To address the challenges inherent in the transition flight control of QTR UAVs, this paper proposes an asynchronous tilt transition control framework that integrates NDIC with an ESO. First, a heterogeneous control allocation strategy is introduced to coordinate the rotors and aerodynamic surfaces, thereby maintaining consistent matching between control demands and actuator capabilities. Furthermore, compared with the synchronous tilt strategy, the proposed asynchronous tilt strategy improves pitch moment balance and forward acceleration capability, thereby enhancing robustness against CG variations and extending the achievable forward acceleration range. Finally, based on the asynchronous tilt transition strategy, a transition flight control method combining NDIC with ESO is presented to achieve precise transition control performance under the lumped disturbances. The simulation results demonstrate that the proposed tilt method achieves a safe and smooth transition, satisfies dynamic performance requirements, and exhibits strong robustness and high control accuracy.

The cooperative tracking of multiple ground targets by multiple UAVs remains challenging under partial observability, limited communication, and obstacle constraints, owing to complex target association, difficult task handover, strong coupling among low-level continuous control decisions, and unstable critic value estimation. To address these issues, this paper proposes a hierarchical-guidance and Gaussian-process-corrected multi-agent proximal policy optimization method, termed HGP-MAPPO. Built upon the centralized-training and decentralized-execution paradigm, HGP-MAPPO introduces low-frequency task-guidance signals derived from target-association information, task handover and recovery cues, task priorities, and desired observation geometry. These guidance signals are incorporated as conditional inputs into the low-level actor–critic framework, thereby reducing the policy learning difficulty in jointly handling target tracking, occlusion recovery, obstacle avoidance, and smooth control. Moreover, to alleviate local estimation bias in the neural-network critic under complex partially observable conditions, a Gaussian-process-based residual correction mechanism is designed. Specifically, the posterior mean is used to compensate for value residuals, while the posterior uncertainty adaptively regulates the correction intensity, improving the stability of value evaluation and policy optimization. A sparse inducing-point approximation is adopted to control the training-stage computational cost, while the Gaussian-process module is removed during decentralized execution and, therefore, introduces no additional online inference overhead. Experiments are conducted in standard-obstacle and densely obstructed multi-UAV multi-target tracking scenarios, with DDPG-MHSA, MAPPO, MADDPG, and MATD3 adopted as baselines. The experimental results demonstrate that HGP-MAPPO achieves faster training convergence, higher average episode rewards, and improved target retention rates. It also effectively reduces UAV–target distance fluctuations and the mean absolute temporal-difference (TD) error. Ablation studies further confirm the contributions of task-guidance signals, Gaussian-process residual correction, and uncertainty-aware weighting to cooperative tracking performance and training stability.

Autonomous flight of UAVs in unmapped environments remains a hot topic in research. While vision- and LiDAR-based autonomy has advanced rapidly for multirotor platforms, research on low-altitude autonomous flight for fixed-wing UAVs is still limited. To address obstacle avoidance in unmapped and complex 3D environments for fixed-wing UAVs, this paper proposes a neural network named FW-MonoAvoid-Net. This network is an end-to-end monocular-vision-based framework for fixed-wing UAVs to accomplish environment awareness and obstacle avoidance. Firstly, we develop a simulation framework to collect safe flight-trajectory data across diverse flight conditions, which is employed to train the proposed FW-MonoAvoid-Net. The proposed flight-learning framework adopts a lightweight visual perception network and a kinematics-aware state fusion module to predict the trajectory. At the output stage, an explicit physics-constrained decoder is embedded to strictly confine predicted trajectories within the feasible flight envelope of fixed-wing UAVs. Experiments are conducted in aerial (simulated spherical obstacles), canyon, urban (simulated high buildings), and New York Manhattan city scenarios. In the Manhattan city test, it achieves an 82.26% success rate at approximately 105 m flight altitude and 20 m/s cruise speed. Manhattan city is not included in the training datasets, FW-MonoAvoid-Net did not “see” similar scenarios in the training process. The testing results demonstrate its strong generalization and zero-shot deployment capability across unmapped environments within the simulation setting. FW-MonoAvoid-Net is a “smaller” model which has fewer than 3 million parameters. A Jetson Orin NX 16GB platform was employed to test the computation efficiency, and the trajectory-generation module reaches an average inference latency of 5.21 ms (about 190 Hz). These results show that, under limited computation and power consumption, the proposed method maintains both high success rates and excellent real-time performance. FW-MonoAvoid-Net is expected to help develop a fully autonomous fixed-wing flight in unmapped 3D spaces.

Cooperative maritime salvage using multiple unmanned helicopters involves coupled cable forces and changes in seabed suction, hydrodynamic drag, and buoyancy. This paper studies helicopter position tracking during seabed release, underwater lifting, partial emergence, and aerial transport. A simplified coupled dynamics model is combined with finite-time prescribed-performance control (FPPC) and an unknown system dynamics estimator (USDE). The performance boundary reaches a prescribed final value at a user-selected time, while the estimator reconstructs lumped cable-induced disturbances for compensation. The control structure includes position, velocity, attitude-angle, and angular-rate loops. Numerical comparisons with DSC-USDE are conducted for cuboid and cylindrical payloads. The archived controller-error records indicate smaller tracking errors and improved performance-boundary retention in the considered nominal scenarios. These results provide a numerical assessment of the proposed controller within the assumed simulation model; they do not constitute experimental validation of the complete physical salvage system.