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.