Institute of Flight Systems, Universität der Bundeswehr München, 85579 Neubiberg, Germany.
Institute of Applied Mathematics and Scientific Computing, Universität der Bundeswehr München, 85579 Neubiberg, Germany.
Sensors (Basel). 2023 Jan 6;23(2):664. doi: 10.3390/s23020664.
UAVs are widely used for aerial reconnaissance with imaging sensors. For this, a high detection performance (accuracy of object detection) is desired in order to increase mission success. However, different environmental conditions (negatively) affect sensory data acquisition and automated object detection. For this reason, we present an innovative concept that maps the influence of selected environmental conditions on detection performance utilizing sensor performance models. These models are used in sensor-model-based trajectory optimization to generate optimized reference flight trajectories with aligned sensor control for a fixed-wing UAV in order to increase detection performance. These reference trajectories are calculated using nonlinear model predictive control as well as dynamic programming, both in combination with a newly developed sensor performance model, which is described in this work. To the best of our knowledge, this is the first sensor performance model to be used in unmanned aerial reconnaissance that maps the detection performance for a perception chain with a deep learning-based object detector with respect to selected environmental states. The reference trajectory determines the spatial and temporal positioning of the UAV and its imaging sensor with respect to the reconnaissance object on the ground. The trajectory optimization aims to influence sensor data acquisition by adjusting the sensor position, as part of the environmental states, in such a way that the subsequent automated object detection yields enhanced detection performance. Different constraints derived from perceptual, platform-specific, environmental, and mission-relevant requirements are incorporated into the optimization process. We evaluate the capabilities of the sensor performance model and our approach to sensor-model-based trajectory optimization by a series of simulated aerial reconnaissance tasks for ground vehicle detection. Compared to a variety of benchmark trajectories, our approach achieves an increase in detection performance of 4.48% on average for trajectory optimization with nonlinear model predictive control. With dynamic programming, we achieve even higher performance values that are equal to or close to the theoretical maximum detection performance values.
无人机广泛应用于搭载成像传感器的空中侦察。为此，希望其目标检测的检测性能（准确率）能够得到提高，从而提高任务成功率。然而，不同的环境条件（会对其产生负面影响）会影响传感器数据的采集和自动目标检测。有鉴于此，我们提出了一种创新的概念，即利用传感器性能模型来映射所选环境条件对检测性能的影响。这些模型用于基于传感器模型的轨迹优化，以便为固定翼无人机生成具有对齐传感器控制的优化参考飞行轨迹，从而提高检测性能。这些参考轨迹是使用非线性模型预测控制和动态规划计算得出的，两者都结合了一个新开发的传感器性能模型，该模型在本文中进行了描述。据我们所知，这是第一个在无人机侦察中使用的传感器性能模型，它映射了基于深度学习的目标检测器的感知链的检测性能与选定的环境状态。参考轨迹决定了无人机及其成像传感器相对于地面侦察目标的空间和时间定位。轨迹优化旨在通过调整传感器位置（作为环境状态的一部分）来影响传感器数据的采集，从而使后续的自动目标检测产生更高的检测性能。优化过程中纳入了源自感知、平台特定、环境和任务相关要求的各种约束。我们通过一系列地面车辆检测的模拟空中侦察任务来评估传感器性能模型和基于传感器模型的轨迹优化方法的能力。与各种基准轨迹相比，我们的方法在使用非线性模型预测控制进行轨迹优化时，平均检测性能提高了 4.48%。使用动态规划，我们甚至可以实现更高的性能值，这些值等于或接近理论最大检测性能值。
