In this paper, we propose a method that removes raindrops with light field image using image inpainting. We first use the depth map generated from light field image to detect raindrop regions which are then expressed as a binary mask. The original image with raindrops is improved by refocusing on the far regions and filtering by a high-pass filter. With the binary mask and the enhanced image, image inpainting is then utilized to eliminate raindrops from the original image. We compare pre-trained models of several deep learning based image inpainting methods. A light field raindrop dataset is released to verify our method. Image quality analysis is performed to evaluate the proposed image restoration method. The recovered images are further applied to object detection and visual localization tasks.

The purpose of this paper is to develop formation control strategies for heterogeneous multi-intelligent-agent systems in complex environments, with the goal of enhancing their performance, reliability, and stability. Complex flight conditions, such as navigating narrow gaps in urban high-rise buildings, pose considerable challenges for agent control. To address these challenges, this paper proposes a consensus-based formation strategy that integrates graph theory and multi-consensus algorithms. This approach incorporates time-varying group consistency to strengthen fault tolerance and reduce interference while ensuring obstacle avoidance and formation maintenance in dynamic environments. Through a Lyapunov stability analysis, combined with minimum dwell time constraints and the LaSalle invariance principle, this work proves the convergence of the proposed control scheme under changing network topologies. Simulation results confirm that the proposed strategy significantly improves system performance, mission execution capability, autonomy, synergy, and robustness, thereby enabling agents to successfully maintain formation and avoid obstacles in both homogeneous and heterogeneous clusters in complex environments.

Nowadays, the practical tasks of UAVs are becoming more and more complicated and diversified. In the practical flight process, the large-scale changes of the flight environment, the modeling errors, and the external disturbances may induce the instability of the UAV flight system. Meanwhile, the constraints of the UAV attitudes also have to be guaranteed during the flight process. However, most existing control methods still have limitations in handling the constraints and the multisource disturbances simultaneously. To address this problem, in this paper, we focus on the actual output tracking control for the UAV systems with full-state constraints and multisource disturbances. Firstly, a high-order tan-type barrier Lyapunov function (HOBLF) has been constructed for the UAV to maintain the full-state constraints. Secondly, by combining the adaptive backstepping technique and the fuzzy logic systems, the modeling errors and the unknown nonlinearities of the UAV attitude control system can be handled. Moreover, by properly constructing several adaptive laws, the time-varying disturbances existing in the UAV attitude control system can be suppressed. Finally, the full-state-constrained antidisturbance controller is formed, ensuring that the tracking error approaches arbitrarily to small neighborhood and does not violate the given constraints. The simulation results illustrate the feasibility and the advantages of the proposed method.

With the rapid development of the low-altitude economy, the deployment of unmanned aircraft vehicles (UAVs) in many fields is increasing continuously, and the demand for collaborative flights is also growing. However, the issue of flight safety in complex airspace remains a pressing concern. Precise flight path prediction, collision detection, and avoidance are paramount for secure collaborative operations. This study proposes an integrated framework that combines an EKF-LSTM model for trajectory prediction, a Trajectory Dispersion Cone (TDC) method for probabilistic collision risk assessment, and a Velocity Obstacle-Model Predictive Control (VO-MPC) strategy for dynamic collision avoidance. Experimental results demonstrate the advantages of our approach: the EKF-LSTM model reduces prediction errors in complex flight states. Furthermore, the VO-MPC method achieves a 99.8% collision avoidance success rate under low-noise conditions—an 8.6% improvement over traditional MPC—while reducing the average collision probability by 66.7%. It also maintains stable performance under medium- and high-noise conditions, reducing the collision probability to only 27.7% and 34.2% of that of conventional MPC, respectively. The proposed framework offers a solution for safe manned–unmanned collaboration in complex environments. Future work will extend these methods to multi-aircraft cooperative scenarios.