基于流线的流场规划的距离和转向启发式算法
Distance and Steering Heuristics for Streamline-Based Flow Field Planning
摘要：
在流场影响下的车辆的运动规划可以受益于基于流线的规划的思想，其利用来自流体动力学的思想来实现计算效率。对于这些规划者来说，重要的是一种有效的方法来计算自由空间中两点之间的行程距离和方向，但这在强不可压缩流（如洋流）中很难实现。我们提出了两个有用的距离函数的分析形式，结合欧几里得距离与流场相关的流函数的值，并与两点之间的相对流的强度估计。此外，我们提出了转向算法，是有用的转向对采样点。我们评估这些想法，将它们与RRT* 和比较算法的性能与国家的最先进的方法在人工流场和实际的海洋预测数据在该地区的主导东澳大利亚电流之间的悉尼和布里斯班。结果表明，该方法的计算效率和能力，找到高质量的路径优于国家的最先进的方法，并显示出实际使用的承诺与自主海洋机器人。
英文摘要：
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.
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