Energy management plays a crucial role in achieving extended endurance for solar-powered Unmanned Aerial Vehicles (UAVs). Current studies in energy management primarily focus on natural energy harvesting and task-oriented path planning. This paper aims to optimize energy consumption during the climb and glide stages by exploring variable climb speeds and glide powers. To achieve this, fitness functions are established for both the climb and glide stages, taking into account the maximum climb speed and glide power limits of the aircraft. The particle swarm optimization (PSO) algorithm is employed to solve the problem, resulting in significant energy savings of over 68% in the climb stage and 4.8% in the glide stage. Based on an analysis of the optimization trends, this study proposes an energy-management strategy to fulfill the demand for long-endurance flights. The findings of this study can serve as a valuable reference for high-altitude missions that require extended flight times.

With the rapid development of remote sensing technology, remote sensing target detection faces many problems; for example, there is still no good solution for small targets with complex backgrounds and simple features. In response to the above, we have added dynamic snake convolution (DSC) to YOLOv7. In addition, SPPFCSPC is used instead of the original spatial pyramid pooling structure; the original loss function was replaced with the EIoU loss function. This study was evaluated on UAV image data (VisDrone2019), which were compared with mainstream algorithms, and the experiments showed that this algorithm has a good average accuracy. Compared to the original algorithm, the mAP0.5 of the present algorithm is improved by 4.3%. Experiments proved that this algorithm outperforms other algorithms.

As the only flying mammal in nature, bats have superb flight skills and aerodynamic characteristics that have been the subject of research by scholars from all over the world. In recent years, the research on bionic flapping-wing aircraft has made good progress. However, such research mostly uses birds or insects as the research objects, and there are few studies on bat-imitating flapping-wing aircraft. This paper combines the characteristics of bats’ flexible wings to model and analyze the aerodynamic theory and parameters of the flexible wings of bat-like flapping aircraft. The longitudinal dynamic and kinematic model design of bat-like flapping aircraft is based on the pitch angle of LQR. In terms of height control, the controller uses energy control methods to complete the closed-loop longitudinal channel control of the bat-like flapping aircraft. Finally, this study performed the simulation and flight experimentation of the designed bat-like flapping aircraft, demonstrating the correctness of this system.

This article describes a quadcopter drone with foldable arms, which we have named the Peregrine Falcon. It can take off from a moving vehicle by barrel launching and perform its mission in the air after autonomously spreading its arms. The design helps the drone to be put into operation quickly, which is a great advantage for applications in industries such as forest fire reconnaissance. This paper introduces the design process of the Peregrine Falcon from the aspects of structure, avionics and control algorithm, and proposes control compensation methods for the large attitude change generated during the barrel launching process and the vertical drift problem after impact, respectively. The stability and feasibility of the Peregrine Falcon in the static and dynamic launching process are verified by flying experiments.