legislation, developing national GSDDT strategies, and creating dedicated GSDDT units within forestry agencies. This review also underscores how GSDDTs contribute to compliance with seven European Union (EU) acquis chapters, how they support eight Sustainable Development Goals (SDGs) and their sixteen targets, and how they advance several EU Green Agenda objectives. Strengthening institutional capacities, promoting legal alignment, and enabling cross-border data interoperability are essential for integrating GSDDTs into national (agro)forest policies and research agendas. This review underscores GSDDTs’ untapped potential in forest genetic monitoring and landscape restoration, advocating for their institutional integration as catalysts for evidence-based policy and ecological resilience in WB6 (agro)forestry systems. Full article

Bacterial leaf streak (BLS), caused by Xanthomonas translucens pv. undulosa, has become an intermittent yet economically significant disease of wheat in the Upper Midwest during the last decade. Because chemical and cultural controls remain ineffective, breeders rely on developing resistant varieties, yet visual ratings in inoculated nurseries are labor-intensive, subjective, and time-consuming. To accelerate this process, we combined unmanned-aerial-vehicle hyperspectral imaging (UAV-HSI) with a carefully tuned chemometric workflow that delivers rapid, objective estimates of disease severity. Principal component analysis cleanly separated BLS, leaf rust, and Fusarium head blight, with the first component explaining 97.76% of the spectral variance, demonstrating in-field pathogen discrimination. Pre-processing of the hyperspectral cubes, followed by robust Partial Least Squares (RPLS) regression, improved model reliability by managing outliers and heteroscedastic noise. Four variable-selection strategies—Variable Importance in Projection (VIP), Interval PLS (iPLS), Recursive Weighted PLS (rPLS), and Genetic Algorithm (GA)—were evaluated; rPLS provided the best balance between parsimony and accuracy, trimming the predictor set from 244 to 29 bands. Informative wavelengths clustered in the near-infrared and red-edge regions, which are linked to chlorophyll loss and canopy water stress. The best model, RPLS with optimal preprocessing and variable selection based on the rPLS method, showed high predictive accuracy, achieving a cross-validated R² of 0.823 and cross-validated RMSE of 7.452, demonstrating its effectiveness for detecting and quantifying BLS. We also explored the spectral overlap with Sentinel-2 bands, showing how UAV-derived maps can nest within satellite mosaics to link plot-level scouting to landscape-scale surveillance. Together, these results lay a practical foundation for breeders to speed the selection of resistant lines and for agronomists to monitor BLS dynamics across multiple spatial scales. Full article

With the accelerated commercialization of autonomous vehicles, new accident types and complex risk factors have emerged beyond the scope of existing traffic safety management systems. This study aims to contribute to sustainable safety by establishing a quantitative basis for early recognition and response to high-risk situations in urban traffic environments where autonomous and conventional vehicles coexist. To this end, high-risk factors were identified through a combination of literature meta-analysis, accident history and image analysis, autonomous driving video review, and expert seminars. For analytical structuring, the six-layer scenario framework from the PEGASUS project was redefined. Using the analytic hierarchy process (AHP), 28 high-risk factors were identified. A risk prediction model framework was then developed, incorporating observational indicators derived from expert rankings. These indicators were structured as input variables for both road segments and autonomous vehicles, enabling spatial risk assessment through agent-based strategies. This space–object integration-based prediction model supports the early detection of high-risk situations, the designation of high-enforcement zones, and the development of preventive safety systems, infrastructure improvements, and policy measures. Ultimately, the findings offer a pathway toward achieving sustainable safety in mixed traffic environments during the initial deployment phase of autonomous vehicles. Full article

The energy transformation of transport infrastructure represents a significant challenge, being implemented along the TEN-T network under the introduced AFIR regulation (Regulation for the Deployment of Alternative Fuels Infrastructure). The goal of this transformation is the development of alternative fuels infrastructure deployed along the Trans-European Transport Network (TEN-T), dedicated to light-duty electric vehicles (eLDVs) and heavy-duty electric vehicles (eHDVs). The measures undertaken must be preceded by an analytical process assessing the assumptions outlined in the AFIR regulation, defining targeted actions for achieving the regulation’s objectives, and evaluating the baseline status as well as projected conditions for the years 2025, 2027, 2030, and 2035. This assessment is essential during the planning and management stages of the energy transformation process of transport infrastructure being undertaken by individual EU Member States. Meeting the targets set by AFIR for transport infrastructure necessitates the development of appropriate research tools. The approach proposed in this article offers an innovative framework for addressing the challenges of energy transformation. The initial step involves designing a model for the energy transformation of transport infrastructure, followed by the definition of indicators to assess the implementation of AFIR objectives. This paper presents a model for the energy transformation of road transport infrastructure, defines the individual elements of the model, specifies indicators for evaluating the transformation process, and conducts a research study incorporating these components. This article aims to elucidate the core aspects of the energy transformation of transport infrastructure, identify actions aligned with achieving the objectives of the AFIR regulation, and perform an evaluation of its implementation. Additionally, the research addresses the question of how the energy transformation of road transport infrastructure is unfolding in Poland. The study is based on the structure of electric vehicles (EVs) and transport infrastructure along the TEN-T network in the territory of Poland. The current level of AFIR compliance for eLDVs for the years 2025, 2027, 2030, and 2035 is approximately 175%, 96%, 37%, and 13%, respectively. In contrast, for eHDVs, the compliance level is around 20%, 0%, and 0% for the TEN-T core network, and approximately 10%, 3%, and 0% for the TEN-T comprehensive network. Full article

With the advancement of autonomous and electric vehicles, an increasing demand has been observed for the automatic robot-controlled charging of electric vehicles. The idea of developing such charging stations was raised at several research institutions and universities as early as the 2010s, however the appearance of automatic charging stations with higher Technology Readiness Levels (TRL) can only be dated from 2019 onwards. In most of the developed concepts and solutions, a dedicated parking system is required by vehicle drivers, since the operating range of the robots used for charging is limited. In most cases, solutions do not incorporate robots with unique geometries; instead, proven industrial solutions are applied. The robots in these prototypes are typically installed in a fixed position, similar to industrial applications, and are not mobile. The charging of one vehicle is usually performed by one robot. A high-level summary of the developed mechanical system is presented in this project overview. In this research, an automated, robot-controlled electric vehicle charging system was designed, in which vehicles are parked perpendicularly adjacent to each other, and multiple vehicles are charged using a single collaborative robot. The mechanical system was implemented with a robot mounted on an extendable arm attached to a carriage, which is guided in two directions along rails. In this manner, the automatic charging system is positioned precisely at the parking location of the vehicle to be charged. Full article

Personal Rapid Transit (PRT) and Group Rapid Transit (GRT) are classes of fully automated public transport systems, where passengers can travel in small vehicles on an interconnected, grade-separated network of guideways, non-stop, from origin to destination. PRT and GRT are considered sustainable as they are low-emission and able to attract car drivers. The parameterized cost modeling framework developed in this paper has the advantage that profitability of different PRT/GRT systems can be rapidly verified in a transparent way and in function of a variety of relevant system parameters. This framework may contribute to a more transparent, rapid, and low-cost evaluation of PRT/GRT schemes for planning and decision-making purposes. The main innovation is the introduction of the “peak hour network saturation” S: the number of vehicles in circulation during peak hour divided by the maximum number of vehicles running at line speed with minimum time headways. It is an index that aggregates the main uncertainties in the planning process, namely the demand level relative to the supply level. Furthermore, a maximum S can be estimated for a PRT/GRT project, even without a detailed demand estimation. The profit per trip is analytically derived based on S and a series of more certain parameters, such as fares, capital and maintenance costs, daily demand curve, empty vehicle share, and physical properties of the system. To demonstrate the ability of the framework to analyze profitability in function of various parameters, we apply the methods to a single vehicle PRT, a platooned PRT, and a mixed PRT/GRT. The results show that PRT services with trip length proportional fares could be profitable already for $S>0.25$. The PRT capacity, profitability, and robustness to tripled infrastructure costs can be increased by vehicle platooning or GRT service during peak hours. Full article

The growing popularity of Personal Light Electric Vehicles (PLEVs), such as e-scooters, has revolutionized urban mobility by offering compact, cost-effective, and environmentally friendly transportation solutions. However, safety concerns, including inadequate infrastructure, poor protective measures, and high accident rates, remain critical challenges. This paper presents the design and development of an innovative self-balancing microvehicle under the H2020 LEONARDO project, which aims to address these challenges through advanced engineering and user-centric design. The vehicle combines features of monowheels and e-scooters, integrating cutting-edge technologies to enhance safety, stability, and usability. The design adheres to European regulations, including Germany’s eKFV standards, and incorporates user preferences identified through representative online surveys of 1500 PLEV users. These preferences include improved handling on uneven surfaces, enhanced signaling capabilities, and reduced instability during maneuvers. The prototype features a lightweight composite structure reinforced with carbon fibers, a high-torque motorized front wheel, and multiple speed modes tailored to different conditions, such as travel in pedestrian areas, use by novice riders, and advanced users. Braking tests demonstrate deceleration values of up to 3.5 m/s², comparable to PLEV market standards and exceeding regulatory minimums, while smooth acceleration ramps ensure rider stability and safety. Additional features, such as identification plates and weight-dependent motor control, enhance compliance with local traffic rules and prevent misuse. The vehicle’s design also addresses common safety concerns, such as curb navigation and signaling, by incorporating large-diameter wheels, increased ground clearance, and electrically operated direction indicators. Future upgrades include the addition of a second rear wheel for enhanced stability, skateboard-like rear axle modifications for improved maneuverability, and hybrid supercapacitors to minimize fire risks and extend battery life. With its focus on safety, regulatory compliance, and rider-friendly innovations, this microvehicle represents a significant advancement in promoting safe and sustainable urban mobility. Full article