The current challenge in drone swarm technology is three-dimensional path planning and adaptive formation changes. The traditional A* algorithm has limitations, such as low efficiency, difficulty in handling obstacles, and numerous turning points, which make it unsuitable for complex three-dimensional environments. Additionally, the robustness of drone formations under the leader–follower mode is low, and effectively handling obstacles within the environment is challenging. To address these issues, this study proposes a virtual leader mode for drone formation flight and introduces a new Theta*–APF method for three-dimensional space drone swarm path planning. This algorithm optimizes the A* algorithm by transforming it into an omnidirectional forward Theta* algorithm. It also enhances the heuristic function by incorporating artificial potential field methods in a three-dimensional environment. Formation organization and control of UAVs is achieved using speed-control modes. Compared to the conventional A* algorithm, the Theta*–APF algorithm reduces the search time by about 60% and the trip length by 10%, in addition to the safer flight of the UAV formation, which is subject to artificial potential field repulsion by about 42%.

The emergence of large language models (LLMs), such as GPT (Generative Pre-trained Transformer), has had a profound impact and brought about significant changes across various sectors of human society. Integrating GPT-3.5 into a multi-robot control system, termed MultiBotGPT (Multi-Robot Control System with GPT), represents a notable application. This system utilizes layered architecture and modular design to translate natural language commands into executable tasks for UAVs (Unmanned Aerial Vehicles) and UGVs (Unmanned Ground Vehicles), enhancing capabilities in tasks such as target search and navigation. Comparative experiments with BERT (Bidirectional Encoder Representations from Transformers) in the natural language-processing component show that MultiBotGPT with GPT-3.5 achieves superior task success rates (94.4% and 55.0%) across 50 experiments, outperforming BERT significantly. In order to test the auxiliary role of the MultiBotGPT-controlled robot on a human operator, we invited 30 volunteers to participate in our comparative experiments. Three separate experiments were performed, Participant Control (Manual Control only), Mix Control (Mix Manual Contr and MultiBotGPT Control), and MultiBotGPT Control (MultiBotGPT Control only). The performance of MultiBotGPT is recognized by the human operators and it can reduce the mental and physical consumption of the human operators through the scoring of the participants’ questionnaires.

Recently, electric vertical take-off and landing (eVTOL) aircraft have become a top priority for urban air transportation due to their ability to overcome urban ground traffic congestion. In this research, a new type of scaled lift–cruise ‘Dragonfly’ has been designed. The ‘Dragonfly’ combines the characteristics of an octocopter and a fixed-wing aircraft. Compared with the same type of eVTOL aircraft, it has a longer wingspan and a more stable aircraft structure, it can not only take off and land vertically without the need for a runway, but also fly quickly in a straight line and hover in mid-air. In order to ensure the success of the flight test, it was also simulated in this paper. A simulation scenario highly fitting with the flight test environment of eVTOL is designed in the Gazebo simulation platform, and then combined with the PX4 flight control platform, the system SITL of the constructed aircraft simulation model is carried out on the Gazebo platform, Finally, simulation flight test data for accurate analysis are obtained, the accuracy and stability of the control algorithm are fed back, and scientific support for the follow-up ‘Dragonfly’ aircraft hardware-in-the-loop simulation and physical flight test is provided.

Recently, rescue robots have been used in scenarios such as earthquakes, chemical explosions, etc. The challenges for these robots lie in terrain awareness and target recognition by machine vision. The rescue environments are complex, with harsh weather changes and challenging terrain, leading to differences in the temporal and spatial domains between the visual training and test data. Traditional cross-domain sample set collection methods are time-consuming and heavily rely on large sample sizes; furthermore, obtaining a comprehensive training sample set under extreme weather conditions, including various time domains (e.g., ice, snow) and spatial domains (e.g., fog, poison gas), is difficult. This article proposes an across time-space environment adaptive perception method for rescue robots based on an improved CycleGAN termed “DenseNet CycleGAN” (DN-CycleGAN). With the support of a terrain diversified-sample synthesis method based on style transfer, it can enhance the transition layer and discriminator of the cycle network generator. Compared with the traditional CycleGAN, the DN-CycleGAN in this article has a 5.26% higher PSNR score and a 9.27% higher SSIM, and the DN-CycleGAN obtains a more realistic image to a certain extent, with color characteristics closer to the target style image. In this article, multiple recognition Xception (MR-Xception), a terrain classification algorithm based on Xception transfer learning, is proposed, which can cope with the low accuracy of traditional deep neural networks in terrain recognition and training time, which can deal with the low accuracy of traditional deep neural networks in terrain recognition and training time. This method enables the diverse cross-domain recognition of small sample terrains and enhances the terrain recognition accuracy to 99.54%. It also ensures the transfer of the fundamental terrain style across various time-space domains; this method enables the cross-domain migration of terrain dataset for the task of robot’s terrain recognition across different domains. Finally, it facilitates rapid visual perception and self-adaptation of robots in cross-domain tasks within unstructured environments.