We propose a novel systematic approach for the deployment optimization of unmanned aerial vehicles (UAVs). In this context, this study focuses on enhancing the coverage of UAV-mounted 6G mobile base stations. The number and placement optimization of UAV-mounted 6G mobile base stations, deployed to support terrestrial base stations during periods of increased population density in a given area, are addressed using a symbolic limited optimal discrete controller synthesis technique. Within the scope of this study, the UAVs’ altitude and attitude behaviors are optimized to ensure the most efficient trajectory toward the designated base station coordinates. Additionally, at their new locations, these behaviors are adjusted to facilitate accurate coverage estimation from the base stations they serve. In the deployment optimization of UAVs, the placement of base stations is determined using received signal strength data obtained through the ray-tracing-based channel modeling technique. The channel model considered critical parameters such as path loss, received power, weather loss, and foliage loss. Final average path loss values of 102.3 dB, 111.7 dB, and 127.4 dB were obtained at the carrier frequencies of 7 GHz, 26 GHz, and 140 GHz, respectively. These findings were confirmed with MATLAB-based ray tracing simulations. Our proposed approach is validated through experimental evaluations, demonstrating superior performance compared to existing methods reported in the literature.

Task scheduling for multiple humanoid robot manipulators in industrial and collaborative settings remains a significant challenge due to the complexity of coordination, resource sharing, and real-time decision-making. In this study, we propose a framework for modeling task scheduling for multiple humanoid robot manipulators by using the symbolic discrete controller synthesis technique. We encode the task scheduling problem as discrete events using parallel synchronous dataflow equations and apply our synthesis algorithms to manage the task scheduling of multiple humanoid robots via the resulting controller. The control objectives encompass the fundamental behaviors of the system, strict rules, and mutual exclusions over shared resources, categorized as the safety property, whereas the optimization objectives are directed toward maximizing the throughput of robot-processed products with optimal efficiency. The humanoid robots considered in this study consist of two pairs of six-degree-of-freedom (6-DOF) robot manipulators, and the inverse kinematics problem of the 6-DOF arms is addressed using metaheuristic approaches inspired by biomimetic principles. Our approach is experimentally validated, and the results demonstrate high accuracy and performance compared to other approaches reported in the literature. Our approach achieved an average efficiency improvement of 40% in 70-robot systems, 20% in 30-robot systems, and 10% in 10-robot systems in terms of production throughput compared to systems without a controller.

In this study, we propose an architectural model for path optimization in cluster order picking within warehouse robotics, utilizing a hybrid approach that combines symbolic control and metaheuristic techniques. Among the optimization strategies, we incorporate bio-inspired metaheuristic algorithms such as the Walrus Optimization Algorithm (WOA), Puma Optimization Algorithm (POA), and Flying Foxes Algorithm (FFA), which are grounded in behavioral models observed in nature. We consider large-scale warehouse robotic systems, partitioned into clusters. To manage shared resources between clusters, the set of clusters is first formulated as a symbolic control design task within a discrete synthesis framework. Subsequently, the desired control goals are integrated into the model, encoded using parallel synchronous dataflow languages; the resulting controller, derived using our safety-focused and optimization-based synthesis approach, serves as the manager for the cluster. Safety objectives address the rigid system behaviors, while optimization objectives focus on minimizing the traveled path of the warehouse robots through the constructed cost function. The metaheuristic algorithms contribute at this stage, drawing inspiration from real-world animal behaviors, such as walruses’ cooperative movement and foraging, pumas’ territorial hunting strategies, and flying foxes’ echolocation-based navigation. These nature-inspired processes allow for effective solution space exploration and contribute to improving the quality of cluster-level path optimization. Our hybrid approach, integrating symbolic control and metaheuristic techniques, demonstrates significantly higher performance advantage over existing solutions, with experimental data verifying the practical effectiveness of our approach. Our proposed algorithm achieves up to 3.01% shorter intra-cluster paths compared to the metaheuristic algorithms, with an average improvement of 1.2%. For the entire warehouse, it provides up to 2.05% shorter paths on average, and even in the worst case, outperforms competing metaheuristic methods by 0.28%, demonstrating its consistent effectiveness in path optimization.

Voltage dips and short interruptions are major power quality problems that can affect adversely sensitive electronic devices such as LED lighting systems. In this study, the post-fault behavior of an LED driver circuit based on Flyback Converter (FBC) under these disturbances was experimentally analyzed. The test results showed that the driver maintained stable operation in voltage dips up to 60% (dip voltage ≈125 V) and interruptions up to 200 ms, without any user intervention. The output current was still 498 mA and the output voltage was 40.6 V, indicating good immunity to both disturbances. The results demonstrate the robustness of the FBC-based LED driver under power quality faults and give useful insights for designing robust lighting systems.

Güç elektroniği dönüştürücüleri, elektromanyetik girişim (EMI) açısından hassas yapılara sahiptir ve özellikle IP66 gibi yüksek koruma sınıflarında kullanılan dolgu (potting) malzemeleri, sistemin elektromanyetik davranışını doğrudan etkileyen kritik bir bileşen haline gelir. Bu malzemeler, devre bileşenlerini çevresel etkilere karşı korurken aynı zamanda devre-yapı arasındaki dielektrik ortamı değiştirir ve parazitik kapasitansları artırarak rezonans kaynaklı iletkenlik emisyonu (CE) piklerinin yükselmesine neden olabilir. Bu çalışmada, bir güç elektroniği modülünde gözlemlenen 2.438 MHz frekansındaki CE limit aşımının, dolgu malzemesinin bağıl dielektrik sabiti (εr) ve kayıp faktörü (tan δ) ile ilişkisi araştırılmıştır.Çalışmanın temel amacı, dolgu malzemesinin elektromanyetik özelliklerini laboratuvar ölçümüne gerek kalmadan ters karakterizasyon yöntemi ile belirlemektir. Bu kapsamda, ölçülen CE spektrumu ve devre parametreleri kullanılarak frekans alanında bir optimizasyon modeli kurulmuş, malzemenin εr ve tan δ parametreleri simülasyon–ölçüm uyumu üzerinden tahmin edilmiştir. Rezonans frekansı ve pik genliği, malzeme parametrelerinin duyarlılığı açısından analiz edilmiş; yüksek εr değerlerinin parazitik LC rezonansını kaydırdığı, düşük tan δ değerlerinin ise sönümleme yetersizliği nedeniyle pik genliğini artırdığı gösterilmiştir.Elde edilen sonuçlar, dolgu malzemesi kaynaklı EMC problemlerinin yalnızca tasarım aşamasında değil, saha uygulamalarında da yanlış malzeme seçimi nedeniyle ortaya çıkabileceğini göstermektedir. Ters karakterizasyon yaklaşımı, fiziksel numune testlerine olan bağımlılığı azaltarak EMC doğrulama süreçlerini hızlandıran etkili bir mühendislik aracı sunmaktadır.