让无人船像老水手一样借流航行，省电30%~50%
Khalasi: Energy-Efficient Navigation for Surface Vehicles in Vortical Flow Fields
- 用强化学习学如何感知局部水流，自主规划节能路径
- 相比现有方法，航行能耗降低30%至50%
- 适合需要长时间续航的海洋自主航行任务
数百年来，卡拉萨人（khalasi，古吉拉特语意为水手）巧妙利用洋流在广阔海域中航行，几乎不费力气。在自主系统中复现这种直觉仍是重大挑战，尤其对需在严格能源预算下执行长期任务的自主水面车辆而言。本文提出一种基于强化学习的能量高效水面车辆导航方法，适用于涡旋流场环境，克服了传统路径规划在部分可观测条件下的局限性。我们构建了一个端到端的强化学习框架，基于软演员-评论家算法（Soft Actor Critic），仅使用局部速度测量值学习具备流场感知能力的导航策略。在多样且动态丰富的场景中进行广泛评估后，该方法展现出显著的节能效果，并能鲁棒地泛化至此前未见的流场条件。所生成的航行路径相较现有最优技术，能量节约达30%至50%，为海洋环境中实现长期自主提供了可行路径。
原文摘要 · Abstract (English)
For centuries, khalasi (Gujarati for sailor) have skillfully harnessed ocean currents to navigate vast waters with minimal effort. Emulating this intuition in autonomous systems remains a significant challenge, particularly for Autonomous Surface Vehicles tasked with long duration missions under strict energy budgets. In this work, we present a learning-based approach for energy-efficient surface vehicle navigation in vortical flow fields, where partial observability often undermines traditional path-planning methods. We present an end to end reinforcement learning framework based on Soft Actor Critic that learns flow-aware navigation policies using only local velocity measurements. Through extensive evaluation across diverse and dynamically rich scenarios, our method demonstrates substantial energy savings and robust generalization to previously unseen flow conditions, offering a promising path toward long term autonomy in ocean environments. The navigation paths generated by our proposed approach show an improvement in energy conservation 30 to 50 percent compared to the existing state of the art techniques.
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