论文标题
基于流线的流场计划的距离和转向启发式
Distance and Steering Heuristics for Streamline-Based Flow Field Planning
论文作者
论文摘要
在流场影响下的车辆运动计划可以受益于基于简化的计划的想法，从而利用流体动力学的想法来实现计算效率。对于此类计划者来说，重要的是计算自由空间中两个点之间的旅行距离和方向的有效手段，但是在强大的不可压缩流（例如洋流）中，这很难实现。我们提出了分析形式的两个有用的距离函数，它们将欧几里得距离与与流场相关的流函数的值结合在一起，并估算了两个点之间相对流的强度。此外，我们提出了转向启发式方法，这些启发式方法对于转向采样点很有用。我们通过将这些想法与RRT*集成在一起，并将算法的性能与人工流场中的最新方法以及在悉尼和布里斯班之间主要东澳大利亚电流区域的实际海洋预测数据中进行比较来评估这些想法。结果证明了该方法的计算效率和找到高质量路径的能力，其表现优于最先进的方法，并显示出与自主海洋机器人实际使用的前景。
Motion planning for vehicles under the influence of flow fields can benefit from the idea of streamline-based planning, which exploits ideas from fluid dynamics to achieve computational efficiency. Important to such planners is an efficient means of computing the travel distance and direction between two points in free space, but this is difficult to achieve in strong incompressible flows such as ocean currents. We propose two useful distance functions in analytical form that combine Euclidean distance with values of the stream function associated with a flow field, and with an estimation of the strength of the opposing flow between two points. Further, we propose steering heuristics that are useful for steering towards a sampled point. We evaluate these ideas by integrating them with RRT* and comparing the algorithm's performance with state-of-the-art methods in an artificial flow field and in actual ocean prediction data in the region of the dominant East Australian Current between Sydney and Brisbane. Results demonstrate the method's computational efficiency and ability to find high-quality paths outperforming state-of-the-art methods, and show promise for practical use with autonomous marine robots.
