In path planning and guidance algorithms for vehicles, such as unmanned aerial vehicles (UAVs) and missiles, it is essential and imperative to account for obstacle avoidance in complicated flight environment (e.g., no-fly zones). This article presents a novel safe guidance scheme with the guarantee of critical obstacle avoidance for intercepting maneuvering targets. First, the engagement of the missile and the target is formulated as nonlinear planer pursuit-evasion dynamics, and the interaction between the obstacle and the missile are determined. Then, the maneuvering target knowledge is estimated online by the extended disturbance observer (EDO) and incorporated into a separated feedback term of the input channel for the implementation of an approximately optimal actor–critic guidance law framework. To render the safety, the obstacle is thought as constraint objects and mathematically described by high-order control barrier functions (HO-CBFs). Furthermore, HO-CBFs are synthesized with the proposed guidance framework to intercept the maneuvering target with obstacle settings. Finally, numerical simulations under various types of nonstationary targets are performed to illustrate the feasibility and effectiveness of the proposed scheme.

In this study, a state-following-kernel-based reinforcement learning (RL) method with an extended disturbance observer is proposed, whose application to a missile-target interception system is considered. Firstly, the missile-target engagement is formulated as a vertical planar pursuit-evasion problem. The target maneuver is then estimated by an extended disturbance observer in real time, which leads to 10 an infinite-horizon optimal regulation problem. Next, utilizing the local state approximation ability of state-following kernels, the critic neural network (NN) and actor NN for synchronous iteration are constructed to calculate the approximate optimal guidance policy. The states and NN weights are proven to be uniformly ultimately bounded (UUB) using the Lyapunov method. Finally, numerical simulations against different types of nonstationary targets are effectively tested, and the results highlight the role 15 of state-following kernels in the value function and policy approximation.

In order to address the potential collision issue arising from multiple fixed-wing unmanned aerial vehicles (UAVs) intercepting targets in n-on-n and n-on-1 pursuit-evasion scenarios, we propose a collision-avoidance guidance strategy for UAVs based on high-order control barrier functions (HOCBFs). Initially, a two-dimensional model of multiple UAVs and targets is established, and the interaction between UAVs is determined. Subsequently, the collision-avoidance problem within a UAV swarm is formulated as a mathematical problem involving multiple constraints in the form of higher-order control obstacle functions. Multiple HOCBF constraints are then simplified into a single linear constraint for computational convenience. By integrating HOCBF constraints with quadratic programming problems, we obtain a closed-form solution for UAVs that incorporates collision-avoidance guidance terms alongside nominal guidance terms. Simulations with different numbers of pursuers and different target motion states are conducted. The results demonstrate an excellent experimental effect, ensuring that the multi-UAVs consistently remain above the minimum safe distance and ultimately hit the targets accurately.

Object detection in Unmanned Aerial Vehicle (UAV) imagery remains challenging due to the prevalence of small targets, complex backgrounds, and the stringent requirement for real-time processing on computationally constrained platforms. Existing methods often struggle to balance detection accuracy, particularly for small objects, with operational efficiency. To address these challenges, this paper proposes YOLO-CAM, an enhanced object detector based on YOLOv5n. First, a novel Combined Attention Mechanism (CAM) is integrated to synergistically recalibrate features across both channel and spatial dimensions, enhancing the network’s focus on small targets while suppressing background clutter. Second, the detection head is strategically optimized by introducing a dedicated high-resolution head for tiny targets and removing a redundant head, thereby expanding the detectable size spectrum down to small pixels with reduced parameters. Finally, the CIoU loss is replaced with the inner-Focal-EIoU loss to improve bounding box regression accuracy, especially for low-quality examples and small objects. Extensive experiments on the challenging VisDrone2019 benchmark demonstrate the effectiveness of our method. YOLO-CAM achieves a mean Average Precision (mAP0.5) of 31.0%, which represents a significant 7.5% improvement over the baseline YOLOv5n, while maintaining a real-time inference speed of 128 frames per second. Comparative studies show that our approach achieves a superior balance between accuracy and efficiency compared to other state-of-the-art detectors. The results indicate that the proposed YOLO-CAM establishes a new way for accuracy–efficiency trade-offs in UAV-based detection. Due to its lightweight design and high performance, it is particularly suitable for deployment on resource-limited UAV platforms for applications requiring reliable real-time small object detection.