Existing solutions for collaborative trajectory planning using multiple UAVs suffer from issues such as low accuracy, instability, and slow convergence. To address the aforementioned issues, this paper introduces a new method for multiple unmanned aerial vehicle (UAV) 3D terrain cooperative trajectory planning based on the cuckoo search golden jackal optimization (CS-GJO) algorithm. A model for single UAV trajectory planning and a model for multi-UAV collaborative trajectory planning have been developed, and the problem of solving the models is restructured into an optimization problem. Building upon the original golden jackal optimization, the use of tent chaotic mapping aids in the generation of the golden jackal’s initial population, thereby promoting population diversity. Subsequently, the position update strategy of the cuckoo search algorithm is combined for purpose of update the position information of individual golden jackals, effectively preventing the algorithm from getting stuck in local minima. Finally, the corresponding nonlinear control parameter were developed. The new parameters expedite the decrease in the convergence factor during the pre-exploration stage, resulting in an improved overall search speed of the algorithm. Moreover, they attenuate the decrease in the convergence factor during the post-exploration stage, thereby enhancing the algorithm’s global search. The experimental results demonstrate that the CS-GJO algorithm efficiently and accurately accomplishes multi-UAV cooperative trajectory planning in a 3D environment. Compared with other comparative algorithms, the CS-GJO algorithm also has better stability, higher optimization accuracy, and faster convergence speed.

In global navigation satellite system denial environment, cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle (UAV) systems. The essence of cross-view geo-localization resides in matching images containing the same geographical targets from disparate platforms, such as UAV-view and satellite-view images. However, images of the same geographical targets may suffer from occlusions and geometric distortions due to variations in the capturing platform, view, and timing. The existing methods predominantly extract features by segmenting feature maps, which overlook the holistic semantic distribution and structural information of objects, resulting in loss of image information. To address these challenges, dilated neighborhood attention Transformer is employed as the feature extraction backbone, and Multi-feature representations based on Multi-scale Hierarchical Contextual Aggregation (MMHCA) is proposed. In the proposed MMHCA method, the multiscale hierarchical contextual aggregation method is utilized to extract contextual information from local to global across various granularity levels, establishing feature associations of contextual information with global and local information in the image. Subsequently, the multi-feature representations method is utilized to obtain rich discriminative feature information, bolstering the robustness of model in scenarios characterized by positional shifts, varying distances, and scale ambiguities. Comprehensive experiments conducted on the extensively utilized University-1652 and SUES-200 benchmarks indicate that the MMHCA method surpasses the existing techniques, showing outstanding results in UAV localization and navigation.