Sensor Model-Based Trajectory Optimization for UAVs Using Nonlinear Model Predictive Control

Zwick, Markus; Gerdts, Matthias; Stütz, Peter

doi:10.2514/6.2022-1286

Cited by 4 publications

(14 citation statements)

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“…On the other hand, with the method nonlinear model predictive control (NMPC) a locally optimal reference trajectory is obtained for a given starting point and heading. Trajectory optimization with DP&OC and NMPC has already been described in our previous work [ 13 , 14 ]. Both approaches are described in condensed form in this paper to better understand their use and evaluation with the newly developed sensor performance model ( Section 2.2.1 ).…”

Section: Materials and Methodsmentioning

confidence: 99%

“…We presented our approach of generating optimized flight trajectories with nonlinear model predictive control to enhance detection performance in [ 13 ]. It is based on path planning ( Section 2.4.1 ) followed by the actual trajectory optimization with nonlinear model predictive control in Section 2.4.2 and Section 2.4.3 .…”

Section: Materials and Methodsmentioning

confidence: 99%

“…In addition to automated sensory data acquisition and object detection, we also address the optimization of UAV flight trajectories in the following. In this work, we reuse the two optimization methods nonlinear model predictive control (NMPC) and dynamic programming & optimal control (DP&OC), which were proposed in our previous works [ 13 , 14 ]. NMPC is a well-established method for UAV trajectory optimization and is used for example by [ 15 , 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

“…Our innovative contribution to the research problem is the development of a functional concept for sensor-model-based trajectory optimization in the field of unmanned aerial reconnaissance, which we presented for the first time in our previous works [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

“…The optimization specifically exploits the dependencies of the photographic states elevation angle and ground sample distance (as part of the environmental states) on the detection performance to generate a UAV reference flight trajectory along with a coordinated sensor footprint movement on the ground. In our previous works [ 13 , 14 ], we were able to enhance detection performance resulting from the optimized UAV reference trajectories compared to those of benchmark trajectories. Thus, the validity and capability of our concept could be proven.…”

Section: Introductionmentioning

confidence: 99%

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Sensor-Model-Based Trajectory Optimization for UAVs to Enhance Detection Performance: An Optimal Control Approach and Experimental Results

Zwick

Gerdts

Stütz

2023

Sensors

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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.

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Section: Materials and Methodsmentioning

confidence: 99%

Section: Materials and Methodsmentioning

confidence: 99%