Unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) have rapidly evolved, becoming integral to various applications such as environmental monitoring, disaster response, and precision agriculture. This paper provides a comprehensive review of the advancements and the challenges in UAV-UGV collaboration and its potential applications. These systems offer enhanced situational awareness and operational efficiency, enabling complex tasks that are beyond the capabilities of individual systems by leveraging the complementary strengths of UAVs and UGVs. Key areas explored in this review include multi-UAV and multi-UGV systems, collaborative aerial and ground operations, and the communication and coordination mechanisms that support these collaborative efforts. Furthermore, this paper discusses potential limitations, challenges and future research directions, and considers issues such as computational constraints, communication network instability, and environmental adaptability. The review also provides a detailed analysis of how these issues impact the effectiveness of UAV-UGV collaboration.

This study proposes a modular path optimization framework for uncrewed ground vehicles (UGVs) in uncrewed aerial vehicle (UAV)-assisted navigation environments to improve the efficiency, smoothness, and executability of paths generated by classical grid-based path planning algorithms. The principal innovation of this work is the Visibility and Line-of-Sight Path Simplification (VLoSPS) algorithm, an algorithm-independent post-processing method that removes redundant waypoints through long-range axis-aligned visibility analysis while preserving path feasibility. VLoSPS is integrated with the Direction-Aware Path Planning Approach (DAPPA) to reduce angular deviations and improve directional continuity. The proposed framework is applicable to standard algorithms, including A*, Dijkstra, Breadth-First Search (BFS), and Depth-First Search (DFS), without modifying their internal search mechanisms. The main academic contributions comprise the formulation of a generalized post-processing architecture for UAV-derived occupancy maps, the introduction of a visibility-aware waypoint reduction strategy, and extensive validation using two synthetic maze datasets and three UAV-derived semantically segmented real-world datasets. On the Göttingen Maze Dataset, the VLoSPS and DAPPA pipeline reduced the average path lengths of A*, Dijkstra, BFS, and DFS by 5.42%, 9.46%, 10.44%, and 86.00%, respectively. The consistent improvements across real-world datasets demonstrate the effectiveness, computational feasibility, and general applicability of the proposed framework for UAV-assisted UGV path planning. The implementation code and benchmark resources developed in this study are publicly released to promote reproducibility and facilitate future research.

Grid-based path planning and optimization remain widely used in robotics and autonomous navigation because real environments can be discretized into binary occupancy grids, allowing algorithms such as A* to guarantee a solution whenever a collision-free path exists. However, the resulting grid paths are often suboptimal in length and contain unnecessary directional changes, which increase traversal time, energy consumption, and control effort. This paper proposes Supercover-Based Corridor Reconstructed A* (SBCR-A*), a framework that combines an A* search backbone with an SBCR reconstruction pipeline to convert discrete grid paths into trajectories with improved path-length optimality while preserving collision-freedom. SBCR-A* utilizes a supercover-based line-of-sight formulation together with obstacle-boundary-guided refinement to replace stepwise grid paths with a small number of long, straight, feasible segments. On the Göttingen Maze Dataset, SBCR-A* achieves a path optimality of $$107.72\%$$ 107.72 % relative to standard A*, which is the highest path optimality among all methods evaluated in this study, including recent learning-based planners and existing A*-based improvements. Moreover, when the SBCR-A* reconstruction pipeline is decoupled from its own search component and applied to paths produced by these A*-based variants, it delivers additional average path-length reductions in the range $$1.85\%-5.71\%$$ 1.85 % - 5.71 % . To the best of our knowledge, no existing reconstruction method achieves higher path optimality on these benchmarks while providing equally general, planner-agnostic integration. Consequently, SBCR-A* can be regarded as a state-of-the-art reconstruction framework for improving the geometric quality and optimality of grid-based paths.

The increasing demand for compact, efficient, and cost-effective power conversion systems across sectors such as renewable energy, industrial automation, and telecommunications has highlighted the need for high-performance DC-DC converters. This paper presents the design, implementation, and experimental validation of a 500 W half-bridge LLC resonant DC-DC converter for 63-75 V input and a regulated 14.5 V output. A key feature of the design is resonant-tank optimization to maintain inductive-region operation and allow soft switching, with experimental evaluation of the soft-switching margin that identifies where ZVS degrades near high-load operation. The incorporation of GaN FETs allows 100 kHz operation with peak efficiency above 90%, and a controlled GaN-versus-MOSFET comparison on the same platform under identical conditions shows a load-dependent efficiency improvement that increases toward higher output power, ranging from approximately 1% to 10%. Practical high-frequency hardware effects, including gate-drive and auxiliary-winding oscillations, are characterized and mitigated using a simple decoupling-and-gate-tuning approach. In addition, an adaptive protection framework is integrated, including configured input over- and under-voltage thresholds and multi-level over-current protection, to provide safe operation. Experimental validation through efficiency measurements and dynamic-load testing confirms stable behaviour over the tested operating range.