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 Dynamical 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 traditional 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.

翻译：无人机（UAV）光谱遥感技术被广泛应用于水质监测。然而，在动态环境中，变化的照明条件（如阴影和镜面反射（太阳耀斑））可能导致严重的光谱失真，从而降低数据可用性。为了在确保飞行安全的同时最大化获取高质量数据，本文提出了一种用于动态光影干扰规避的主动路径规划方法。首先，构建了一个动态预测模型，将时变的光影干扰区域转化为三维虚拟障碍物。其次，引入了一种改进的受干扰流体动力学系统（IFDS）算法，该算法通过构建排斥力场生成平滑的初始避障路径。随后，采用模型预测控制（MPC）框架进行滚动时域路径优化，以处理飞行动力学约束并实现实时轨迹跟踪。此外，设计了一种动态飞行高度调整（DFAA）机制，在可观测区域狭窄时主动降低飞行高度，从而提高空间分辨率。仿真结果表明，与传统的PID和单一避障算法相比，所提方法在密集干扰场景下的避障成功率达到了98%，显著提高了路径平滑度，并将有效观测数据量提升了约27%。本研究为复杂光照环境下实现精确的无人机水质监测提供了一种有效的工程解决方案。