Enhancing Detection Performance through Sensor Model-based Trajectory Optimization for UAVs

We propose an approach to enhance the overall detection performance in unmanned aerial reconnaissance with imaging sensors. This is achieved by generating near global optimal UAV trajectories utilizing previously developed sensor performance models. Here, we are taking into account UAV, sensor and environment characteristics as well as mission objectives. The approach can be briefly summarized as follows: First, we apply coverage path planning to determine the sensor footprint path within a predefined recon area. Second, the sensor performance model is utilized to calculate discretized two-dimensional perception maps for each footprint, which is a state-dependent representation of the detection performance. For each perception map, we identify suitable sensor positions of high detection performance. From here, we systematically connect these positions of consecutive perception maps to create a transition graph. With use of optimal control, we assign costs (e.g. recon duration) to these connections while taking several requirements and constraints into account. Dynamic programming is utilized to determine the optimal path in the graph with the lowest cost. Finally, we smooth the path by cubic spline motion primitives, which leads to the reference flight trajectory for the UAV. We evaluate our approach through a series of simulated vehicle detection reconnaissance tasks with a fixed wing UAV. Our approach improves the detection performance by 3% compared to a trajectory generated with a local optimal greedy optimization and the reconnaissance time is reduced by 20%. In comparison with a primitive trajectory, the improvement in detection performance is even up to 10% with an associated increase in reconnaissance time of at least 15%.