A wide range of research has been conducted on path planning and collision avoidance to enhance the operational efficiency of unmanned aerial vehicles (UAVs). The existing works have mainly assumed an environment with static obstacles and global positioning system (GPS) signals. However, practical environments have often been involved with dynamic obstacles, dense areas with numerous obstacles in confined spaces, and blocked GPS signals. In order to consider these issues for practical implementation, a deep reinforcement learning (DRL)-based method is proposed for path planning and collision avoidance in GPS-denied and dense environments. In the proposed method, robust path planning and collision avoidance can be conducted by using the received signal strength (RSS) value with the extended Kalman filter (EKF). Additionally, the attitude of the UAV is adopted as part of the action space to enable the generation of smooth trajectories. Performance was evaluated under single- and multi-target scenarios with numerous dynamic obstacles. Simulation results demonstrated that the proposed method can generate smoother trajectories and shorter path lengths while consistently maintaining a lower collision rate compared to conventional methods.

With the recent advancement of virtual try-on (VTO) technology, it is being applied to various fields. Although advancements in VTO technology have enabled not only 2D but also 3D visualization, applying style transfer to hyper-local regions remains challenging due to the complex surface curvature and ambiguous boundaries of 3D objects. To address these challenges, we propose an immersive 3D style transfer framework based on 3D Gaussian splatting. A segmentation model is employed to accurately segment target regions, and a large-scale specialized dataset is constructed to capture the morphological diversity of human hands. Furthermore, neural style transfer is integrated with the 3D representation to enable precise style application to hyper-local regions. The proposed framework achieves a mean intersection of union (mIoU) of 0.806 in segmentation and high-fidelity stylization with learned perceptual image patch similarity (LPIPS) and reference-based LPIPS (Ref-LPIPS) scores of 0.1472 and 0.0196, respectively. These results indicate that the proposed framework can provide the quality requirements and immersive VTO experience.

An integration of deep learning and classical filtering techniques has been developed for achieving an accurate and robust positioning system. Despite significant technological advances, positioning systems often face fundamental challenges such as signal obstruction, multipath interference, and error drift. Although 3D map matching techniques using LiDAR sensors can mitigate these challenges, their practical implementation is limited by high costs, computational burdens, and dependency on pre-modeled environments. In response, recent research has increasingly focused on enhancing positioning systems through deep learning–based sensor fusion and adaptive filtering methods, emphasizing improvements in accuracy and operational robustness. In this paper, a seamless hierarchical context-aware intelligent positioning system (SH-CAIPS) is proposed by integrating sensor fusion, two-stage outlier mitigation gates, and deep reinforcement learning (DRL)-based noise scaling with an innovation-based adaptive Kalman filter (IAKF). As a result, the proposed positioning system can enhance robustness by adaptively adjusting measurement noise and dynamically updating the position based on the confidence score between model predictions and sensor measurements. From simulation results, it is confirmed that the positioning performances can be significantly improved based on the proposed SH-CAIPS compared with conventional positioning systems.

Computing services can be extended to infrastructure-limited areas through unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC). However, service provision continues to be challenging when spatially clustered ground users (GUs) are initially unknown and their intermittent workloads expire within finite validity windows. Therefore, conventional trajectory controllers assuming known GU locations and requests are unsuitable under sensing-limited conditions, where out-of-range workloads remain hidden between visits. Partially observable control has been adopted to address these challenges; however, existing frameworks are insufficient, as the discovery of unknown service regions and the repeated revisits required to re-observe hidden demand are not jointly considered. In this paper, a deadline-paced patrol with goal-conditioned multi-agent reinforcement learning (DPP-GCMARL) framework is proposed. In the proposed framework, a rule-based deadline-paced patrol (DPP) layer converts sensing-derived cell memory into deconflicted target cells using staleness normalized by the collection deadline. The assigned targets are then mapped to continuous movement commands by a learned goal-conditioned movement (GCM) layer. Simulation results show that DPP-GCMARL improves the end-to-end completed-workload ratio compared with multi-agent proximal policy optimization (MAPPO). It also increases exploration-coupled fairness and the worst-cluster collection ratio while reducing the mean travel per UAV. These results substantiate the proposed framework’s potential for deadline-constrained MEC service in infrastructure-limited environments.