視野制約付き自律飛行のための大域的ヨーパラメータ化による軌道最適化
Trajectory Optimization with Global Yaw Parameterization for Field-of-View Constrained Autonomous Flight
視野制約のあるドローンの軌道生成において、360度のヨー変化を可能にする大域的パラメータ化を提案し、特異点を回避しつつ制御努力を低減して最適化の実現可能性を向上させた。
著者: Yuwei Wu, Yuezhan Tao, Igor Spasojevic, Vijay Kumar
分類: cs.RO
原文アブストラクト
Trajectory generation for quadrotors with limited field-of-view sensors has numerous applications such as aerial exploration, coverage, inspection, videography, and target tracking. Most previous works simplify the task of optimizing yaw trajectories by either aligning the heading of the robot with its velocity, or potentially restricting the feasible space of candidate trajectories by using a limited yaw domain to circumvent angular singularities. In this paper, we propose a novel \textit{global} yaw parameterization method for trajectory optimization that allows a 360-degree yaw variation as demanded by the underlying algorithm. This approach effectively bypasses inherent singularities by including supplementary quadratic constraints and transforming the final decision variables into the desired state representation. This method significantly reduces the needed control effort, and improves optimization feasibility. Furthermore, we apply the method to several examples of different applications that require jointly optimizing over both the yaw and position trajectories. Ultimately, we present a comprehensive numerical analysis and evaluation of our proposed method in both simulation and real-world experiments.
