WA-TD3 is introduced, a data-driven control framework that enables real-time wind disturbance perception and adaptive compensation without dedicated wind sensors, and consistently outperforms state-of-the-art methods on tracking accuracy under strong winds.
Robust quadrotor trajectory tracking under wind disturbances is challenging because real outdoor wind is multimodal with time-varying and heavy-tailed characteristics, whereas existing solutions suffer from insufficient disturbance observability and poor out-of-distribution robustness. To this end, this paper presents a robust quadrotor trajectory-tracking method based on residual-aware deep reinforcement learning for multimodal wind disturbances. The proposed method augments a standard tracking policy with a compact online acceleration-residual feature, which provides disturbance-related information without requiring an explicit wind sensor, a full disturbance observer, or a long-history recurrent estimator. To reduce excessive dependence on wind-specific temporal patterns, a residual-input regularization term is introduced during policy optimization. In addition, a tail-risk-aware reward is designed to balance nominal tracking accuracy, control smoothness, and suppression of large tracking deviations. The proposed method is evaluated under in-distribution wind, held-out out-of-distribution wind, and measured real-wind disturbances. The results show that the proposed method achieves the most balanced robustness under multimodal wind conditions compared with baselines.
Reliable and accurate trajectory prediction is critical for the safe and efficient operation of unmanned aerial vehicles (UAVs) in complex urban environments, where flight dynamics are subject to wind disturbances, dense obstacle fields, and strongly phase-dependent behavior. Conventional physics-based approaches are limited by parameter uncertainty, simplifying assumptions, and unmodeled disturbances, while data-driven models may lack physical plausibility and robustness across different flight phases. To address these limitations, this paper proposes a physics-guided hybrid residual-correction framework for UAV trajectory prediction. The approach combines a physics-based model with a data-driven sequence model and learns a residual correction that compensates for the deviation between analytical prediction and observed flight behavior. In addition, the model is trained with physics-guided feasibility regularization to promote realistic speed, acceleration, jerk, and landing descent behavior. Experimental evaluation on a real-world test set shows that the proposed method yields improved results relative to the stand-alone physics model, the LSTM model, and an MLP-based fusion model across all major metrics, including RMSE, MAE, ADE, and FDE. Phase-wise analysis further demonstrates strong improvements in cruise and landing, while highlighting takeoff as the most challenging phase. The results indicate that combining physical structure with learned residual correction provides a more accurate, physically consistent, and operationally interpretable approach for UAV trajectory forecasting.
Md Ashraful Islam, Stanley Förster, Tianxiong Zhang et al.· Scientific Reports· 0 citations
DRIQN is proposed to integrate Distributionally Robust Optimization (DRO) with implicit quantile networks to optimize worst-case performance under natural environmental conditions and incorporates heterogeneous noise sources and target robustness-critical scenarios.
Zhao-Fan Zhang, Minghao Yang, Si-Hong Xie et al.· 0 citations
Planning and control of fixed-wing Unmanned Aerial Vehicles (UAVs) are challenging due to nonlinear dynamics, aerodynamic limits, and environmental disturbances. Differential flatness offers a principled way to generate fast, feasible trajectories, but its use has largely been confined to model-free controllers, which lack predictive capabilities and demand tuning. In this paper, we propose a unified framework that integrates differential flatness-based trajectory generation with Nonlinear Model Predictive Control (NMPC), combining computationally efficient planning with predictive, constraint-aware control. To further improve robustness, we introduce a wind-aware sampling strategy embedded within the NMPC framework, enabling the generation of dynamically feasible reference trajectories that proactively account for wind disturbances while strictly enforcing aerodynamic and control input constraints. We validate the proposed framework through extensive simulations and real-world flight experiments, demonstrating improved tracking accuracy and robustness for complex trajectories, particularly when using the proposed wind-aware sampling strategy under strong wind conditions.
Nishanth Bobbili, P. Rao, Luca Morando et al.· 0 citations
Autonomous flight of aerial robots in narrow space remains challenging due to strong aerodynamic disturbances and limited flying space. Existing approaches mainly address aerodynamic disturbances at the control level, while motion planning typically relies on geometric constraints and fixed speed limits, leading to conservative or unsafe behaviors in confined environments. This paper presents a disturbance-aware planning and control framework (DAPCF) that integrates online disturbance estimation into the planning-control loop for quadrotor flight in narrow space. First, the dual-loop observers estimate 6-degree-of-freedom disturbance forces and torques in real time based on odometry and motor speed measurements. Then, a disturbance risk function is introduced that adaptively modulates the reference speed of the planner based on disturbance estimation, reducing velocity when disturbances exceed a threshold and restoring it under low-disturbance conditions. Finally, a motor-dynamics-based nonlinear model predictive controller (MDNMPC) with disturbance compensation is designed to ensure robust trajectory tracking under perturbed conditions. Experiments demonstrate that a quadrotor with a diagonal length of 0.39~m can traverse straight, sloped, and curved tunnels as narrow as 0.6~m, outperforming human pilots in both success rate and flight efficiency.
L. Qiang, Tianyu He, Chenyang Sun et al.· arXiv.org· 0 citations
UAV coverage in real environments is challenging because onboard energy limits and spatially varying wind jointly affect motion, safety, and propulsion costs. This paper proposes ETA-PPO, or Energy and Time Aware Prior Guided PPO, within a framework that separates wind aware flight execution from fleet level coverage coordination. At the execution layer, ETA-PPO augments PPO with a deterministic state conditional VAE action prior and a bounded residual policy for closed loop point to point flight under wind disturbed dynamics. At the coordination layer, Heuristic JointETA-PPO-H assigns target, return to base, and hold macro actions above the frozen low level executor. Experiments are conducted in a shared 3D urban simulator with CFD derived time varying wind fields. Across point-to-point navigation, ETA-PPO achieves a 100% success rate on all evaluated tracks while maintaining a practical balance between energy use and flight time against actor-critic baselines. The same executor also completes all four long-horizon single-UAV ROI tours, demonstrating reliable transfer from individual legs to chained coverage execution. In the scaled multi-target, multi-UAV case study, Heuristic JointETA-PPO-H reaches 100% coverage in every tested fleet and launch configuration. The fleet-level results are therefore treated as deterministic systems evidence.
T. Tran, Thi Ngoc Anh Mai, Changha Lee et al.· IEEE Access· 0 citations
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