让无人机在高速转弯的无人艇上精准降落，不依赖通信
CurviTrack: Curvilinear Trajectory Tracking for High-speed Chase of a USV
- 设计抗阻力模型结合模型预测控制，实现高速曲线上跟踪与着陆
- 预测误差降低40%，着陆成功率提升40%，极端转弯下仍稳定
- 适合高动态海洋场景中的无人艇-无人机协同任务
海洋环境中，异构机器人团队常因无人艇需停航等待无人机充电而产生时间和能量损耗。本文提出一种新型抗阻力模型与模型预测控制（MPC）相结合的方法，实现无人艇高速曲线上运动时的精准轨迹跟踪与无人机自主着陆，全程无需通信。相比现有方法，预测误差减少40%，预测置信度提升3倍，跟踪性能提高30%，在激进转向条件下着陆成功率提升40%。实验在两种不同尺寸的真实海船及仿真中验证，统计分析进一步证明该方法具有强鲁棒性。
原文摘要 · Abstract (English)
Heterogeneous robot teams used in marine environments incur time-and-energy penalties when the marine vehicle has to halt the mission to allow the autonomous aerial vehicle to land for recharging. In this paper, we present a solution for this problem using a novel drag-aware model formulation which is coupled with MPC, and therefore, enables tracking and landing during high-speed curvilinear trajectories of an USV without any communication. Compared to the state-of-the-art, our approach yields 40% decrease in prediction errors, and provides a 3-fold increase in certainty of predictions. Consequently, this leads to a 30% improvement in tracking performance and 40% higher success in landing on a moving USV even during aggressive turns that are unfeasible for conventional marine missions. We test our approach in two different real-world scenarios with marine vessels of two different sizes and further solidify our results through statistical analysis in simulation to demonstrate the robustness of our method.
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