Safe Path Planning and Observation Quality Enhancement Strategy for Unmanned Aerial Vehicles in Water Quality Monitoring Tasks

Safe Path Planning and Observation Quality Enhancement Strategy for Unmanned Aerial Vehicles in Water Quality Monitoring Tasks Yuanshuang Fua, Qianyao Wangb, Qihao Wangb, Bonan Zhanga, Jiaxin Zhaob, Yiming Caob, Zhijun Lib,∗ aUniversity of Electronic Science and Technology of China bNorth China University of Technology Abstract Unmanned Aerial Vehicle (UAV) spectral remote sensing technology is widely used in water quality monitoring. However, in dynamic environments, varying illumination conditions, such as shadows and specular reflection (sun glint), can cause severe spectral distortion, thereby reducing data availability. To maximize the acquisition of high- quality data while ensuring flight safety, this paper proposes an active path planning method for dynamic light and shadow disturbance avoidance. First, a dynamic prediction model is constructed to transform the time-varying light and shadow disturbance areas into three-dimensional virtual obstacles. Second, an improved Interfered Fluid Dynam- ical System (IFDS) algorithm is introduced, which generates a smooth initial obstacle avoidance path by building a repulsive force field. Subsequently, a Model Predictive Control (MPC) framework is employed for rolling-horizon path optimization to handle flight dynamics constraints and achieve real-time trajectory tracking. Furthermore, a Dynamic Flight Altitude Adjustment (DFAA) mechanism is designed to actively reduce the flight altitude when the observable area is narrow, thereby enhancing spatial resolution. Simulation results show that, compared with tradi- tional PID and single obstacle avoidance algorithms, the proposed method achieves an obstacle avoidance success rate of 98% in densely disturbed scenarios, significantly improves path smoothness, and increases the volume of effective observation data by approximately 27%. This research provides an effective engineering solution for precise UAV water quality monitoring in complex illumination environments. Keywords: UAV; Water Quality Monitoring; Path Planning; IFDS; MPC 1. Introduction Water resources are a fundamental element for maintaining ecological system stability and human societal sus- tainable development. Nevertheless, with the improper discharge of industrial wastewater, non-point agricultural pollution, and domestic sewage, global water pollution is worsening, and the risk of water quality deterioration is continuously increasing Wang et al. (2024); Jones et al. (2022); Mekonnen and Hoekstra (2016); Koutroulis et al. (2019). Efficient monitoring of key water quality parameters, such as chlorophyll concentration, turbidity, and typical pollutant content, is a prerequisite for pollution tracing, risk early warning, and ecological restoration, as well as cru- cial support for scientific water resource management Fendereski et al. (2024); Hou et al. (2022); Zhao et al. (2024). In recent years, with the continuous advancement of sensor technology and UAV platforms, UAV-based water qual- ity monitoring methods equipped with hyperspectral or multispectral sensors have become a research hotspot Wang et al. (2025b); Wu et al. (2025); Sagan et al. (2020). Compared with traditional manual sampling or fixed monitoring stations, this method offers advantages such as high spatial resolution, wide coverage, and strong maneuverability, capable of obtaining high-precision monitoring information for large-scale water bodies in a short time Chen et al. ∗Corresponding author Email addresses: 202421230104@std.uestc.edu.cn (Yuanshuang Fu), marlowe@mail.ncut.edu.cn (Qianyao Wang), wangqihao.ncut@gmail.com (Qihao Wang), 202421230105@std.uestc.edu.cn (Bonan Zhang), 23101150106@mail.ncut.edu.cn (Jiaxin Zhao), 23101150119@mail.ncut.edu.cn (Yiming Cao), marlowe@mail.ncut.edu.cn (Zhijun Li) arXiv:2512.21375v1 [cs.RO] 24 Dec 2025 (2025); Zainurin et al. (2022); Trinh et al. (2024). Furthermore, UAV spectral monitoring features non-contact sam- pling, avoiding secondary disturbance to the water body, and enables the simultaneous inversion of multiple water quality indicators, including chlorophyll, suspended matter, and chemical oxygen demand, based on multi-band spec- tral reflectance characteristics Fu et al. (2024a); Peng et al. (2025); Zhang et al. (2025). Consequently, UAV spectral monitoring has shown unique advantages in tracking pollution processes, assessing eutrophication in lakes and reser- voirs, and verifying the effectiveness of watershed ecological restoration, establishing itself as a vital component of the water quality monitoring technology system Chen et al. (2021a); Fu et al. (2021, 2023). Due to the rapid development of UAV spectral monitoring in recent years, an increasing number of studies have focused on using remote sensing imagery combined with traditional algorithms and machine learning methods to perform high-precision quantitative inversion of key water quality parameters. For instance, Sun et al. (2024b) mainly focused on using remote sensi

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