This paper addresses the core issues of slow coverage rate growth and high repeated detection rates in multi-UAV cooperative search operations within unknown areas. A distributed cooperative search algorithm based on the maximum entropy mechanism is proposed to resolve these challenges. It innovatively integrates the entropy gradient decision framework with DMPC-OODA (Distributed Model Predictive Control-Observe, Orient, Decide, Act) rolling optimization: environmental uncertainty is quantified through an exponential decay entropy model to drive UAVs to migrate toward high-entropy regions; element-wise product operations are employed to efficiently update environmental maps; and a dynamic weight function is designed to adaptively adjust the weights of coverage gain and entropy gain, thereby balancing “rapid coverage” and “accurate exploration”. Through multiple independent repeated experiments, the algorithm demonstrates significant improvements in coverage efficiency—by 6.95%, 12.22%, and 59.49%, respectively—compared with the Search Intent Interaction (SII) mode, non-entropy mode, and random mode, which effectively enhances resource utilization.

Three-dimensional reconstruction using unmanned aerial vehicle (UAV) platforms has been extensively utilized in various fields. While conventional techniques such as oblique photogrammetry can produce mesh models with geographical references, they often require substantial computational resources. Although recent studies have attempted to incorporate camera pose parameters into the emerging 3D Gaussian Splatting (3DGS), these methods often treat georeferencing as a post-processing step or rely on global bundle adjustment, which may propagate systematic errors and compromise final accuracy. This work integrates georeferencing as an intrinsic constraint during 3DGS training, enabling simultaneous optimization of geographic and photometric accuracy. The core of our approach lies in introducing a similarity transformation matrix T connecting the local model space with the global geographic coordinate system, along with a dedicated geographic loss function. Geographic coordinates are transformed via T before reprojection to compute the loss function. It was demonstrated that GeoRefGS presents a viable solution for efficiently integrating georeferenced information into 3DGS. Indeed, the proposed framework achieves an improvement of approximately 3.31 dB in peak signal-to-noise ratio while maintaining distance errors below 0.054 m, enabling reliable geographically referenced 3D reconstruction in substantially less time compared to conventional photogrammetric approaches.

Multi-UAV cooperative search with static unknown targets requires both efficient regional allocation and responsive local maneuvering. However, single-level learning methods often suffer from redundant coverage, unclear division of labor, and unstable training. This paper proposes a high- and low-level decoupled reinforcement learning method for multi-UAV cooperative search. The high level periodically generates UAV-specific regional goals from visitation maps, target-existence belief maps, and UAV positions, while a spatial self-attention module enhances the representation of unvisited regions, high-belief target areas, and UAV distributions. The low level performs discrete steering actions based on local observations and high-level contexts, supported by a structured reward that encourages coverage, target discovery, goal-oriented progress, repeated-visit suppression, and boundary-safe motion. Simulation experiments are conducted in a two-dimensional grid environment with static targets and ideal sensing. Under this simplified simulation setting, the proposed method achieves higher training return and coverage rate than representative baseline algorithms while maintaining a high final target discovery rate and reaching the discovery threshold earlier. Ablation and visualization results further demonstrate the effectiveness and interpretability of the proposed hierarchical guidance mechanism within the considered simulation scenario.