Unmanned aerial vehicles (UAVs) are increasingly used for active sensing and information gathering in spatially distributed environments. Their performance, however, is constrained by limited flight time, sensing uncertainty, and the trade-off between spatial coverage and observation accuracy. This paper presents a real-world validation of a multi-UAV active sensing framework for probabilistic binary terrain mapping, with precision agriculture used as the application case. The environment is represented as a probabilistic belief map, where spatial dependencies are modeled through a factor-graph formulation. UAV decision making is guided by Information Gain based Informative Path Planning (IGbIPP), and the approach is compared with Random Walk and Sweep coverage path planning baselines using both synthetic terrains and real UAV-derived agricultural imagery. The study also evaluates spatial correlation weights and several probabilistic belief-fusion rules for multi-UAV information sharing. Results show that IGbIPP reduces entropy and mapping error more effectively than the baselines, while a wider field of view improves real-world coverage and map accuracy. The results further show that simple equal or biased spatial weights can be more robust than adaptive weights, and that Bayesian, log-odds, and Dempster--Shafer fusion achieve the best cooperative mapping performance. These findings highlight the importance of uncertainty-driven planning, sensing geometry, spatial modeling, and probabilistic fusion for real-world UAV-based active sensing.

翻译：无人机越来越多地被用于空间分布式环境中的主动感知与信息收集。然而，其性能受到有限飞行时间、感知不确定性以及空间覆盖与观测精度之间权衡的限制。本文针对概率二值地形测绘任务，提出了一种多无人机主动感知框架在实际场景中的验证，并以精准农业作为应用案例。环境被表示为概率信念地图，空间依赖性通过因子图模型进行建模。无人机的决策由基于信息增益的信息路径规划（IGbIPP）引导，并与随机游走和扫描覆盖路径规划基线方法在合成地形及真实无人机农业影像上进行了对比。研究还评估了空间相关权重及多种概率信念融合规则在多无人机信息共享中的效果。结果表明，IGbIPP在降低熵和减少测绘误差方面优于基线方法，同时更宽的视场有助于提升实际覆盖范围与地图精度。进一步结果显示，简单均衡或偏置的空间权重比自适应权重更为稳健，贝叶斯融合、对数几率融合与德姆斯特-谢弗融合实现了最优的协同测绘性能。这些发现凸显了不确定性驱动规划、感知几何、空间建模与概率融合在实际无人机主动感知中的重要性。