With the continuous development of UAV technology and swarm intelligence technology, the UAV formation cooperative mission has attracted wide attention because of its remarkable function and flexibility to complete complex and changeable tasks, such as search and rescue, resource exploration, reconnaissance and surveillance. The collaborative trajectory planning of UAV formation is a key part of the task execution. This paper attempts to provide a comprehensive review of UAV formation trajectory planning algorithms. Firstly, from the perspective of global planning and local planning, a simple framework of the UAV formation trajectory planning algorithm is proposed, which is the basis of comprehensive classification of different types of algorithms. According to the proposed framework, a classification method of existing UAV formation trajectory planning algorithms is proposed, and then, different types of algorithms are described and analyzed statistically. Finally, the challenges and future research directions of the UAV formation trajectory planning algorithm are summarized and prospected according to the actual requirements. It provides reference information for researchers and workers engaged in the formation flight of UAVs.

UAV obstacle avoidance technology is one of the key factors to realize UAV autonomous flight, efficient and accurate obstacle avoidance is significant to complete the UAV autonomous flight task. In contrast, the dynamic, real-time and uncertainty of the environment in which the UAV is located makes the problem very tricky, especially in the indoor environment. At present, a large number of scholars have shown strong interest in the indoor UAV obstacle avoidance problem. With the rapid development of computer technology and hardware devices, many intelligent algorithms have been proposed to solve the obstacle avoidance problem. However, the research on indoor UAV obstacle avoidance technology is not comprehensive enough, and there is a lack of summarization of the research results in recent years. This paper introduces the sensor modules commonly used for indoor UAV environment sensing, related obstacle avoidance methods based on sensory detection. Classifies and composes the commonly used UAV path planning obstacle avoidance algorithms, and gives several representative UAV flight control research methods. This paper summarizes the advantages and disadvantages of different perception modules and detection methods applied to UAV obstacle avoidance tasks, and compares various current path planning methods. Finally, the critical difficulties and challenges faced in the field of indoor UAV obstacle avoidance are discussed, and future research in the field of UAV obstacle avoidance has prospected.

Unmanned Aerial Vehicles (UAVs) have been extensively researched and used in civil and military applications due to their effectiveness and flexibility. However, when identifying obstacles and avoiding them, most of the existing path planning methods fail to accurately perceive the environment, such as without considering the differences between obstacles, which leads to low timeliness and easy fall into a local minimum. In this work, an improved artificial potential field UAV path planning algorithm (G-APF) guided by the rapidly-exploring random tree (RRT) based on an environment-aware model is designed to overcome the limitations of traditional methods. The model can perceive different objects in the environment through the addition of supervised environment modeling to traditional unsupervised path planning. Specifically, an environment-aware model based on YOLOv8 is used to establish the UAV flight environment model, and an adaptive optimal threat distance calculation module is used to construct the repulsive potential field. Secondly, to improve the timeliness of path planning and the global awareness of the model, we first use the G-APF algorithm to plan the rough flight path based on the UAV flight environment. Then, the initially generated trajectory is replanned by building an attractive potential field and combining it with a repulsive potential field. Finally, the problems of local minimum and target unreachability and local trajectory oscillation generated by the artificial potential field (APF) algorithm are solved by G-APF. Experiments with generated regions are performed to demonstrate the efficiency and effectiveness of the proposed approach.

Aiming at the demand of multi-UAV missions in urban low-altitude environment, combined with the concept of low-altitude economy, the urban environment is divided into multiple layers in the vertical direction to accomplish path planning. The traditional ant colony algorithm has problems such as slow planning speed and easy to fall into local optimization when performing UAV urban environment path planning. In order to improve these problems, we combine the Ant Colony Algorithm (ACO) with the Particle Swarm Algorithm (PSO), and utilize the early and fast convergence of the PSO to generate a suboptimal solution as the initial condition of the pheromone distribution of the ACO. Meanwhile, parameter adaptive optimization and forbidden strategy optimization were performed for the ACO, and parameter adaptive optimization and local optimum optimization were performed for the PSO. In addition, the energy consumption constraints and layering constraints of the UAVs are considered, and finally the paths are optimized using NURBS curves so that each UAV can reach the end point of its respective layer. The experimental results show that the average optimal fitness of the algorithm is improved by 22.2% and the algorithm running time is reduced by 33.0% compared to the traditional ACO.

A combined control method integrating Linear Active Disturbance Rejection Control (LADRC) and Sliding Mode Control (SMC) is proposed to mitigate model uncertainty and external disturbances in the attitude control of fixed−wing unmanned aerial vehicles (UAVs). First, the mathematical and dynamic models of a small fixed−wing UAV are constructed. Subsequently, a Linear Extended State Observer (LESO) is designed to accurately estimate the model uncertainties and unidentified external disturbances. The LESO is then integrated into the control side to enable the SMC to enhance the control system’s anti−interference performance due to its insensitivity to variations in−system parameters. The system’s stability is proven using the Lyapunov stability theory. Finally, simulations comparing the classical LADRC and the newly developed SMC−LADRC reveal that the latter exhibits strong robustness and anti−interference capabilities in scenarios involving model uncertainty, external disturbances, and internal disturbances, confirming the effectiveness of this control method.

To enable safe cooperative flight of multi-UAV formations in urban 3-D airspace with wind-field disturbances, we develop an integrated planning-control framework.The planning layer uses an APF-guided RRT* with continuous collision prediction and explicit velocity/acceleration limits, and compensates wind online.The control layer adopts a dual-loop MA-AADRC-SMC design. An adaptive ESO estimates disturbances for feed-forward cancellation, and an SMC term improves robustness and tracking accuracy. By coupling the planned trajectory with speed-weighted repulsive fields, the framework coordinates path and attitude in closed loop, enabling collision-free and overshoot-free formation flight in wind and clutter. Simulations show higher tracking accuracy and better formation stability than ADRC, PID and SMC. A Lyapunov analysis proves uniform boundedness and asymptotic stability. The framework is scalable to applications such as disaster assessment and urban air transport.