Engineering

Abstract: Efficient facility-scale tomato ripeness monitoring remains difficult in greenhouses where uneven terrain limits conventional wheeled and rail-guided platforms and planar cameras provide restricted coverage. This study developed a wheel-legged quadruped monitoring system integrating LiDAR, a depth camera, and a panoramic camera. An adaptive gait-switching strategy supported navigation across heterogeneous terrain. Panoramic images were projected into six perspective views, and the left and right views were processed using a YOLOv8-based ripeness recognition model. Time-synchronized detections and robot poses were fused to map ripeness observations into three-dimensional greenhouse coordinates. Five field experiments in a commercial tomato facility demonstrated autonomous row traversal, inter-row transition, and avoidance of pedestrians, obstacles, and cultivation boundaries. The recognition pipeline continuously identified multiple ripeness stages under variable illumination, foliage occlusion, and robot motion, while the spatial fusion procedure produced a facility-scale three-dimensional ripeness distribution. The integration of terrain-adaptive quadruped mobility, panoramic perception, and spatial mapping provides a practical framework for continuous ripeness monitoring and can support targeted harvesting, yield forecasting, and crop management.

Abstract: We propose a unified physics informed neural modelling framework for the simultaneous emulation of hydraulic states and water quality in water distribution systems (WDS). Building on recent advances in physics informed graph neural networks for hydraulics and spatio temporal PINN-GNN hybrids for chlorine transport, our approach (PINNWDSFQ) couples a physics aware Graph Neural Network (GNN) emulator of hydraulic variables (flows, heads, pressures) with a spatio temporal graph PINN for reactive advective transport. The hydraulic GNN employs message passing based on conservation laws to recover nodal/edge flow fields with a small number of layers and low computational cost, matching high fidelity simulator outputs while enabling fast inference across network sizes. The quality module discretizes pipes with virtual nodes and imposes PDE constraints (advection-reaction) using physics informed loss terms in a GNN encoder-processor-decoder, thereby preserving network topology and chemical reaction kinetics. Both modules are jointly trained in a multitask loss that includes supervised data terms (sparse sensor readings or EPANET surrogate labels) as well as physics constraints and consistency coupling (mass/flux conservation at nodes and flow-transport coupling). We show that the hybrid model obtains emulator level accuracy for hydraulics and state-of-the-art water quality RMSE/MAE (order 1e-3-1e-2 mg/L), scales to large networks with thousands of virtual nodes and runs orders of magnitude faster than traditional simulators on multiple benchmark networks. We discuss training strategies, handling transient/pump driven dynamics, sensor placement implications for calibration, and uncertainty quantification for operational use.

Abstract: This research focuses on the development of a robotic cell test bench for manufacturing variable-geometry tubular parts with minimal human intervention. The system integrates dual arm manipulation with cutting, welding, and coating stations, and was validated in collaboration with an industrial partner with strict requirements on weld strength, surface quality, and air leak rate. Stable and repeatable operation of the robotic cell was achieved through the characterization and optimization of key welding process parameters, particularly the robot end-effector travel speed (mm/s) and laser power (W). In addition, preliminary sealing evaluations demonstrated that the welded components could withstand internal pressures. These results highlight the influence of mechanical components, geometries, and material behavior, as well as the need for advanced digital tools. A preliminary automated path generation methodology is introduced to support adaptable robot trajectories. This research aims to advance composite manufacturing using industrial robotics and dedicated tooling for complex processes, while inspiring engineers and researchers to further automate repetitive and labour-intensive tasks that remain challenging to robotize.

Abstract: The paper presents the design, development, and simulation of an intelligent control system for the pitch-decoupled vertical takeoff and landing (VTOL) unmanned aerial vehicle (UAV). In contrast to conventional tilt-rotor VTOL UAVs, the mechanical structure of the pitch-decoupled VTOL UAV allows passive transition from vertical to horizontal flight modes and vice versa without the use of additional servo actuators. A detailed kinematic scheme and the dynamic equations of motion of the pitch-decoupled VTOL UAV equipped with a two-degree-of-freedom robotic arm are developed using the Denavit-Hartenberg parameters and the Euler-Lagrange equations. The aerodynamic stability of the UAV is investigated, and the aerodynamic coefficients used in the complete dynamics model are derived. On this basis, a multivariable control system is developed. To address the well-known transition-phase issues of VTOL UAVs, a neural controller is designed using the reinforcement learning actor-critic method. It is shown that the proposed neural controller provides a smoother and more accurate transition phase and subsequent horizontal flight of the pitch-decoupled VTOL UAV compared with conventional and cascaded PID controllers.

Abstract: Resilience optimization in adversarial Multi-Agent Mission Systems (MAMSs) requires more than resilience measurement: it requires a decision mechanism whose objective is explicitly aligned with the resilience metric itself. This paper proposes a net-effectiveness-based evaluation–optimization framework for bilateral temporal adversarial operational-loop networks (BTAR), turning resilience evaluation into a reusable world model and reward source for decision-making. We model bilateral confrontation as a heterogeneous temporal network and quantify resilience through the integral of net effectiveness, which captures temporal and adversarial dynamics beyond static topological indicators. We then formulate resilience enhancement as a scale-invariant, cost-aware Markov decision process and solve it with a hierarchically decoupled reinforcement learning architecture: an upper-level PPO policy learns when and for which chains to trigger selective replanning, while a lower-level greedy routine selects concrete replacement nodes. In this way, the optimization objective remains formally aligned with the evaluation metric. Experiments on a reproducible multi-scale, multi-style scenario pool show that the learned policy improves Rtotal from 0.3673 to 1.1696, reaching 96.8% of an idealized oracle benchmark (1.2082), while slightly outperforming the always-replan rule (1.1482) with a lower trigger rate. Under nonzero replanning cost, retraining yields a cost-aware policy whose trigger rate drops to 21.2% and whose cost-adjusted resilience at the target cost setting increases by +0.15, surpassing the greedy oracle benchmark in the high-cost regime. These results show that coupling net-effectiveness evaluation with hierarchical RL yields a deployable, scale-invariant, and cost-aware resilience optimizer.

Abstract: In gravimetric liquid-flow calibration systems, the diverter valve defines the effective start and end of mass collection. For this reason, its temporal repeatability affects the mass associated with the transition and can become a relevant contribution to the measurement uncertainty, especially in low-flow modules. Although the state of the art has addressed this problem through geometric improvements, time or mass correction models, CFD analysis, and higher-precision actuators, these approaches are usually treated in isolation, without systematically linking the variable hydrodynamic load, switching repeatability, and the affected-mass contribution within a single optimization framework. This work presents the genetic-algorithm-based tuning of a PID controller for the servo-electric actuation of a diverter valve in a 200 kg gravimetric module, operating in the interval from 2 L/min to 40 L/min. The main contribution consists of optimizing the PID gains as a function of the flow rate and the hydrodynamic load, with the purpose of reducing the temporal dispersion of the mechanism and its relative contribution to the gravimetric calculation. At 40 L/min, the original pneumatic system presented a switching-time standard deviation of 0.6149 s, while with the servo-electric system and the optimized PID this value decreased to 0.0832 s, equivalent to a reduction of 86.5%. Likewise, the relative contribution of the diverter decreased from 5.10% to 0.688%. The results show that the evolutionary optimization of the PID allows improving the repeatability of the diverter and reducing the variability of the affected mass, although it does not represent by itself the complete uncertainty budget of the gravimetric module.

Abstract: Energy-autonomous Internet of Things (IoT) nodes are becoming important components of distributed wireless networks used in building automation, industrial monitoring and wider smart systems. Their operation is constrained not only by the amount of harvested and stored energy, but also by sensing activity, communication cost, computational workload and required service quality. This review analyzes these dependencies from a cross-layer perspective linking energy harvesting and power management, field-level IoT nodes, wireless communication technologies, and edge–fog–cloud computing. The main original contribution is a decision-oriented framework for energy-autonomous distributed intelligence, supporting adaptive placement of sensing, processing, inference and coordination functions across field, edge, fog and cloud resources. The analysis shows that energy autonomy cannot be achieved by optimizing individual nodes only. Wireless connectivity, network topology and communication overhead directly affect the feasibility of higher-level processing, while edge and fog resources can reduce field-node load and improve local service continuity. The proposed framework therefore combines energy feasibility, communication conditions, service requirements and coordination scope. The resulting guidelines are particularly relevant to building automation and smart IoT systems, supporting interoperable, adaptive and energy-efficient distributed wireless architectures.

Abstract: This study presents a synergetic control approach for coordinating the motion of a UAV swarm based on self-organization principles and energy-potential interactions. The proposed method effectively addresses the known limitations of traditional potential field techniques. A procedure for synthesizing synergetic control of swarm motion is developed using a virtual force field with symmetric attraction and repulsion functions, enabling natural collision avoidance and stable formation maintenance. Damping terms are incorporated into the equations of motion to eliminate self-oscillations and ensure asymptotic stability. A synergetic controller based on virtual-pendulum meters is introduced to prevent stagnation. The curse of dimensionality is mitigated by a specialized mathematical framework, as confirmed by simulations. The swarm-formation time increases sublinearly with the number of UAVs, highlighting the scalability of the approach. A modeling study of swarm self-organization reveals three characteristic phases: chaotic motions, transitional reconfiguration, and ordered swarm behavior. The formation process corresponds to a gradual minimization of inter-UAV interaction energy, while stable collective motion is achieved by maintaining a constant polarization of vehicle heading angles. Simulations also identify parameter ranges that provide optimal synergetic properties during swarm formation and coordinated movement. The findings are further validated through real-world experimental tests of swarm control.

Abstract: Europe’s ageing nuclear fleet creates a growing need for repeated radiological characterisation of contaminated, GPS-denied facilities where human access must be kept to a minimum. However, most deployed robotic systems remain limited to a single platform with a narrow task range. Methods: Building on two earlier conference papers by the authors, this article presents the design, safety engineering and initial field evaluation of the EURATOM project XS-ABILITY, tracing how previous European projects shaped its architecture. The system combines legged, wheeled, rail-based and caged aerial robots equipped with gamma/neutron and beta/gamma instruments. The platforms are coordinated through a ROS 2 architecture supporting distributed SLAM, energy-aware task allocation, risk-aware navigation and a radiological digital twin. A safety and conformity framework consolidates machinery, radiation protection, aviation, cybersecurity and AI regulations, with constraints enforced by a supervisory controller. Results: a campaign at the BR1 and BR3 facilities of SCK CEN, Belgium, in April 2026 demonstrated sensor–robot interoperability, synchronised radiological and odometry acquisition, and GPS-denied navigation under actual radiological conditions. Fleet-level coordination and metrological accuracy were not evaluated. Conclusions: Demonstrated capabilities place the system at TRL 4–5. The forthcoming Ignalina campaign is the next validation step, while supervisory human–robot interaction remains a priority for development.

Abstract: The recent surge in the number of energy plants reliant on fossil fuels such as oil, coal, and natural gas has escalated the challenge of achieving a 100% reduction in carbon emissions by 2050. However, in a commendable joint effort with the Australian States, the Australian Government is unwavering in its commitment to making low-emission energy systems more affordable. This collaborative initiative, backed by substantial funding and policies, instills confidence and incentivizes technology makers and businesses to adopt innovative, solution-driven practices. It underscores the crucial role of diverse stakeholders in this transformative process, painting a promising picture of the future of clean energy in Australia. This vision implies spatiotemporal divisions across the renewable energy chain and interoperability, from electricity supply to electricity demand and vice versa. This study makes inductive inferences by combining an initiative logic of the renewable energy scenario thinking model targeting the Capacity Factor of the renewable energy framework. The foresight of merging diverse renewable energy models contributes to involving diverse stakeholders from now on to define an appropriate architecture of future uncertain capacity, promising a future with reduced carbon emissions and a healthier planet.

Abstract: Hail and sudden heavy rainfall regularly destroy vegetable and grain crops in the state of Puebla, Mexico, with the state’s rural-development authority reporting thousands of damaged hectares within a single growing cycle. This paper presents SPAI-EH, a low-cost Internet of Things system that detects intense rainfall and hail in real time, automatically closes a protective gate over the affected plot, and routes the captured water toward irrigation reuse. The pilot implementation uses an ESP32 microcontroller simulated in Wokwi, combining a DHT22 temperature and humidity sensor with an analog potentiometer that emulates a rain and hail intensity sensor, a servo-actuated gate, a warning LED, and a liquid-crystal display. Readings are classified locally through threshold rules and, when connectivity is available, through a call to a cloud generative-AI model that returns a hazard classification and a gate action. Every reading is stored in an Oracle Application Express database accessed through RESTful web services, and a MIT App Inventor mobile application lets a grower monitor five simulated plots on a map and manually close a gate. Across the recorded test window, the system correctly distinguished normal conditions from three simulated hail events and one heavy-rain event in a single plot, while the remaining plots stayed within expected ranges. The paper also discusses the applicable IEEE IoT standards and the gap between the classroom pilot and a field-ready deployment.

Abstract: This paper presents an acoustic emission (AE) based Identification of Active Anomalies (IAA) system that integrates signal clustering, image recognition, and machine learning to monitor the structural condition of a highway overpass located near a major urban agglomeration. The monitoring results provide a reliable foundation for assessing structural health and implementing automated load (traffic) control, which is essential to ensure safe operations. AE signals recorded under service loads undergo multi-parametric analysis using pattern recognition techniques and are assigned to specific classes corresponding to active anomalies within the material or structure. Each class is linked to a distinct structural hazard level, ranging from safe operation to a critical loss of structural safety. Corresponding traffic control measures, including vehicle speed and weight restrictions, are dynamically introduced to maintain operational safety. The proposed methodology was experimentally validated on an A2 highway overpass, a vital component of the Łódź transport hub that facilitates north-south and east-west transit in Poland. The IAA system functions as a proactive diagnostic tool for infrastructure management agencies, preventing sudden, unforeseen structural failures. Ultimately, it enables the efficient and safe operation of a Smart City while optimization-driven funds are rationally allocated for road infrastructure maintenance.

Abstract: This study investigates model-based emergency strategies for damage prevention following cable breaks in six-degree-of-freedom cable-driven parallel robots. By relaxing real-time constraints, the theoretical performance ceiling of existing and novel recovery approaches is assessed through comprehensive simulations of the SEGESTA prototype. While extending prediction horizons improves recovery, it induces prohibitive computational loads. A hybrid approach combining State-of-the-Art approaches emerges as the optimal compromise, preserving high success rates while reducing calculation time and enabling active obstacle avoidance. Furthermore, a time-to-crash analyses after cable breaks demonstrates that the physical CDPR design fundamentally influences post-failure dynamics and could lead to narrow time windows for recovery approaches. Consequently, model-based emergency strategies are approaching their limits. Future research must overcome this by synergizing data-driven control paradigms with structurally fault-tolerant robot designs to ensure robust, real-time damage prevention after cable breaks.

Abstract: The article studies the problem of modeling the nonlinear characteristics of friction using digital twins for cutting process on metal-cutting machine tools. One of the important characteristics of a mathematical model for the cutting force response to the shaping movements of a cutting tool is the friction coefficient between a cutting tool’s flank wear land and the machined workpiece surface. The dependence of the friction coefficient on the temperature-velocity parameters during cutting is widely known, whereas the mathematical dependences within the general mathematical model for elastic-thermodynamic interaction during cutting, have not been determined yet. Therefore, the aim of the study is to create a mathematical model that would reveal the dependence of the friction coefficient between the cutting tool’s flank and the machined workpiece on the temperature of this interaction. To create a model, the authors relied on both the analysis of theoretical studies and the results of a full-scale experiment conducted to determine the actual value of the friction coefficient. By achieving the aim of the study, the authors have refined the general mathematical model for elastic-thermodynamic interaction occurring in the process of cutting on a lathe by taking into account the adhesive-diffusion nature of friction in the contact zone of the cutting tool’s flank and the machined workpiece. Furthermore, the general mathematical model has been validated based on the data obtained in a series of additional experiments using the modern STD.201.1 measuring bench.

Abstract: This paper aims to describe a synthesis procedure for discrete-time, energy-based regulators for continuous-time port-Hamiltonian systems. The methodology consists of three steps. The first deals with the definition of a discrete-time approximation of the plant, which is subsequently employed in the development of the control law. The discrete-time model is obtained from the continuous-time dynamics by replacing the gradient of the Hamiltonian function with a discrete gradient. In this way, passivity, with the energy as storage function, is preserved, although the resulting state equation is in implicit form. The second step concerns the control synthesis and extends the continuous-time energy-shaping plus damping injection design technique to the proposed class of discrete-time port-Hamiltonian systems. Finally, the last step addresses the interconnection between the digital controller and the continuous-time plant. The coupling is implemented via a zero-order hold and relies on the solution of an optimisation problem that determines the “best” and “minimal” correction to be applied to the nominal control action in order to achieve the same performance as that obtained when the regulator is connected in closed loop with the discrete-time model of the plant. This is the reference scenario used to develop and tune the control law. The complete procedure (time discretisation, control design, and coupling implementation) is illustrated through an example.

Abstract: This paper aims to enhance Smith-predictor-based control systems (SPCSs) for multi-input multi-output (MIMO) time delay processes. Conventional SPCSs for MIMO processes are composed of an array of classical feedback controller(s). These controllers act on error signals, calculated through deducting a predicted output by a Smith predictor from a reference signal at the time of operation. Investigations on underperformance of conventional SPCSs identified two major shortcomings: (i) the design of classical feedback controllers is based on trade-off, and their use may lead to windup phenomenon, this adversely influences conventional SPCSs performance, (ii) a predicted output by a Smith predictor belongs to a time in the future and does not concurrent with the reference at the time of operation; that is, in conventional SPCSs, the control error is generated using two asynchronous signals. This paper proposes an enhanced SPCS design method for MIMO time-delay systems based on two enhancements to tackle the aforementioned dual shortcomings. The proposed control system evidently outperforms a conventional SPCS with proportional-integral-derivative (PID) feedback controllers. The case study is a catalytic stirred tank reactor (CSTR) with three inputs (feed and water flow rates and auxiliary temperature), two outputs (output flow concentration and temperature) and three time delays. The presented model of the CSTR is more comprehensive than any CSTR model found in the literature.

Abstract: Battery swapping stations can shorten the charging service time of electric-vehicles, but the value of their operation depends on the coordination between high-power battery charging and renewable generation on one hand, and storage dispatch and tariff-driven grid exchange on the other. This paper develops a linear programming-based optimal power flow model for a grid-connected Battery Swapping Station incorporating photovoltaic generation, wind turbines, and a battery energy storage system. The proposed model manages eight power flow paths among the photovoltaic array, wind turbines system, battery energy storage system, utility grid, and battery swapping station demand. Its objective is to minimize the combined cost associated with electricity purchased from the grid and battery degradation while simultaneously maximizing revenue obtained through feed-in tariffs from surplus renewable generation. A 24-hour case study was conducted by integrating high and low demand season scenarios with a heavy duty battery swapping station demand profile, alongside Cape Town renewable resource data and South African time-of-use tariffs, to examine seasonal demand variations under both weekday and weekend operating conditions. The optimization results reduce the daily grid electricity expenditure from ZAR 7,676.39 to ZAR 564.81 during high-demand weekdays, from ZAR 4,093.28 to ZAR 1,330.89 during low-demand weekdays, and from ZAR 3,256.88 to ZAR 380.89 during low-demand weekends. Based on the analyzed operating conditions, the proposed energy management strategy yields estimated annual savings of approximately ZAR 1.389 million, with an expected discounted payback period of about five years. These findings demonstrate that coordinated dispatch of renewable generation and battery storage can substantially reduce dependence on grid electricity for Battery Swapping Station operation, although the overall economic performance remains strongly influenced by electricity tariff structures, demand characteristics, and assumptions regarding capital investment and operating costs.

Abstract: The layer by layer 3D material deposition remains challenging when applied to complex Additive 3D-manufacturing material. Such process involve paste extrusion which surface geometry, density of layer and intrinsic properties must be very accurate whereas improper printing leads to porosity, irregular surfaces and may cause layers squeezing. When these cases occur, adjusting printing parameters is necessary. But, this also means a presence of human in the monitoring process surveilling printing in real time. Computer vision Systems at an early stage of their appearance, have been introduced for automate detection of cracks, irregular layer deposition, and texture inconsistencies, which directly affect structural integrity and print quality. They are later enhanced when deep learning and AI driven texture classification are directly introduced in their software framework. Additive manufacturing has then emerged, enabling the fabrication of complex geometries with high precision, but these techniques remain challenging when considering real time monitoring. This study proposes an advanced computer vision-based intelligent monitoring framework for real-time defect detection in extrusion-based 3D printing. Based on an hybrid Unet-VGG19 architecture, the proposed approach, combines the effectiveness of a strong semantic segmentation capability using the U-Net decoder, whose role is to perform upsampling the learned features discriminated by a powerful VGG19 encoder. To achieve better performance in the final model, the strategy of data augmentation is applied, leading to improving in generalization of the learned representations, even when transferred for a downstream tasks in non linear systems. A convergence investigation has also been detailed in the methodology and mathematical formulation section showing gradient stability and the model ability in identifying inter-layer boundaries and layer height variations under variable printing conditions. Our presented framework is employed to detect surface texture irregularities and local defects covering both concrete and soil-based materials. The aim is to present a complementary fine-tuned Unet-VGG-based classification, achieving better performance metrics than when evaluated on the dataset derived from Rodrigo Garcia Rill’s experimental works.In fact, experimental results demonstrate that the proposed hybrid architecture outperforms significantly the existing method, leading to superior performance scores when compared with the baseline published, previously trained on non augmented data. This results in a new transferable deep learning knowledge parameters, for printing tasks under extensive ground truth conditions, enabling scalable and real time defect monitoring across complex material systems and paving the way towards a robust quality control procedures in additive manufacturing.

Abstract: This paper presents the design, simulation, and experimental validation of a Genetic Algorithm (GA)-optimized Active Disturbance Rejection Controller (ADRC) for voltage regulation in a standalone Doubly Fed Induction Generator (DFIG)-based Wind Energy Conversion System (WECS). The proposed system is designed to operate under variable wind conditions and nonlinear load profiles, with a battery energy storage system (BESS) connected to the rotor side to enhance performance and stability. A battery management algorithm is incorporated to ensure safe operation and maintain the state of charge (SOC) within optimal limits. The control strategy is evaluated through detailed simulations in MATLAB/Simulink and validated experimentally using a real-time dSPACE 1104 platform. Results show that the GA-tuned ADRC offers superior voltage regulation, fast transient response, and strong disturbance rejection compared to conventional PI controllers. Moreover, the integration of the battery management algorithm ensures reliable energy flow and SOC control, confirming the practical feasibility of the proposed method for isolated renewable energy applications.

Abstract: Unmanned aerial vehicle (UAV) swarms increasingly operate in GNSS-denied environments, where cooperative localization provides relative positioning by fusing onboard odometry with inter-agent range measurements, for which ultra-wideband (UWB) two-way ranging is a common infrastructurefree choice. Beyond accuracy, safe swarm autonomy needs a trustworthy measure of positioning uncertainty, since collision-avoidance and formation-keeping decisions derive their safety margins from the reported covariance. Under sustained agile flight, both are hard to achieve at once: motion within each time-division polling round induces a ranging bias well above the UWB noise floor, and reusing shared information across the network drives the reported covariance below the true error. To address these two problems jointly rather than in isolation, we propose a modular architecture coupling an online maximum-likelihood polynomial least-squares (MPLS) ranging front-end with a fading split covariance intersection (SCI) cooperative back-end through a per-link variance interface: online MPLS compensates the motion-induced bias and reports a calibrated, time-varying variance that fading SCI consumes as measurement noise while its continuous-time fading factor bounds the reused-information covariance. Monte Carlo simulation over anchored and anchor-free 16-node swarms shows the two effects to be empirically decoupled, with front-end ranging quality governing positioning accuracy and back-end correlation handling governing estimator consistency. The method attains sub-meter positioning accuracy in both settings and, without per-scenario tuning, keeps consistency—quantified by the average normalized estimation error squared (ANEES)—within a trusted band; comparably accurate extended Kalman filter and covariance-intersection baselines fall outside it, becoming overconfident and over-conservative, respectively. It thus delivers the trustworthy uncertainty that safety-critical swarm decisions require in GNSS-denied flight.